25 items tagged "data management"

  • 4 Tips to help maximize the value of your data

    4 Tips to help maximize the value of your data

    Summer’s lease hath all too short a date.

    It always seems to pass by in the blink of an eye, and this year was no exception. Though I am excited for cooler temperatures and the prismatic colors of New England in the fall, I am sorry to see summer come to an end. The end of summer also means that kids are back in school, reunited with their friends and equipped with a bevy of new supplies for the new year. Our kids have the tools and supplies they need for success, why shouldn’t your business?

    This month’s Insights Beat focuses on additions to our team’s ever-growing body of research on new and emerging data and analytics technologies that help companies maximize the value of their data.

    Get real about real time

    Noel Yuhanna and Mike Gualtieri published a Now Tech article on translytical data platforms. Since we first introduced the term a few years ago, translytical data platforms have been a scorching hot topic in database technology. Enabling real-time insights is imperative in the age of the customer, and there are a number of vendors who can help you streamline your data management. Check out their new report for an overview of 18 key firms operating in this space, and look for a soon-to-be-published Forrester Wave™ evaluation in this space, as well.

    Don’t turn a blind eye to computer vision

    Interested in uncovering data insights from visual assets? Look no further than computer vision. While this technology has existed in one form or another for many years, development in convolutional neural networks reinvigorated computer vision R&D (and indeed established computer vision as the pseudo-progenitor of many exciting new AI technologies). Don’t turn a blind eye to computer vision just because you think it doesn’t apply to your business. Computer vision already has a proven track record for a wide variety of use cases. Kjell Carlsson published a New Tech report to help companies parse a diverse landscape of vendors and realize their (computer) vision.

    Humanize B2B with AI

    AI now touches on virtually all aspects of business. As techniques grow more and more sophisticated, so too do its use cases. Allison Snow explains how B2B insights pros can leverage emerging AI technologies to drive empathy, engagement, and emotion. Check out the full trilogy of reports and overview now. 

    Drive data literacy with data leadership

    Of course, disruptive changes to data strategy can be a hard sell, especially when your organization lacks the structural forces to advocate for new ideas. Jennifer Belissent, in a recent blog, makes the case for why data leadership is crucial to driving better data literacy. Stay tuned for her full report on data literacy coming soon. More than just

    leadership, data and analytics initiatives require investment, commitment, and an acceptance of disruption. No initiative will be perfect from the get-go, and it’s important to remember that analytics initiatives don’t usually come with a magician’s reveal.

    Author: Srividya Sridharan

    Source: Forrester

  • Data management: compliance, protection, and the role of IT

    Data management: compliance, protection, and the role of IT

    The business benefit of data and data-driven decisions cannot be undervalued, which is a widely agreed-upon mindset on today’s business landscape. At the same time, there are sensitivities around where that data comes from and how it’s being accessed or used. For this reason, data protection and privacy are the driving topics in today’s age and, for enterprise companies, essential to remaining an ongoing business concern.

    To ensure regulatory compliance and generate business value, any data coming into an organization needs to be confidentially handled, trusted and protected. Modern businesses also want their products to be cloud deployable, but many businesses have security concerns that come with sharing information in the cloud. It’s crucial that when you use data, you also protect it, preserve the integrity of original personal ownership and, maintain the privacy of the person to whom it belongs at all costs.

    The first level of data protection is to not collect personal data if there is no legitimate purpose in doing so. If personal data was collected and a legitimate purpose no longer exists, it must be deleted.

    The second level of data protection can be realized through a framework of technology measures: Identity and access management, patch management, separation of business purpose (disaggregation of legal entities), and encryption.

    IT teams often provide data in an encrypted format as a means to get people the information they need, without compromising sensitive information. People receiving the data don’t usually need to know every bit of data, they just want an aggregate of what the data looks like. And IT teams want to ensure that when they transfer important data assets, the information is secure.

    Additionally, when it comes to being data compliant, there are rules and regulations that businesses must follow, such as the General Data Protection Regulation (GDPR) and data protection and privacy agreements.

    GDPR harmonizes data protection regulation throughout the European Union and gives individuals more control over their data. It imposes expansive rules about processing data backed by powerful enforcement, so IT teams must ensure they are compliant. This creates an extra, guaranteed level of security for corporate and personal data, though it’s not without its complications for enterprises.

    Concretely, this means that companies have to technically ensure that only necessary sets move through ‘boundaryless’ end-to-end business scenarios. Here, we consider efficient data control in and through the context of comprehensive business processing for a declared purpose that is legally secured, including by consent of the individual that the data is related to.

    The business context and its technical rendering through customizing and configuration is central to the business capability of efficiently controlling data for purposes of data protection and privacy. Integrated services provide business context by showing information contained in any one data set that is linked to ordered business objects and business object types related to the data subject.

    Here, we have offered an embedded view of the data subject, which can be uniformly changed and managed in the context of a logical sequence of business events.

    Data management capabilities

    To further protect data and stay compliant, many IT teams have started with the approach of applying data management capabilities to encrypt and anonymize data without actually changing the data set. IT simply changes the way data is presented to ensure data is safe.

    One recent example is the adoption of the GDPR rules to be compliant with the legal regulations. In this case the data management capabilities must ensure that only the allowed data is shown and that protected personal data is hidden or deleted (information lifecycle management) without destroying required information and connections.

    By transitioning to what we call an 'intelligent organization', businesses can feed applications and processes with the data essential for the digital economy and intelligently connect people, data and processes safely and secure.

    Solutions offer customers comprehensive in-depth information about the places where their master data exists, which parts reside in which services, applications or systems, and how the data can be accessed, or they can even get direct access. Moreover, a clear picture of the complete master data set and all individual owners can be obtained, including rules for creating data consistency. This provides overall consistency, and the robustness that is required in a service-driven enterprise environment.

    Tiered levels of access

    Another tactic way of keeping data secure is for IT to work closely with each line of business to set tiered levels of access by creating a workflow scenario for first, second, third, and so on, access by individual persons to data within a specific line of business.

    In contrast to the more traditional model outlined above, IT teams can offer a tiered approach to authorization. Users have limited access based on transaction codes, organizational levels, etc., by assigning authorization roles through different lines of business.

    Best practices for data compliance and protection

    Both approaches outlined above allow businesses to review their data to determine the real value of it without compromising the security of the data.

    Overall, it’s important that data compliance is not only a tech topic, but a topic that should be discussed, rolled out, and followed company-wide. As 2019 comes to a close, companies must have a data compliance program in place, a data protection culture within their organizations and the ability for employees to understand the importance of change processes and tools to adhere to the new regulations.

    Including such aspects from the beginning can be a competitive advantage for companies and should be considered at an early stage. Not adhering to data protection and privacy rules and regulation can cause tremendous damage to a company’s image and reputation and can have a heavy financial impact.

    Author: Katrin Lehmann

    Source: Information-management

  • Data redundancy: avoid the negative and use the positive to your advantage

    Data redundancy: avoid the negative and use the positive to your advantage

    Data redundancy means keeping data in two or more locations within a database or storage infrastructure. Data redundancy can occur either intentionally or accidentally within an organization. In case of data corruption or loss, the organization can continue operations or services if conscious redundancy is provided. On the other hand, unconscious redundancy causes duplicate data to waste database space and information inconsistencies throughout the organization.

    Types of data redundancy

    There are two types of data redundancy. Positive data redundancy is provided intentionally within the organization. It ensures that the same data kept and protected in different places are used for redundancy and business sustainability in case of a possible disaster.

    Wasteful data redundancy, which occurs with unintentional data duplication and is an indicator of failed database management, may cause information inconsistencies throughout an organization. When data is stored in numerous places, it takes up valuable storage space and makes it difficult for an organization to figure out which data should be accessed or updated.

    What is the difference between data redundancy, data duplicity, and backup?

    The main difference between redundancy and duplicity, which is often confused, lies in the reason for adding a new copy of the data. From a database point of view, data duplicity refers to data added back to the system by users. In contrast, redundancy requires synchronization between databases to ensure positive redundancy without any problems. While data duplicity inevitably causes inconsistency in databases, database synchronizations and data normalization prevent this issue in data redundancy.

    The distinction between data backup and redundancy may be subtle, but it is crucial. Backing up data creates compressed and encrypted versions of data stored locally or in the cloud. In contrast, data redundancy adds an extra layer of protection to the backup. Local backups are necessary for business continuity; however, it’s also essential to have another protective layer for data. You can reduce the risks by including data redundancy in your disaster recovery plan.

    What is the relationship between data redundancy and data inconsistency?

    Simply put, data redundancy leads to Data Inconsistency. The data inconsistency condition occurs when the same data exists in different formats in multiple tables. It means that other files contain different information about a particular object, situation, event, or person. This inconsistency can cause unreliable and meaningless information.

    Benefits of positive data redundancy

    Data must be stored in two or more locations to be considered redundant. Suppose the initial data is damaged or the hard drive on which it is stored fails. In that case, the backup data can help save the organization money.

    The redundant data may be either a complete copy of the original information or particular elements of it. Keeping only certain pieces of data allows organizations to reassemble lost or destroyed data without pushing their resource limitations. Backups and RAID systems are used to protect data in case of failure. Backups, for example, can be stored on multiple hard drives so that if one fails, the array can activate with minimal downtime.

    There are distinct advantages to data redundancy, which depend on its implementation. The following are some of the potential benefits:

      • Data redundancy helps to guarantee data security. Organizations can use redundant data to replace or recompile missing information when data is unavailable. 
      • Multiple data servers enable data management systems to examine any variances, assuring data consistency. 
      • Data may be easier to access in some areas than others for an organization that covers several physical locations. Accessing information from various sources might allow individuals in a company to access the same data more quickly.
      • Data redundancy is a must in business continuity management. Backup technology ensures data security, while disaster recovery services minimize downtime by prioritizing mission-critical information. Data redundancy serves as an add-on to both of these processes for increased recoverability.

    How to avoid wasteful data redundancy?

    As wasteful data redundancy grows, it takes up a significant server storage space over time. The fewer storage slots there are, the longer it will take to retrieve data, eventually harming business results. On the other hand, inconsistent data is likely to corrupt reports or analytics that can cost organizations direly.

    Data redundancy is popular among organizations as data security or backup method. It appears to be an excellent solution when you have all the resources needed to store and manage your data. But if you don’t have enough resources, the positive redundancy can turn wasteful quickly. Here are some valuable tips to avoid wasteful redundancy:

      • Master Data provides more consistency and accuracy in data. It’s the sum of all your vital business information stored in various systems throughout your company. The use of master data does not eliminate data redundancy; instead, it helps organizations work around a certain degree of redundancy. The main advantage of master data is that it allows companies to work on a single changed data element instead of the overall data.
      • Another source of data redundancy is keeping information that isn’t relevant any longer. Suppose you migrate your data to a new database but forget to delete it from the old one. In that case, you’ll have the same information in two locations, wasting space. Make sure databases that aren’t required anymore are deleted.
      • Data normalization is a technique that involves organizing data in a database to minimize duplication. This approach ensures that the data from all records are comparable and may be interpreted similarly. Standardizing data fields, including customer names, contact information, and addresses is easy with data normalization. Therefore, it will allow you to quickly delete, update, or add any information.

    Author: Hasan Selman

    Source: Dataconomy

  • Data warehousing: ETL, ELT, and the use of big data

    Data warehousing: ETL, ELT, and the use of big data

    If your company keeps up with the trends in data management, you likely have encountered the concepts and definitions of data warehouse and big data. When your data professionals try to implement data extraction for your business, they need a data repository. For this purpose, they can use a data warehouse and a data lake.

    Roughly speaking, a data lake is mainly used to gather and preserve unstructured data, while a data warehouse is intended for structured and semi-structured data.

    Data warehouse modeling concepts

    All data in a data warehouse is well-organized, archived, and arranged in a particular way. Not all data that can be gathered from multiple sources reach a data warehouse. The source of data is crucial since it impacts the quality of data-driven insights and hence, business decisions.

    During the phase of data warehouse development, a lot of time and effort is needed to analyze data sources and select useful ones. It depends on the business processes, whether a data source has value or not. Data only gets into the warehouse when its value is confirmed.

    On top of that, the way data is represented in your database has a critical role. Concepts of data modeling in a data warehouse are a powerful expression of business requirements specific to a company. A data model determines how data scientists and software engineers will design, create, and implement a database.

    There are three basic types of modeling. Conceptual data model describes all entities a business needs information about. It provides facts about real-world things, customers, and other business-related objects and relations.
    The goal of creating this data model is to synthesize and store all the data needed to gain an understanding of the whole business. This model is designed for the business audience.

    Logical data model suits more in-depth data. It describes the structure of data elements, their attributes, and ways these elements interrelate. For instance, this model can be used to identify relationships between customers and products of interest for them. This model is characterized by a high level of clarity and accuracy.

    Physical data model describes specific data and relationships needed for a particular case as well as the way data model is used in database implementation. It provides a wealth of meta-data and facilitates visualizing the structure of a database. Meta-data can involve accesses, limitations, indexes, and other features.

    ELT and ETL data warehouse concepts

    Large amounts of data sorted for warehousing and analytics require a special approach. Businesses need to gather and process data to retrieve meaningful insights. Thus, data should be manageable, clean, and suitable for molding and transformation.

    ETL (extract, transform, load)and ELT (extract, load, transform) are the two approaches that have technological differences but serve the same purpose – to manage and analyze data.

    ETL is the paradigm that enables data extraction from multiple sources and pulling data into a single database to serve a business.

    At the first stage of the ETL process, engineers extract data from different databases and gather it in a single place. The collected data undergo transformation to take the form required for a target repository. Then the data come to a data warehouse or a target database.

    If to switch the letters 'T' and 'L', you get the ELT process. After the retrieval, the data can be loaded straight to the target database. The cloud technology enables large and scalable storage places, and massive datasets can be first loaded and then transformed as per the business requirements and needs.

    The ELT paradigm is a newer alternative to a well-established ETL process. It is flexible and allows fast processing speed to work with raw data. On the one hand, ELT requires special tools and frameworks, but on the other, it enables unlimited access to business data, thus saving BI and data analytics experts so much time.

    ETL testing concepts are also essential to ensure that data is loading in a data warehouse in a correct and accurate manner. This testing involves data verification at transitional phases. And before data reaches the destination, its quality and usefulness are already verified.

    Types of data warehouse for your company

    Different data warehouse concepts presuppose the use of particular techniques and tools to work with data. Basic data warehouse concepts also differ depending on a company’s size and purposes of using data.

    Enterprise data warehouse enables a unique approach to organizing, visualizing, and representing all the data across a company. Data can be classified by a subject and can be accessed based on this attribute.

    Data mart is a subcategory of a data warehouse designed for specific tasks in business areas such as retail, finance, and so forth. Data comes into a data mart straight from the sources.

    Operational data store satisfies the reporting needs within a company. It is updating in real time, which makes this solution best-suited for keeping in all business records.

    Big data and data warehouse ambiguity

    A data warehouse is an architecture that has proved to be valuable for data storing over the years. It involves data that has a defined value and can be used from the start to solve some business needs. Everyone can access this data, and the features of datasets are reliability and accuracy.

    Big data is a hyped field these days. It is the technology that allows retrieving data from heterogeneous sources. The key features of big data are volume, velocity or data streams, and a variety of data formats. Unlike a data warehouse, big data is a repository that can hold unstructured data as well.

    Companies seek to adopt custom big data solutions to unlock useful information that can help improve decision-making. These solutions help drive revenue, increase profitability, and cut customer churn thanks to the comprehensive information collected and available in one place.

    Data warehouse implementation entails advantages in terms of making informed decisions. It provides comprehensive insights into what is going on within a company, while big data can be in the shape of massive but disorganized datasets. However, big data can be later used for data warehousing.

    Running a data-driven business means dealing with billions of data on in-house, external operations, consumers, and regulations.

    Author: Katrine Spirina

    Source: In Data Labs

  • Enabling Data Stewardship to Improve Data Quality and Management at your Organization

    Enabling Data Stewardship to Improve Data Quality and Management at your Organization

    As we continue to do business in a digitally connected world, more data-driven organizations are prioritizing data stewardship to improve data quality and management. Data stewards maintain and protect data assets that need special care, not just for cybersecurity but for better business insights and more informed decision-making.

    Understand Data Stewardship Roles and Responsibilities 

    In his presentation at the Data Governance & Information Quality Conference, Jimm Johnson, the Data Governance manager at HireRight, discussed key data stewardship best practices he’s turned to during his 25-plus years of experience in multiple industries and areas of IT, including Data Governance “long before Data Governance became an actual thing.”

    At its core, data stewardship involves “taking care of data on behalf of someone” and being held formally accountable for it, said Johnson. In his organization, he prefers straightforward titles for different types of stewards: “Analytics stewards” focus on business intelligence reports and dashboards, “application stewards” work within IT systems, and “data stewards” take a broader enterprise-level approach to data management. Each plays a key role in an organization’s Data Governance program.

    Regardless of which titles you choose, be sure to define in detail what your data stewards do:

    “You can assign any titles you want to your stewards,” said Johnson. “If you want to come up with a theme – a Star Wars theme, or Disney, or whatever – that’s fine, that might engender interest, but just be very, very clear about their responsibilities and the processes you want them to follow.”

    Exploring data stewardship as a new type of business function, Johnson highlighted four labels you can use to help everyone in the organization understand steward roles and responsibilities:

    • Knowledge keepers: Data stewards serve as subject matter experts, maintaining and sharing insider “tribal” knowledge of institutional data processes. They help to represent teams and business units in collaborative workflows and may also coach or train others.
    • Friendly gatekeepers: Data stewards should know a lot about the rules and standards governing data maintenance. They may research how to match departmental needs to enterprise standards or how to classify and protect different data assets.
    • Quality inspectors: Data stewards should apply these rules and match them to decisions that will keep the company compliant and up to standard. That may involve flagging and remediating problems with data or measuring and improving data quality.
    • Change agents: This is where data stewards will contribute to the process of change that benefits a company or enterprise. When there is a need for new initiatives and evaluations, data stewardship pros can assist others, embrace data literacy, and cultivate the buy-in that’s needed to advance projects to an active stage.

    Identify Important Traits and Skills of Data Stewards 

    Business leaders must understand what makes data stewards successful in order to find the ideal candidates for the role. Johnson outlined some of the characteristics best suited for stewards.

    Coming from both business and IT: Many times, data stewards do best when they have a background in both technology and line-of-business department work. Johnson referred to them as “purple people” – having skills and experience spanning these two different job positions. Data stewards should be multiskilled, as well as “bilingual” and “bicultural” when it comes to the very different worlds of, say, product development and cloud management

    Acting as bridges: Data stewards should be able to translate both simple and complex information and communicate it in written or oral form. Johnson recommended that they also have a good sense of objectivity, distinguishing fact from fiction, and be able to envision what challenges and issues a company might face in the future.

    Excited by data: Thinking globally and participating in an influence culture, data stewards should get immersed in the ideas surrounding good Data Governance and better data handling. “When you’re talking to somebody, and they get really excited about data and their eyes light up, and they’re all energized and stuff, it’s a good sign – they might be fit for a steward role,” Johnson said.

    Data stewards are change agents, Johnson reminded the audience, which ultimately benefits the employers who rely on them to develop best practices for data policies and processes.

    “Data stewards want to embrace change and be part of that change disruption in your organization. If you keep going status quo, you are more than likely not going to reach the outcomes you want. So, you’ve got to change something, and your steward is going to be part of that change process.”

    Help Data Stewards Achieve Success

    Once you’ve found capable data stewards within your organization, you must actively position them for success. “Create a super-transparent list of as many data problems as you’re working on – the issues, the questions, etc.,” recommended Johnson. Next, ensure your data stewards have access to tools that not only provide organization for frameworks but also display their value to stakeholders. Organizations can support data stewards by taking the following measures:

    • Fostering awareness of data challenges: Stewards can use a data quality tracker to sort, assess, categorize, and triage different types of tasks or requirements and then share the results with stakeholders.
    • Classifying data with sensitivity labels: Labeling data confidential or public can help data stewards assess the data assets and work with them in the ways mentioned above. 
    • Cultivating regulatory transparency: First, the company should list applicable state, federal, and international regulatory regimens, such as for California’s Consumer Privacy Act, the federal HIPAA standard, and the European Union’s GDPR. Then, data stewards can help the business make compliance transparent with data reporting tools. 
    • Showcasing program value: Using labels like people, processes, data, and technology, data stewards can form reports that show the value of actions and drive buy-in when it’s needed the most.

    Most importantly, foster a sense of community that brings data stewards together, celebrates their successes, and documents their stories to acknowledge their accomplishments and establish their credibility within the enterprise.

    “Share data steward successes at your council meetings – maybe do videos and once a year release them through internal teams,” suggested Johnson. “Give data stewards the kudos that they deserve and make that very public facing within your company, so that people are aware of all that work they’re doing.”

    Building the connective tissue between people and departments will help achieve a supportive corporate culture, allowing data stewards to properly manage data assets and ensure they are secure, trustworthy, and put to good use within the organization.

    Author: Justin Stoltzfus

    Source: Dataversity

  • Everything you need to know about a database management system and its uses

    Everything you need to know about a database management system and its uses

    Strong database management facilitates fast and effective business decision-making.

    Data drives everyday decision-making to help businesses complete tasks and accomplish their goals. Therefore, it requires proper management. But the question is how to effectively manage business data to ensure quick decision-making and smooth workflows? Using a database management system is the answer.

    A database management system makes it easier to store, organize, and share data across your business departments. It pulls data from the various tools, platforms, and applications your business uses and centralizes its storage so it can be easily searched and retrieved. It also eliminates risks such as data loss that delay or disrupt daily workflows.

    If you’re someone who works with data day in and day out or who relates to the everyday challenges of managing databases, this blog is for you. We explain what a database management system is and how you can use it to ensure data integrity and streamline data management processes.

    What is a database management system?

    A database management system is a software platform that helps you store and organize data. It creates a single centralized data source that can be used by stakeholders across departments. It combines the capabilities of data manipulation, analytics, and reporting to ensure better use of key data points.

    A database management system acts as an interface between your databases and employees. Employees can add, update, access, and delete data in the databases, based on the levels of permissions you assign to them. You can use database management software for:

    • Data management: Store, manage, categorize, and update business data.
    • Data retrieval: Find specific data points using the search functionality.
    • Queries: Run queries to perform specific actions such as calculations.
    • Data replication: Create duplicate instances of data and use them as a distributed database among employees.
    • Data security: Ensure data is secure from malicious attacks, unauthorized access, and accidents such as deleted data.
    • Data conversion: Transfer data from one database to another—also known as data migration.

    Why do you need a database management system?

    For people like you who depend on data to get their jobs done, using a database management system has multiple benefits. It assists with structured data management to ensure easy access and sharing. It also frees you from time-consuming manual processing tasks such as finding a specific data point and sharing it with employees.

    In addition, database management software ensures business data is shared only with relevant internal or external stakeholders. This helps mitigate risks such as information loss or unauthorized access.

    Here are a few benefits of implementing a database system into your work processes:

    • Increases productivity due to fewer data-related errors
    • Speeds up decision-making with timely and uninterrupted access to data
    • Improves data sharing and security by allowing access to only authorized users

    Your business’s need for database management software depends on how your employees use data. For instance, some might use it for daily research (normal priority), while others might use it to develop software tools (high priority). Keep such usage scenarios in mind when deciding whether or not to use database management systems.

    1. Relational database management system

    A relational database is a collection of data that is related to each other so different data points can be combined for better usability. The related points could be time, data, or logic, and the relation can be categorized in the following ways:

    • One on one: A data point in one table is related to a data point in another table.
    • One to many: A data point in one table is related to multiple data points in another table.
    • Many to one: Multiple data points in one table are related to a data point in another table.
    • Many to many: Multiple data points in one table are related to multiple data points in another table.

    A relational database management system is software that manages the storage and shareability of relational databases. It organizes data in a relational database by forming functional dependencies between multiple data points. It also stores data in an organized manner so it’s easier for employees to find and use data for their daily tasks.

    A relational data structure uses structured query language (SQL) to allow employees to run queries and find the information they need. A relational database management system typically:

    • Stores large volumes of data
    • Enables fast data-fetching
    • Allow users to simultaneously access multiple data elements

    2. Object-oriented database management system

    An object-oriented database is a collection of data that is presented in the form of an object. Multiple data points are combined into a single unit or object, making it easier for employees to find and use data. This type of database is used to accomplish high-performance tasks, such as software development and programming, that require faster decision-making.

    An object-oriented database management system is software that stores and manages databases as objects. It allows employees to look for complete objects instead of individual data points, resulting in a quicker search. An object-oriented database structure typically:

    • Maintains a direct relationship between database objects and real-world scenarios so the objects don’t lose their purpose
    • Provides an object identifier for employees to quickly locate objects and use them
    • Handles different data types such as pictures, text, and graphics

    3. Hierarchical database management system

    A hierarchical database is a collection of data that is organized into a tree-like structure wherein the stored data is connected through links and arranged from top to bottom. The primary data point is at the top, and the secondary data points follow in hierarchy depending on their relevance. Your business’s organizational structure is a perfect example of a hierarchical database.

    A hierarchical database management system is software that stores and manages hierarchical databases. It maintains accuracy in data hierarchy or flow based on the usage in work processes. Data within a hierarchical system is typically:

    • Easy to add and delete
    • Easy to search and retrieve
    • Follows a one-to-many relational data model

    4. Network database management system

    A network database is a collection of data where each data point is connected to multiple primary and secondary data points. Having interconnected data points makes this data model more flexible in terms of usage.

    A network database management system is software that stores and manages the interrelated data points in a network database. This software was built to overcome the shortcomings of a hierarchical database model that doesn’t allow interconnection between data points, besides the top-to-bottom flow. A network database system typically:

    • Facilitates quick data access
    • Supports many-to-many relational database models
    • Allows to create and manage complex database structures

    Who uses a database management system?

    In the table below, we share a couple of examples of professionals who use a database management system. Please note that these are just a few examples, and there are many such professionals for whom data is on top priority to accomplish tasks.



    Application programmers

    These are professionals who interact with databases to develop software apps and tools. They mostly use an object-oriented database management system to write codes and then convert them into objects for better usability. Converting large codes into smaller objects makes it less confusing for application programmers, especially when checking the performance of the developed applications.

    Data analysts

    These are professionals who collect raw business data and organize it into a database. They mostly use SQL in a relational database management system to identify raw data, draw valuable insights from it, and convert the insights into action points to impact business decision-making.

    DBMS software applications are also used in the following industry functions:

    • Railway reservation systems: A database management system is used to manage information such as ticket bookings, train timings, and arrival/departure status.
    • Library management: A database management system is used in libraries to manage the list of books. This includes keeping track of issuing dates, patron names, and author names.
    • Banking and finance: A database management system is used to manage the list of bank transactions, mode of payments, account details, and more.
    • Educational institutions: A database management system is used to manage the list of students, classes, lecture timings, and the number of hours logged in by both teachers and students.

    Use database management systems to enhance business decision-making

    Data is key to better decision-making, and efficient database management is key to getting data right. Therefore, it’s essential to manage your business data for effective usage, accessibility, and security.

    Author: Saumya Srivastava

    Source: Capterra

  • How data management can learn from basketball

    How data management can learn from basketball

    A data management plan in a company is not something that can be implemented in isolation by one department or a team in your organisation, it is rather a collective effort, similar to how different players perform in a basketball court.  

    From the smallest schoolyard to the biggest pro venue, from the simplest pickup game to the NBA finals, players, coaches, and even fans will tell you that having a game plan and sticking to it is crucial to winning. It makes sense; while all players bring their own talents to the contest, those talents have to be coordinated and utilized for the greater good. When players have real teamwork, they can accomplish things far beyond what they could achieve individually, even if they are nominally part of the squad. When team players aren’t displaying teamwork, they’re easy targets for competitors who know how to read their weaknesses and take advantage of them.

    Basketball has been used as an analogy for many aspects of business, from coordination to strategy, but among the most appropriate business activities that basketball most resembles is, believe it or not, data management. Perhaps more than anything, companies need to stick to their game plan when it comes to handling data: storing it, labeling it, and classifying it.

    A good data management plan could mean a winning season

    Without a plan followed by everyone in the organization, companies will soon find that their extensive collections of data are useless, just like the top talent a team manages to amass is useless without everyone on a team knowing what their role is. Failure to develop a data management plan could cost a company in time, and even in money. If data is not classified or labeled properly, search queries are likely to miss a great deal of it, skewing reports, profit and loss statements, and much more. 

    Even more worrying for companies is the need for an ability to produce data when regulators come calling. With the implementation of the European Union’s General Data Protection Regulation (GDPR), companies no longer have an option not to have a tight game plan for data management. According to GDPR rules, all EU citizens have 'the right to be forgotten', which requires companies to know what data they have about an individual, and demonstrate an ability to delete it to EU inspectors on demand. Those rules apply not just to companies in Europe, but to all companies that do business with EU residents as well. GDPR violators could be fined as much as €20 million, or 4% annual global turnover, whichever is greater.

    Even companies that have no EU clients or customers need to improve their data management game, because GDPR-style rules are moving stateside as well. California recently passed its own digital privacy law (set to go into effect in January), which gives state residents the right to be forgotten other states are considering similar laws. And with heads of large tech firms calling for privacy legislation in the U.S., it’s likely that federal legislation on the matter will be passed sooner than later.

    Data Management Teamwork, When and Where it Counts

    In basketball, players need to be molded to work together as a unit. A rogue player who decides that they want to be a 'shooting star' instead of following the playbook and passing when appropriate may make a name for themselves, but the team they are playing for is unlikely to benefit much from that kind of approach. Only when all the players work together, with each move complementing the other as prescribed by the game plan, can a team succeed.

    In data management, teams generate information that the organization can use to further its business goals. Data on sales, marketing, engagement with customers, praises and complaints, how long it takes team members to carry out and complete tasks, and a million other metrics all go into the databases and data storage systems of organizations for eventual analysis.

    With that data, companies can accomplish a great deal: Improve sales, make operations more efficient, open new markets, research new products and improve existing ones, and much more. That, of course, can only happen if all departments are able to access the data collected by everyone.

    Metadata management - A star 'player'

    Especially important is the data about data: the metadata, used to refer to data structures, labels, and types. When different departments, and even individual employees, are responsible for entering data into a repository, they need to follow the metadata 'game plan'.Tthe one where all data is being labeled according to a single standard, using common dictionaries, glossaries, and catalogs. Without that plan, data could easily get 'lost', and putting together search queries could be very difficult.

    Another problem is the fact that different departments will use different systems and products to process their data. Each data system comes with its own rules, and of course each set of rules is different. That there is no single system for labeling between the different products just contributes to the confusion, making resolution of metadata issues all the more difficult.

    Unfortunately, not everyone is always a team player when it comes to metadata. Due to pressure of time or other issues, different departments tend to use different terminology for data. For example, a department that works with Europe may label its dates in the form of year/month/day, while one that deals with American companies will use the month/day/year label. In a search form, the fields for 'years' and 'days' will not match across all data repositories, creating confusion. The department 'wins', but what about everyone else? And even in situations where the same terminology is used, the fact that different data systems are in use could impact metadata.

    Different departments have different objectives and goals, but team members cannot forget the overall objective: helping the 'team', the whole company, to win. The data they contribute is needed for those victories, those advancements. Without it, important opportunities could be lost. When data management isn’t done properly, teams may accomplish their own objectives, but the overall advancement of the company will suffer.

    'Superstars', whose objective is to aggrandize themselves, have no place on a basketball team; they should be playing one-on-one hoops with others of their type. Teams in companies should learn the lessonL if you want to succeed in basketball, or in data management, you need to work together with others, following the data plan that will ensure success for everyone.

    Author: Amnon Drori

    Source: Dataconomy

  • How to Extract Actionable Market Insights using a Holistic Data Approach

    How to Extract Actionable Market Insights using a Holistic Data Approach

    In the age of abundant information, companies face the challenge of extracting actionable insights from an overwhelming volume of data. Merely having access to good data is no longer sufficient; businesses require accurate, reliable, and unbiased information about customer opinions, behaviors, and motivations. 

    Making informed business decisions necessitates holistic data that encompasses the entire market, managed with consent and sophistication. Building this encompassing “big-picture” data set goes beyond first-party data collection – it requires combining and analyzing data from across the industry, including data from competitors and objective third-party sources.

    Unfortunately, several sectors are falling behind in the quest for comprehensive market data, impeding their ability to capitalize on the full potential of their data assets. 

    Integrated, Multi-Source Data Means Better Insights

    In an era of rampant misinformation and data manipulation, obtaining accurate, objective, multi-source market data becomes paramount. Businesses can no longer rely solely on first-party data within a “walled garden” environment – doing so will result in biased data sets that can provide only a partial picture of the market landscape. 

    Imagine if everyone’s favorite AI language model, ChatGPT, only sourced its input data from a single site – the result would be a far cry from the paradigm-shifting sensation that we see today. Rather, the platform’s API scrapes and integrates data from across the entire internet, allowing it to tap into almost the sum total of human knowledge. Similarly, businesses across industries must increasingly rely on data from multiple sources to gain deep insights into customer preferences, sentiment, and purchase behavior, and tailor their products, services, and marketing strategies accordingly. 

    Emerging technologies are making it easier and more cost-effective to store, process, and analyze large volumes of data. Additionally, the emergence of machine learning and artificial intelligence techniques is enhancing the ability to extract valuable insights from unstructured data. These technological advancements have lowered barriers to entry, making big data analytics accessible to a wider range of businesses.  

    But it’s not just about having the largest amount of available data – the type of data used is key to making the most informed business decisions.  

    Consumer Purchase Data Unlocks New Value

    The combination of third-party competitor and survey data with first-party consumer purchase data, such as transaction revenue and other direct sources of consumer information, is important for several reasons. Data obtained directly from transactions and consumer receipts provides accurate and reliable information about customer behavior, preferences, and purchase patterns, and offers an additional level of granularity and detail.  

    Transaction data captures specific information such as the products purchased, the time and location of the transaction and the payment method used. By supplementing these findings with self-reported data or surveys, businesses can achieve a more accurate understanding of their customers’ actions and make informed decisions based on factual information. 

    Partnerships and Collaboration Drive a More Well-Rounded View

    Partnerships, including those with competitors and stakeholders in the broader market, also play a significant role in accessing diverse contextual data and driving better analytics and return on investment (ROI). These partnerships enable businesses to access a broader range of data sources that they may not have access to independently.  

    By collaborating with competitors and objective third parties, companies can pool their data resources, creating improved data quality and accuracy by cross-validating and verifying information, as well as leading to cost and resource optimization. This expanded market reach allows organizations to tailor their strategies and offerings more effectively, driving better customer engagement and the all-important ROI. 

    Harnessing the Full Potential of Data Is Easier Said Than Done

    The amount of data generated globally is increasing exponentially. With the proliferation of digital devices, social media platforms, Internet of Things (IoT) devices, and other sources, there is an abundance of structured and unstructured data available. Traditional databases and data management approaches struggle to handle this vast volume and variety of information effectively. As a result, companies face added costs in terms of data management, storage, and infrastructure. 

    But the benefits of stronger, more comprehensive data and insights are well worth the cost. Advertising and CPG companies have embraced data-driven approaches, leveraging advanced analytics, machine learning and AI algorithms to optimize marketing campaigns, refine product offerings, and enhance customer experiences. These industries have realized the importance of accurate and holistic data in gaining a competitive edge. 

    In contrast, retailers and consumer durables brands, which constitute a significant portion of the retail market, are lagging. Many of these businesses continue to hold their data hostage or lack access to reliable objective data from external sources, narrowing their view of their own markets. 

    Failure to unlock the full potential of available data can result in reduced market share and diminished customer loyalty and profitability. These industries must prioritize the adoption of sophisticated data management practices, including comprehensive data collection, integration and analysis, to bridge the gap and remain competitive in the information age. 

    Date: August 24, 2023

    Author: Chad Pinkston

    Source: Dataversity

  • Leading your organization to success through Agile Data Governance

    Leading your organization to success through Agile Data Governance

    Laura Madsen wants to challenge your outdated ideas about Data Governance. “I’m pretty sure that we wouldn’t use software that we used 20 years ago, but we’re still using Data Governance and Data Governance methodologies the same way we did 20 years ago.” And although she advocates for Agile, she’s not an Agile coach or a SCRUM master; rather she wants companies to consider agility in a broader sense as well. “Very briefly, when we think about Agile, essentially, we think about reducing process steps.” She paraphrases David Hussman’s belief that there is no inherent value in “process” — process exists in order to prove to other people that “we’re doing something.” To that end, most organizations create an enormous number of process steps she refers to as “flaming hoops,” showing that there was a lot of work put into activities such as status updates, but nothing that provided actual value.

    Madsen is the author of Disrupting Data Governance, Chief Executive Guru at Via Gurus, and Mastermind at the Sisterhood of Technology Professionals (Sistech).

    Resource Use

    Historically, Data Governance has always been resource-intensive, and with Agile Data Governance in particular, she said, the most important resource is the individuals who do the work. The need for a data owner and a data steward for each domain, often with multiple stewards or owners covering the same data domain, etc., emerged as a system designed to serve data warehouses with hundreds of tables, and thousands of rows per table. “That’s a rather parochial idea in 2020, when we have petabytes of data blown through our data warehouses on any given day.”

    One resource-heavy relic from the past is the standing committee, which always starts off with a lot of participation and enthusiasm, but over time people disengage and participation dwindles. Another historical shortcoming in Data Governance is the reliance on one or two individuals who hold the bulk of the institutional knowledge. With the amount of risk attached to Data Governance processes, the people who serve as the governance linchpin are under a lot of pressure to do more with less, so when they leave, the Data Governance program often collapses.

    Instead, she recommends developing responsive and resilient capabilities by creating a dependency on multiple people with similar capabilities instead of one person who knows everything.

    To make best use of time and resources, committees should be self-forming and project-based. Distinct functions must be created for participating resources: “And we should be laser clear about what people are doing.”

    The Kitchen Sink

    Still another legacy from the past is the tendency to take a “kitchen sink” approach, throwing compliance, risk, security, quality, and training all under the aegis of Data Governance, creating a lack of clarity in roles. “When you do everything, then you’re really doing nothing,” she said. Data stewards aren’t given formal roles or capabilities, and as such, they consider their governance duties as something they do on the side, “for fun.”

    Madsen sees this as arising out of the very broad scope of the historical definition of Data Governance. Intersecting with so many different critical areas, Data Governance has become a catch-all. In truth, she said, instead of being wholly responsible for compliance, risk, security, protection, data usage, and quality, Data Governance lives in the small area where all of those domains overlap.

    She considers this narrower focus as essential to survival in modern data environments, especially now, when there are entire departments devoted to these areas. Expecting a Data Governance person to be fully accountable for compliance, privacy, risk, security, protection, data quality, and data usage, “is a recipe for absolute disaster.” Today, she said, there is no excuse for being haphazard about what people are doing in those intersecting areas.

    Four Aspects of Success

    To succeed, companies must move away from the kitchen sink definition of Data Governance and focus on four aspects:

    These categories will not need equal focus in every organization, and it’s expected that priorities will shift over time. Madsen showed a slide with some sample priorities that could be set with management input:

    • Increased data use at 40% importance
    • Quality at 25%
    • Management at 25%
    • Protection at 10%

    From an Agile perspective, every sprint or increment can be measured against those values, creating “an enormous amount of transparency.” And although executives may not care about the specific tasks used to address those priorities, they will care that they are being tackled strategically, she said.

    Increased Use of Data

    If the work of Data Governance isn’t leading to greater use of data, she says, “What the heck are we doing?” Building data warehouses, creating dashboards, and delivering ad hoc analytics are only useful if they enable greater data use. All governance activity should be focused toward that end. The only way to get broad support for Data Governance is to increase the usage of the data.

    Data Quality

    Record counts and data profiling can show what’s in the data and whether or not the data is right, but analysis is not the same as data quality. “What we’re really driving towards here is the context of the data,” Madsen said, which leads to increased data use. The core of Data Quality Management is ensuring it has meaning, and the only way for the data to have meaning is to provide context.

    Data Management

    She talks specifically about the importance of lineage within the context of Data Management. Most end users only interact with their data at the front end when they’re entering something, and at the back end, when they see it on a report or a dashboard. Everything that happens in between those two points is a mystery to them, which creates anxiety or confusion about the accuracy or meaning of the end result. “Without lineage tools, without the capability of looking at and knowing exactly what happened from the source to the target, we lose our ability to support our end users.” For a long time those tools didn’t exist, but now they do, and those questions can be answered very quickly, she said.

    Data Protection

    Although Data Governance has a part in mitigating risk and protecting data, again, these are areas where governance should not be fully responsible. Instead, governance should be creating what Madsen calls “happy alliances” with those departments directly responsible for data protection, and focusing on facilitating increased data usage. This is often reversed in many organizations: If data is locked down to the point where it’s considered “completely safe,” risk may be under control, but no one is using it.

    Moving into the Future/Sweeping Away the Past—Fixing Committees

    Committees, she said, are not responsive, they’re not Agile and they don’t contribute to a resilient Data Governance structure. Theoretically, they do create a communication path of sorts, because a standing meeting at least assumes participants are paying attention for a specific period of time — until they lose interest. 

    What works better, she said, is self-forming Scrum teams or self-forming Agile teams that are on-demand or project-based, using a “backlog” (list of tasks) that becomes the working document for completing the company’s project list. “You come together, you work on the thing, and then you go your own separate ways.”

    A sample self-forming Agile team might consist of a CDO, serving as a product owner, someone from

    security, privacy, and IT, which creates regulatory and IT standards, and executives from business departments like finance, sales, or operations, who might also serve assubject matter experts.

    The backlog serves as a centralized document where data issues are tracked, responsibilities are outlined and milestones on the way to completion are logged.

    Traditional concepts like data ownership and data stewardship still have a part, but they are in service to a project or initiative rather than a fixed area or department. When the project is completed, the team disbands.

    Named Data Stewards

    Named data stewards serve as a resource for a particular project or area, such as the customer data domain. Named data stewards or owners for each area of responsibility should be published so that anyone can quickly and easily find the data steward for any particular domain.

    On Demand Data Stewards

    “Everyone’s a data steward, just like everyone’s in charge of sales.” Anyone who has a question about the data and wants to know more is, in that moment, a data steward, she said, whether they are trained for it or not. By taking ownership of a question and being willing to find an answer, the “on-demand” steward gains the ability to help the organization do a better job in that particular moment. “Ownership is so integral to successful deployment of any data function in an organization.”

    Ensuring Success

    To sum up, Madsen recommends starting a backlog, using it to consistently document exit criteria (your definition of “done”), and committing to actively managing it. Start thinking like a Data Governance product owner, keep communications open among intersecting areas — those “happy alliances” — and keep the ultimate goal of increased data use in mind. Focus on progress over perfection, she says, “And then just keep swimming, just keep swimming …”

    Author: Amber Lee Dennis

    Source: Dataversity

  • Making the case for universal data health standards  

    Making the case for universal data health standards

    It’s a big data world; we’re just living in it

    In late 2020, a CEO at an American bank revealed the thinking that’s becoming common in many businesses these days. “We’re a 103-year-old bank,” their CEO told me. “We’re doing everything on spreadsheets. But we are trying to become a highly profitable, digital-first bank that anticipates financial needs and empowers our clients with frictionless experiences. We need to become a data company.”

    Companies in every industry are utterly dependent on their data. Retailers do more than sell goods in stores; their success depends on collecting, analyzing, and sharing data on consumer behavior, preferences, and actions. Financial services companies can mine the wealth of data they obtain on trades for insights and sell it as a source of intelligence, creating additional revenue streams. Healthcare providers no longer just treat their patients’ illnesses and injuries — rather, they collect and analyze data to treat the patient before their condition becomes acute.

    Every business is now in the data business. Even before COVID hit, many organizations had already begun that journey. When we found ourselves face-to-face with a global pandemic, the need to transform became more urgent.

    Too many trees and not enough forest

    But even though companies want and need to become data-driven, they’re not that successful at doing it yet. Surveys show that nearly 70% of companies report that they have not created a data-driven organization, and over half are not yet treating data as a business asset. Companies know that the path to the future depends on using data. So why is using data so challenging?

    For decades, managing and using data for analysis was focused on the mechanics of the process: collecting data, cleaning it, storing it, and cataloging it. It turns out this was the wrong problem to solve. The preoccupation with the mechanics of data management created some enormous challenges:

    • There’s no connection between the people who prepare data and those who make the decisions or assess the state of the business.
    • There’s no way for the people and systems on the front lines to easily validate that the data fueling day-to-day business is reliable or risk-free.
    • The piecemeal approach to managing, integrating, and storing data has created silos. It is expensive and difficult to manage, and it also creates dark data where analysis cannot penetrate.
    • For the most part, software and platforms for moving, collecting, preparing, and storing data is not helping companies gain a deeper understanding of the data they have or helping drive better data outcomes.

    More and more companies finally realize that this piecemeal approach doesn’t work. It’s not enough to simply collect, move, and prepare data more efficiently. 

    Data management is focused on the wrong things

    The data management market, estimated to be worth about $130 billion, has attracted a lot of attention recently, and rightly so. These solutions have become highly effective at moving and storing more corporate data. But in our view, for many companies, this efficiency is creating as much of a risk as it is a reward. 

    While capturing and storing data was a problem, it was never the problem and certainly never the end game. The old message to companies was that they should collect as much data as possible and figure out how to use it later. Well, later has arrived and many companies are ill-equipped to move from being data-saturated to being data-driven.

    When data is moved and stored without any other considerations, it essentially becomes a digital landfill of corporate information. Instead of solving problems, data is making it harder to sort through the chaos. Companies are drowning in their data.

    We have a very scary situation on our hands. A huge number of companies, all of which rely on data to stay in business and now also believe they need to become a data company, still haven’t addressed some of the most basic components of the data equation. They still don’t know what data they have, where it is, or who is using it, and, critically, they have absolutely no way to measure its health. 

    Ask any company how they measure the health of their business, and they will list metrics backed by the data they run their business on. Besides employees, data is the single most important asset any company has, yet it’s the least understood or measured. Data runs the world, and yet it’s the one thing we understand the least.

    Getting a pulse on corporate data health

    Data management can’t be a simple pass-through as it typically is today. It needs to be an active and intentional system that increases an organization’s understanding of its data — its reliability, risk, and opportunity to provide value for the business. You should have visibility and clarity in your data. The solutions you use to manage data should provide the knowledge that will help make your organization smarter, more agile, and more efficient while avoiding risk.

    This may sound impossible. Is it really plausible to understand corporate data — what it is, where it’s located, whether it’s accurate, who’s touched it, and how it’s distributed? Is it really possible to get a measurable and quantifiable view into the most valuable, yet today the most intangible, business asset?

    Yes. Every business can do it through a concept called data health.

    Data health is Talend’s vision for a holistic system of preventative measureseffective treatments, and a supportive culture to actively manage the well-being of corporate information. The system would include monitoring and reporting tools for helping organizations understand and communicate, in a quantifiable way, the overall reliability, risk, and return of an asset that’s essential to their viability.

    In the future, the aim is for data health solutions to help create a universal set of metrics to evaluate the health of corporate data and establish it as an essential indicator of the overall strength of a business.

    For too long we’ve treated data as simple, concrete units: cells on a spreadsheet, fields in a database — passive digital objects waiting for an analyst. But that’s no longer a sufficient model. Data is a complex, constantly changing organism. New inputs flow in and out, updated by users and transformed by shifting contexts. Those inputs and actions both provide an opportunity to learn about and change the value of the data itself. To truly understand what our data means, we need a more responsible, holistic view of that data.

    Data is complex, and every organization has its unique requirements, regulations, and risk tolerance. This is why we imagine data health as a journey. Just like human health, data health would be different for companies of every age, life stage, and maturity level.

    Our initial framework imagines three areas for companies to focus on as they begin the journey to establish data health: 

    • Preventative measures — preemptively identifying and resolving data challenges
    • Effective treatments — systematically improving data reliability and reducing risk
    • Supportive culture — establishing an organizational discipline around data care and maintenance

    Overseeing all of these focus areas is a comprehensive system of proactive monitoring and reporting to indicate success in achieving your data’s well-being. The combination of technologies and cultural practices that form this system will be unique to every organization, but the standards applied will be universal.

    A vision for a better future

    We believe there will come a time in the not-too-distant future where we’ll look back and wonder how we ever functioned in business or as a broader society, without a quantifiable way to measure the reliability, risk, and return of an asset so critical to our success. The risks of compromised data — and opportunities for innovation — are just too great. 

    In 2019, Vyaire Medical, a global respiratory care company, kicked off a digital transformation initiative. Like many companies, their data infrastructure was a patchwork of one-off solutions and inefficient structures, including 12 enterprise planning systems, meaning that employees had to collect data on parts location, production, factory efficiency, and more from wherever they could. Decision-makers often received conflicting data depending on its source. This created confusion and made it difficult to make the fast, data-driven decisions necessary for the company to continue to thrive. There were so many design elements, top to bottom, that were never built to scale to the numbers we needed to scale to,” said Ed Rybicki, Vyaire’s global chief information officer. “So we really needed to rethink the whole thing.”

    “Rethinking the whole thing” meant making some key infrastructure decisions — moving to a cloud infrastructure rather than remaining on-prem, building a centralized data repository that anyone in the company could access, and instituting data quality standards to ensure the data in the company-wide data lake is clean, accurate, and available in real-time. In short, they made the call to ensure healthy data to anyone who needed it for analysis and business decision-making

    Vyaire’s wholesale transformation project proved extremely prescient; in 2020, COVID emerged, which meant there was more demand for their ventilators than ever before. “We had to replicate a highly customized manufacturing process for this line of ventilators,” said Rybicki. “It was probably 20 times beyond what had ever been done before, all in six or seven months. At the end of the day, we knew that if we weren’t able to scale up, it would mean that people who needed ventilators might not get them. We were able to scale up these old systems—and help save lives.”

    For Vyaire, data health was no longer optional. It’s not something they could live without or could plan for in a year. Ensuring data health was what the business needed as soon as they could deliver it. Vyaire Medical’s experience is not a fluke. Because they made deliberate investments in the health of their data, they had complete clarity into their entire operation from the factory floor to the boardroom. They knew exactly what the capacity of each cog in their supply chain could be and what it should be. And that meant that they were able to answer the call of a lifetime — to meet the moment and produce the equipment that patients around the world desperately needed. Thanks to their data, Vyaire optimized their business and overcame the obstacles a global pandemic presented. Data health offers that exact type of clarity that you could have into your business.

    No one should ever have to make decisions on information they can’t find, see, or understand. Data should not be a black box. The ultimate goal of creating a data health practice is to establish confidence and total visibility into your data, and therefore a real quantification of value. You should understand how data is working in every aspect of your business. You should be able to identify what data is doing for you. You should be able to establish an ROI for all your data investments. We believe that once you establish your company’s baselines for data health, it will become as indispensable as Google Maps — you won’t be able to imagine life without it.

    Author: Christal Bemont

    Source: Talend

  • Making your Organization more intelligent with a Cloud Data Strategy

    Making your Organization more intelligent with a Cloud Data Strategy

    At a time when most major companies are showing a long-range commitment to “data-driven culture,” data is considered the most prized asset. An Enterprise Data Strategy, along with aligned technology and business goals, can significantly contribute to the core performance metrics of a business. The underlying principles of an Enterprise Data Strategy comprise a multi-step framework, a well-designed strategy process, and a definitive plan of action. However, in reality, very few businesses today have their Data Strategy aligned with overall business and technology goals.

    Data Management Mistakes Are Costly

    Unless the overall business and technology goals of a business are aligned with a Data Strategy, the business may suffer expensive Data Management failure incidents from time to time. If the Data Strategy is implemented in line with a well-laid out action plan that seeks to transform the current state of affairs into “strategic Data Management initiatives” leading to the fulfillment of desirable business needs and objectives in the long term, then there is a higher chance of that Data Strategy achieving the desired outcomes. 

    Data provides “insights” that businesses use for competitive advantage. When overall business goals and technology goals are left out of the loop of an Enterprise Data Strategy, the data activities are likely to deliver wrong results, and cause huge losses to the business.

    What Can Businesses Do to Remain Data-Driven?

    Businesses that have adopted a data-driven culture and those expecting to do so, can invest some initial time and effort to explore the underlying relationships between the overall business goals, technology goals, and Data Strategy goals. The best part is they can use their existing advanced analytics infrastructure to make this assessment before drafting a policy document for developing the Data Strategy.

    This initial investment in time and effort will go a long way toward ensuring that the business’s core functions (technology, business, and Data Science) are aligned and have the same objectives. Without this effort, the Data Strategy can easily become fragmented and resource-heavy—and ineffective.

    According to Anthony Algmin, Principal at Algmin Data Leadership, “Thinking of a Data Strategy as something independent of Business Strategy is a recipe for disaster.”

    Data Governance has recently become a central concern for data-centric organizations, and all future Data Strategies will include Data Governance as a core component. The future Data Strategy initiatives will have to take regulatory compliances seriously to ensure long-term success of such strategies. The hope is that this year, businesses will employ advanced technologies like big data, graph, and machine learning (ML) to design and implement a strong Data Strategy.

    In today’s digital ecosystem, the Data Strategy means the difference between survival and extinction of a business. Any business that is thinking of using data as a strategic asset for predetermined business outcomes must invest in planning and developing a Data Strategy. The Data Strategy will not only aid the business in achieving the desired objectives, but will also keep the overall Data Management activities on track.

    A Parallel Trend: Rapid Cloud Adoption

    As Data Strategy and Data Governance continue to gain momentum among global businesses, another parallel trend that has surfaced is the rapid shift to cloud infrastructures for business processing.

    With on-premise Data Management practices, Cloud Data Management practices also revolve around MDM, Metadata Management, and Data Quality. As the organizations continue their journey to the cloud, they will need to ensure their Data Management practices conform to all Data Quality and Data Governance standards.

    A nagging concern among business owners and operators who have either shifted to the cloud or are planning a shift is data security and privacy. In fact, many medium or smaller operations have resisted the cloud as they are unsure or uninformed about the data protection technologies available on the cloud. Current businesses owners expect cloud service providers to offer premium data protection services.

    The issues around Cloud Data Management are many: the ability of cloud resources to handle high-volume data, the security leaks in data transmission pipelines, data storage and replication policies of individual service providers, and the possibilities of data loss from cloud hosts. Cloud customers want uninterrupted data availability, low latency, and instant recovery—all the privileges they have enjoyed so far in an on-premise data center.

    One technology solution often discussed in the context of cloud data protection is JetStream. Through a live webinar, Arun Murthy, co-founder and Chief Product Officer of Horton Works, demonstrated how the cloud needs to be a part of the overall Data Strategy to fulfill business needs like data security, Data Governance, and holistic user experience. The webinar proceedings are discussed in Cloud Computing—an Extension of Your Data Strategy.

    Cloud Now Viewed as Integral Part of Enterprise Data Strategy

    One of the most talked about claims made by industry experts at the beginning of 2017 was that it “would be a tipping point for the cloud.” These experts and cloud researchers also suggested that the cloud would bring transformational value to business models through 2022, and would become an inevitable component of business models. According to market-watcher Forrester, “cloud is no longer about cheap servers or storage, (but), the best platform to turn innovative ideas into great software quickly.

    As cloud enables big data analytics at scale, it is a popular computing platform for larger businesses who want the benefits without having to make huge in-house investments. Cloud holds promises for medium and small businesses, too, with tailor-made solutions for custom computing needs at affordable cost.

    The following points should be kept in mind while developing a strategy plan for the cloud transformation:

    • Consensus Building for Cloud Data Strategy: The core requirement behind building a successful Data Strategy for the cloud is consensus building between the central IT Team, the cloud architect, and the C-Suite executives. This problem is compounded in cases where businesses may be mix-matching their cloud implementations.
    • Data Architectures on Native Cloud: The news feature titled Six Key Data Strategy Considerations for Your Cloud-Native Transformation throws light on cloud-native infrastructure, which is often ignored during a business transformation. According to this article, though enterprises are busy making investments in a cloud-native environment, they rarely take the time to plan the transformation, thus leaving Data Architecture issues like data access and data movement unattended. 
    • Creating Data Replicas: Data replication on the cloud must avoid legacy approaches, which typically enabled data updating after long durations.
    • Data Stores across Multiple Clouds: HIT Think: How to Assess Weak Links in a Cloud Data Strategy specifically refers to storage of healthcare data, where data protection and quick data recovery are achieved through the provisioning of multiple cloud vendors. These solutions are not only cost-friendly, but also efficient and secure. 

    Author: Paramita (Guha) Ghosh

    Source: Dataversity

  • Managing data at your organization? Take a holistic approach

    Managing data at your organization? Take a holistic approach

    Taking a holistic approach to data requires considering the entire data lifecycle – from gathering, integrating, and organizing data to analyzing and maintaining it. Companies must create a standard for their data that fits their business needs and processes. To determine what those are, start by asking your internal stakeholders questions such as, “Who needs access to the data?” and “What do each of these departments, teams, or leaders need to know? And why?” This helps establish what data is necessary, what can be purged from the system, and how the remaining data should be organized and presented.

    This holistic approach helps yield higher-quality data that’s more usable and more actionable. Here are three reasons to take a holistic approach at your organization:

    1. Remote workforce needs simpler systems

    We saw a massive shift to work-from-home in 2020, and that trend continues to pick up speed. Companies like Twitter, Shopify, Siemens, and the State Bank of India are telling employees they can continue working remotely indefinitely. And according to the World Economic Forum, the number of people working remotely worldwide is expected to double in 2021.

    This makes it vital that we simplify how people interact with their business systems, including CRMs. After all, we still need answers to everyday questions like, “Who’s handling the XYZ account now?” and “How did customer service solve ABC’s problem?” But instead of being able to ask the person in the next office or cubicle, we’re forced to rely on a CRM to keep us up to date and make sure we’re moving in the right direction.

    This means team members must input data in a timely manner, and others must be able to access that data easily and make sense of it, whether it’s to view the sales pipeline, analyze a marketing campaign’s performance, or spot changes in customer buying behavior.

    Unfortunately, the CRMs used by many companies make data entry and analytics challenging. At best, this is an efficiency issue. At worst, it means people aren’t inputting the data that’s needed, and any analysis of spotty data will be flawed. That’s why we suggest companies focus on improving their CRM’s user interface, if it isn’t already user-friendly.

    2. A greater need for data accuracy

    The increased reliance on CRM data also means companies need to ramp up their Data Quality efforts. People need access to clean, accurate information they can act on quickly.

    It’s a profound waste of time when the sales team needs to verify contact information for every lead before they reach out, or when data scientists have to spend hours each week cleaning up data before they analyze it.

    Yet, according to online learning company O’Reilly’s The State of Data Quality 2020 report, 40% or more of companies suffer from these and other major Data Quality issues:

    • Poor quality controls when data enters the system
    • Too many data sources and inconsistent data
    • Poorly labeled data
    • Disorganized data
    • Too few resources to address Data Quality issues

    These are serious systemic issues that must be addressed in order to deliver accurate data on an ongoing basis.

    3. A greater need for automation

    Data Quality Management is an ongoing process throughout the entire data lifecycle. We can’t just clean up data once and call it done.

    Unfortunately, many companies are being forced to work with smaller budgets and leaner teams these days, yet the same amount of data cleanup and maintenance work needs to get done. Automation can help with many of the repetitive tasks involved in data cleanup and maintenance. This includes:

    • Standardizing data
    • Removing duplicates
    • Preventing new duplicates
    • Managing imports
    • Importing/exporting data
    • Converting leads
    • Verifying data

    A solid business case

    By taking a holistic approach to Data Management – including simplifying business systems, improving data accuracy, and automating whenever possible – companies can improve the efficiency and effectiveness of teams throughout their organization. These efforts will help organizations come through the pandemic stronger, with a “new normal” for data that’s far better than what came before.

    Author: Oilivia Hinkle

    Source: Dataversity

  • Master Data Management and the role of (un)structured data

    MasterDataManagementTraditional conversations about master data management’s utility have centered on determining what actually constitutes MDM, how to implement data governance with it, and the balance between IT and business involvement in the continuity of MDM efforts.

    Although these concerns will always remain apposite, MDM’s overarching value is projected to significantly expand in 2018 to directly create optimal user experiences—for customers and business end users. The crux of doing so is to globalize its use across traditional domains and business units for more comprehensive value.

    “The big revelation that customers are having is how do we tie the data across domains, because that reference of what it means from one domain to another is really important,” Stibo Systems Chief Marketing Officer Prashant Bhatia observed.

    The interconnectivity of MDM domains is invaluable not only for monetization opportunities via customer interactions, but also for streamlining internal processes across the entire organization. Oftentimes the latter facilitates the former, especially when leveraged in conjunction with contemporary opportunities related to the Internet of Things and Artificial Intelligence.

    Structured and Unstructured Data

    One of the most eminent challenges facing MDM related to its expanding utility is the incorporation of both structured and unstructured data. Fueled in part by the abundance of external data besieging the enterprise from social, mobile, and cloud sources, unstructured and semi-structured data can pose difficulties to MDM schema.

    After attending the recent National Retail Federation conference with over 30,000 attendees, Bhatia noted that one of the primary themes was, “Machine learning, blockchain, or IoT is not as important as how does a company deal with unstructured data in conjunction with structured data, and understand how they’re going to process that data for their enterprise. That’s the thing that companies—retailers, manufacturers, etc.—have to figure out.”

    Organizations can integrate these varying data types into a single MDM platform by leveraging emerging options for schema and taxonomies with global implementations, naturally aligning these varying formats together. The competitive advantage generated from doing so is virtually illimitable. 

    Original equipment manufacturers and equipment asset management companies can attain real-time, semi-structured or unstructured data about failing equipment and use that to influence their product domain with attributes informing the consequences of a specific consumer’s tire, for example. The aggregation of that semi-structured data with structured data in an enterprise-spanning MDM system can influence several domains. 

    Organizations can reference it with customer data for either preventive maintenance or discounted purchase offers. The location domain can use it to provide these services close to the customer; integrations with lifecycle management capabilities can determine what went wrong and how to correct it. “That IoT sensor provides so much data that can tie back to various domains,” Bhatia said. “The power of the MDM platform is to tie the data for domains together. The more domains that you can reference with one another, you get exponential benefits.”

    Universal Schema

    Although the preceding example pertained to the IoT, it’s worth noting that it’s applicable to virtually any data source or type. MDM’s capability to create these benefits is based on its ability to integrate different data formats on the back end. A uniformity of schema, taxonomies, and data models is desirable for doing so, especially when using MDM across the enterprise. 

    According to Franz CEO Jans Aasman, traditionally “Master Data Management just perpetuates the difficulty of talking to databases. In general, even if you make a master data schema, you still have the problem that all the data about a customer, or a patient, or a person of interest is still spread out over thousands of tables.” 

    Varying approaches can address this issue; there is growing credence around leveraging machine learning to obtain master data from various stores. Another approach is to considerably decrease the complexity of MDM schema so it’s more accessible to data designated as master data. By creating schema predicated on an exhaustive list of business-driven events, organizations can reduce the complexity of myriad database schemas (or even of conventional MDM schemas) so that their “master data schema is incredibly simple and elegant, but does not lose any data,” Aasman noted.

    Global Taxonomies

    Whether simplifying schema based on organizational events and a list of their outcomes or using AI to retrieve master data from multiple locations, the net worth of MDM is based on the business’s ability to inform the master data’s meaning and use. The foundation of what Forrester terms “business-defined views of data” is oftentimes the taxonomies predicated on business use as opposed to that of IT. Implementing taxonomies enterprise-wide is vital for the utility of multi-domain MDM (which compounds its value) since frequently, as Aasman indicated, “the same terms can have many different meanings” based on use case and department.

    The hierarchies implicit in taxonomies are infinitely utilitarian in this regard, since they enable consistency across the enterprise yet have subsets for various business domains. According to Aasman, the Financial Industry Bank Ontology can also function as a taxonomy in which, “The higher level taxonomy is global to the entire bank, but the deeper you go in a particular business you get more specific terms, but they’re all bank specific to the entire company.” 

    The ability of global taxonomies to link together meaning in different business domains is crucial to extracting value from cross-referencing the same master data for different applications or use cases. In many instances, taxonomies provide the basis for search and queries that are important for determining appropriate master data.

    Timely Action

    By expanding the scope of MDM beyond traditional domain limitations, organizations can redouble the value of master data for customers and employees. By simplifying MDM schema and broadening taxonomies across the enterprise, they increase their ability to integrate unstructured and structured data for timely action. “MDM users in a B2B or B2C market can provide a better experience for their customers if they, the retailer and manufacturer, are more aware and educated about how to help their end customers,” Bhatia said.


    Author: Jelani Harper

    Source: Information Management

  • Migros: an example of seizing the opportunities BI offers

    Migros: an example of seizing the opportunities BI offers

    Migros is the largest retailer in Turkey, with more than 2500 outlets selling fresh produce and groceries to millions of people. To maintain high-quality operations, the company depends on fresh, accurate data. And to ensure high data quality, Migros depends on Talend.

    The sheer volume of data managed by Migros is astonishing. The company’s data warehouse currently holds more than 200 terabytes, and Migros is running more than 7,000 ETL (extract, transform, load) jobs every day. Recently, the quality of that data became the focal point for the BI (business intelligence) team at Migros.

    “We have 4,000 BI users in this company,” said Ahmet Gozmen, Senior Manager of IT Data Quality and Governance at Migros. “We produce 5-6 million mobile reports every year that our BI analysts see on their personal dashboards. If they can’t trust the timeliness or the accuracy of the reports, they can’t provide trustworthy guidance on key business decisions.”

    In 2019, Mr. Gozmen and his team decided they needed a more reliable foundation on which to build data quality. “We were having a few issues with our data at that time,” he said. “There would be occasional problematic or unexpected values in reports—a store’s stock would indicate an abnormal level, for example—and the issue was in the data, not the inventory. We had to address these problems, and more than that we wanted to take our data analysis and BI capabilities to a higher level.''

    From Community to Commercial

    Initially, Mr. Gozmen’s team used the non-commercial version of Talend Data Quality. “It was an open-source solution that we could download and set up in one day,” he said. “At first, we just wanted to see whether we could do something with this tool or not. We explored its capabilities, and we asked the Talend Community if we had questions or needed advice.”

    Mr. Gozmen discovered that Talend had far more potential than he expected. “We found that the data quality tool was very powerful, and we started exploring what else we could do with Talend,” he said. “So we also downloaded the data integration package, then the big data package. Talend could handle the huge volumes of data we were dealing with. And very soon we started thinking about the licensed, commercial versions of these solutions, because we saw a great deal of potential not only for immediate needs but for future plans.”

    By upgrading to the commercial versions, Migros also elevated the level of service and support that was available. “The Community served our purposes well in the early stages,” said Mr. Gozmen, “but with the commercial license we now have more personalized support and access to specialists who can help us immediately with any aspect of our implementation.”

    From Better Data Quality to Big Data Dreams

    With Talend Data Quality, Migros has improved the accuracy and reliability of its BI reporting, according to Mr. Gozmen. “We are a small department in a very big company,” he said, “but with help from Talend we can instill confidence in our reporting, and we can start to support other departments and have a larger impact on improving processes and even help generate more income.”

    The higher level of data quality Migros has achieved with Talend has also led Mr. Gozmen to consider using Talend for future data initiatives. “We have big dreams, and we are testing the waters on several fronts,” he said. “We are exploring the possibilities for predictive analytics, and we feel Talend’s big data capabilities are a good match.”

    The Migros team is also considering using Talend in moving from its current batch processing mode to real-time data analysis, according to Mr. Gozmen. “We are currently using date-minus-one or date-minus two batch processing, but we want to move to real-time big data predictive analytics and other advanced capabilities as soon as possible,” he said. “We are currently testing new models that can help us achieve these goals.”

    While the business advantages of using Talend are manifesting themselves in past, present, and future use cases, Mr. Gozmen sums them up this way: “With Talend we can trust our data, so business leaders can trust our reports, so we can start to use big data in new ways to improve processes, supply chain performance, and business results.”

    Author: Laura Ventura

    Source: Talend

  • Rubrik is data resilience leider in nieuwste Forrester report

    data science rubrik

    Rubrik is data resilience leider in nieuwste Forrester report

    In de nieuwste editie van het Forrester Wave-rapport over Data Resilience Solutions is Rubrik benoemd tot leider. De aanbieder op het gebied van multi-cloud data control kreeg zelfs de hoogste score toegekend op het gebied van strategie.

    Forrester heeft tien vendoren geëvalueerd op basis van veertig criteria, die weer zijn onderverdeeld in drie categorieën: huidige aanbod, strategie en aanwezigheid in de markt. Rubrik behaalde de hoogst mogelijke score op het gebied van strategie en security.

    'Rubrik past bij bedrijven die erop uit zijn om hun data resilience te vereenvoudigen, moderniseren en consolideren', aldus het rapport. Rubrik wordt omschreven als een ‘eenvoudige, intuïtieve en krachtige policy engine die de bescherming van data regelt, ongeacht het soort, de locatie of het doel van de data'.

    Volgens CEO Bipul Sinha van Rubrik laat de erkenning laat zien dat Rubrik goed is gepositioneerd om de transformatie van de data management-markt te leiden. 'Klanten stellen steeds hogere eisen aan data management-oplossingen, die verder gaan dan alleen back-up en recovery. Dat we de hoogste score hebben gekregen op het gebied van strategie bevestigt dat we op de juiste weg zijn om door middel van innovatie steeds beter te voldoen aan de vraag van onze klanten'.

    Bron: BI Platform

  • Solutions to help you deal with heterogeneous data sources

    Solutions to help you deal with heterogeneous data sources

    With enterprise data pouring in from different sources; CRM systems, web applications, databases, files, etc., streamlining data processes is a significant challenge as it requires integrating heterogeneous data streams. In such a scenario, standardizing data becomes a pre-requisite for effective and accurate data analysis. The absence of the right integration strategy will give rise to application-specific and intradepartmental data silos, which can hinder productivity and delay results.

    Consolidating data from disparate structured, unstructured, and semi-structured sources can be complex. A survey conducted by Gartner revealed that one-third of respondents consider 'integrating multiple data sources' as one of the top four integration challenges.

    Understanding the common issues faced during this process can help enterprises successfully counteract them. Here are three challenges generally faced by organizations when integrating heterogeneous data sources, as well as ways to resolve them:

    Data extraction

    Challenge: Pulling source data is the first step in the integration process. But it can be complicated and time-consuming if data sources have different formats, structures, and types. Moreover, once the data is extracted, it needs to be transformed to make it compatible with the destination system before integration.

    Solution: The best way to go about this is to create a list of sources that your organization deals with regularly. Look for an integration tool that supports extraction from all these sources. Preferably, go with a tool that supports structured, unstructured, and semi-structured sources to simplify and streamline the extraction process.

    Data integrity

    Challenge: Data Quality is a primary concern in every data integration strategy. Poor data quality can be a compounding problem that can affect the entire integration cycle. Processing invalid or incorrect data can lead to faulty analytics, which if passed downstream, can corrupt results.

    Solution: To ensure that correct and accurate data goes into the data pipeline, create a data quality management plan before starting the project. Outlining these steps guarantees that bad data is kept out of every step of the data pipeline, from development to processing.


    Challenge: Data heterogeneity leads to the inflow of data from diverse sources into a unified system, which can ultimately lead to exponential growth in data volume. To tackle this challenge, organizations need to employ a robust integration solution that has the features to handle high volume and disparity in data without compromising on performance.

    Solution: Anticipating the extent of growth in enterprise data can help organizations select the right integration solution that meets their scalability and diversity requirements. Integrating one data point at a time is beneficial in this scenario. Evaluating the value of each data point with respect to the overall integration strategy can help prioritize and plan. Say that an enterprise wants to consolidate data from three different sources: Salesforce, SQL Server, and Excel files. The data within each system can be categorized into unique datasets, such as sales, customer information, and financial data. Prioritizing and integrating these datasets one at a time can help organizations gradually scale data processes.

    Author: Ibrahim Surani

    Source: Dataversity

  • The (near) future of data storage

    The (near) future of data storage

    As data proliferates at an exponential rate, companies must not only store it. They must approach Data Management expertly and look to new approaches. Companies that take new and creative approaches to data storage will be able to transform their operations and thrive in the digital economy.

    How should companies approach data storage in the years to come? As we look into our crystal ball, here are important trends in 2020. Companies that want to make the most of data storage should be on top of these developments.

    A data-centric approach to data storage

    Companies today are generating oceans of data, and not all of that data is equally important to their function. Organizations that know this, and know which pieces of data are more critical to their success than others, will be in a position to better manage their storage and better leverage their data.

    Think about it. As organizations deal with a data deluge, they are trying hard to maximize their storage pools. As a result, they can inadvertently end up putting critical data on less critical servers. Doing so is a problem because it typically takes longer to access data on slower, secondary machines. It’s this lack of speed and agility that can have a detrimental impact on businesses’ ability to leverage their data.

    Traditionally organizations have taken a server-based approach to their data backup and recovery deployments. Their priority is to back up their most critical machines rather than focusing on their most business-critical data.

    So, rather than having backup and recovery policies based on the criticality of each server, we will start to see organizations match their most critical servers with their most important data. In essence, the actual content of the data will become more of a decision-driver from a backup point of view.

    The most successful companies in the digital economy will be those that implement storage policies based not on their server hierarchy but on the value of their data.

    The democratization of flash storage

    With the continuing rise of technologies like IoT, artificial intelligence, and 5G, there will be an ever-greater need for high-performance storage. This will lead to the broader acceptance of all-flash storage. The problem, of course, is that flash storage is like a high-performance car: cool and sexy, but the price is out of reach for most.

    And yet traditional disk storage simply isn’t up to the task. Disk drives are like your family’s old minivan: reliable but boring and slow, unable to turn on a dime. But we’re increasingly operating in a highly digital world where data has to be available the instant it’s needed, not the day after. In this world, every company (not just the biggest and wealthiest ones) needs high-performance storage to run their business effectively.

    As the cost of flash storage drops, more storage vendors, are bringing all-flash arrays to the mid-market and more organizations will be able to afford this high-performance solution. This price democratization will ultimately enable every business to benefit from technology.

    The repatriation of cloud data

    Many companies realize that moving to the cloud is not as cost-effective, secure, or scalable as they initially thought. They’re now looking to return at least some of their core data and applications to their on-premises data centers.

    The truth is that data volumes in the cloud have become unwieldy. And organizations are discovering that storing data in the cloud is not only more expensive than they thought but It’s also hard to access that data expeditiously due to the cloud’s inherent latency.

    As a result, it can be more beneficial in terms of cost, security, and performance to move at least some company data back on-premises.

    Now that they realize the cloud is not a panacea, organizations are embracing the notion of cloud data repatriation. They’re increasingly deploying a hybrid infrastructure in which some data and applications remain in the cloud, while more critical data and applications come back home to an on-premises storage infrastructure.

    Immutable storage for businesses of all sizes

    Ransomware will continue to be a scourge to all companies. Because hackers have realized that data stored on network-attached storage devices is extremely valuable, their attacks will become more sophisticated and targeted. This is a serious problem because backup data is typically the last line of defense. Hackers are also attacking unstructured data. The reason is that if the primary and secondary (backup) data is encrypted, businesses will have to pay the ransom if they want their data back. This increases the likelihood that an organization, without a specific and immutable recovery plan in place, will pay a ransom to regain control over its data.

    It is not a question of if, but when, an organization will need to recover from a ‘successful’ ransomware attack. Therefore, it’s more important than ever to protect this data with immutable object storage and continuous data protection. Organizations should look for a storage solution that protects information continuously by taking snapshots as frequently as possible (e.g., every 90 seconds). That way, even when data is overwritten, older objects remain as part of the snapshot: the original data. That way, even when data is overwritten,there always will be another, immutable copy of the original objects that constitute the company’s data that can be instantly recovered… even if it’s hundreds of terabytes.

    Green storage

    Global data centers consume massive amounts of energy, which contributes to global warming. Data centers now eat up around 3% of the world’s electricity supply. They are responsible for approximately two percent of global greenhouse gas emissions. These numbers put the carbon footprint of data centers on par with the entire airline industry.

    Many companies are seeking to reduce their carbon footprint and be good corporate citizens. As part of this effort, they are increasingly looking for more environmentally-friendly storage solutions, those that can deliver the highest levels of performance and capacity at the lowest possible power consumption.

    In 2020, organizations of all sizes will work hard to get the most from the data they create and store. By leveraging these five trends and adopting a modern approach to data storage, organizations can more effectively transform their business and thrive in the digital economy.

    The ‘Prevention Era’ will be overtaken by the ‘Recovery Era’

    Organizations will have to look to more efficient and different ways to protect unstructured and structured data. An essential element to being prepared in the ‘recovery era’ will involve moving unstructured data to immutable object storage with remote replication, which will eliminate the need for traditional backup. The nightly backup will become a thing of the past, replaced by snapshots every 90 seconds. This approach will free up crucial primary storage budget, VMware/Hyper-V storage, and CPU/memory for critical servers.

    While data protection remains crucial, in the data recovery era, the sooner organizations adopt a restore and recover mentality, the better they will be able to benefit from successful business continuity strategies in 2020 and beyond.

    Author: Sean Derrington

    Source: Dataversity

  • The Power of Real-Time Data Warehousing  

    The Power of Real-Time Data Warehousing

    Modern data management techniques which include real-time data warehousing are transforming how businesses use awareness. The tools are provided to the businesses that they need to stay on the cutting edge of data-driven decision-making by enabling the consistent integration of streaming data into conventional data warehouse solutions. The continual ingestion and analysis of data as it flows in real-time is made possible by this ground-breaking fusion, guaranteeing that organizations have access to the most recent data.

    With some additional improving operational efficiency, this transformation also makes it possible to take neutralizing actions, keep an eye on things in real-time, and have a quick reaction to shifting market conditions. Real-time Data Warehousing is important for surviving in this age of data-driven competition.

    Making timely and educated decisions is necessary for staying ahead of the competition in today’s fast-paced corporate environment. When it comes to maintaining and analyzing historical data, traditional data warehousing solutions have proved invaluable, but they frequently fail to meet when it comes to offering real-time insights. Organizations are increasingly utilizing real-time data warehousing, which integrates streaming data for real-time intelligence, to close this gap. We’ll discuss real-time data warehousing and how it is changing how businesses manage data in this post.

    Data Warehousing’s Development

    Since its inception, data warehousing has advanced significantly. Data warehouses were initially created largely for the purpose of organizing and preserving historical data for use in reporting and analysis. They were distinguished by batch processing, in which data was periodically gathered, converted, and loaded (ETL), typically on a nightly basis, into the warehouse. This method had some drawbacks, particularly when it is needed for quick answers to important questions.

    The Demand for Instantaneous Insights

    In the modern digital world, the data is produced at an unparalleled rate. Online interactions between customers and enterprises, continuous data production from IoT devices, and information stream generation from social media platforms. Organizations need real-time insights to make use of this plethora of data. Consider how an online retailer may alter marketing strategies on the fly by watching website traffic and sales in real-time, or how a banking institution could spot fraudulent transactions as they take place. Real-time data warehousing makes it possible for these scenarios.

    Real-time Data Warehousing: An Overview

    An architectural strategy called real-time data warehousing enables businesses to acquire, process, and analyze streaming data in real-time alongside their conventional historical data. This is achieved by combining streaming data platforms with established data warehousing techniques. Let’s examine some basic elements and tenets of real-time data warehousing.

    Organizations utilize streaming data platforms like Apache Kafka or AWS Kinesis to ingest data in real time. These technologies enable the continual absorption of data in manageable pieces.

    After streaming data has been ingested, it is processed in real-time. This may involve data aggregation, transformation, and enrichment. For this, contemporary tools like Spark Streaming and Apache Flink are used.

    • Integration with Data Warehouse: The classic data warehouse, often known as the “lakehouse” concept, is easily integrated with the processed streaming data. This blends the advantages of real-time data analytics with data warehousing.
    • Analytics and Querying: Business users have the ability to run real-time queries on both historical and streaming data. This process is facilitated by SQL-like querying languages and robust analytical tools, which offer quick insights into shifting data trends.

    Real-time Data Warehousing Benefits

    Real-time data warehousing adoption benefits businesses in a number of ways.

    • Faster Decision-Making: With the help of real-time information, organizations can act fast in response to rapidly altering market conditions and consumer behavior. Personalized customer interactions based on real-time data allow businesses to improve customer happiness and loyalty. Operations that are more cost-effective and efficient can be optimized using real-time data, including supply chain management.
    • Competitive Advantage: Organisations that can make use of real-time data have an advantage over rivals in terms of innovation and reactivity.
    • Data Integrity: Real-time processing helps businesses spot and resolve data integrity problems as they arise, resulting in accurate and trustworthy insights.

    Challenges and Things to Think About

    While real-time data warehousing has many advantages, there are drawbacks as well:

    • Complexity: Setting up and maintaining real-time data warehousing may be challenging and need a high level of technical competence.
    • Cost: Real-time data warehousing solutions can be expensive to build and operate, especially when dealing with large amounts of data.
    • Data Security: Sensitive data must be safeguarded throughout transmission and storage, which raises security issues with real-time data streaming.
    • Scalability: For on-premises solutions in particular, ensuring scalability and performance as data quantities increase can be a challenging task.


    The management and analysis of data in enterprises is changing as a result of real-time data warehousing. Organizations may make educated decisions in real time by integrating streaming data with conventional Data Warehouse Solutions, which improves customer experiences, operational effectiveness, and competitive advantages. Despite its obstacles, real-time data warehousing is becoming increasingly popular across industries and is a vital part of contemporary data management methods. Businesses that adopt real-time data warehousing will be better positioned to prosper in the digital era as the data landscape continues to change.

    For organizations looking for up-to-date insights, streaming data must be integrated into real-time data warehousing. The rapidly increasing amount of real-time data coming from sources like IoT devices and social media is too much for traditional data warehousing solutions, which are built for batch processing. 

    Businesses may obtain timely information, enable quicker decision-making, improve customer experiences, and maintain competitiveness in today’s fast-paced environment by embracing streaming data. Continuous data ingestion, real-time analytics, and quick reaction to shifting trends are all made possible by this change. In summary, the incorporation of streaming data into data warehousing enables businesses to fully utilize their data, spurring innovation and expansion.

    Date: October 9, 2023

    Author: James Warner 

    Source: Datafloq

  • Understanding and taking advantage of smart data distancing

    Understanding and taking advantage of smart data distancing

    The ongoing COVID-19 pandemic has made the term 'social distancing' a cynosure of our daily conversations. There have been guidelines issued, media campaigns run on prime time, hashtags created, and memes shared to highlight how social distancing can save lives. When you have young children talking about it, you know the message has cut across the cacophony! This might give data scientists a clue of what they can do to garner enterprise attention towards the importance of better data management.

    While many enterprises kickstart their data management projects with much fanfare, egregious data quality practices can hamper the effectiveness of these projects, leading to disastrous results. In a 2016 research study, IBM estimated that bad quality data costs the U.S. economy around $3.1 trillion dollars every year.

    And bad quality data affects the entire ecosystem; salespeople chase the wrong prospects, marketing campaigns do not reach the target segment, and delivery teams are busy cleaning up flawed projects. The good news is that it doesn’t have to be this way. The solution is 'smart data distancing'.

    What is smart data distancing?

    Smart data distancing is a crucial aspect of data danagement, more specifically, data governance for businesses to identify, create, maintain, and authenticate data assets to ensure they are devoid of data corruption or mishandling.

    The recent pandemic has forced governments and health experts to issue explicit guidelines on basic health etiquette; washing hands, using hand sanitizer, keeping social distance, etc. At times, even the most rudimentary facts need to be recapped multiple times so that they become accepted practices.

    Enterprises, too, should strongly emphasize the need for their data assets to be accountable, accurate, and consistent to reap the true benefits of data governance.

    The 7 do’s and don’ts of smart data distancing:

    1. Establish clear guidelines based on global best data management practices for the internal or external data lifecycle process. When accompanied by a good metadata management solution, which includes data profiling, classification, management, and organizing diverse enterprise data, this can vastly improve target marketing campaigns, customer service, and even new product development.

    2. Set up quarantine units for regular data cleansing or data scrubbing, matching, and standardization for all inbound and outbound data.

    3. Build centralized data asset management to optimize, refresh, and overcome data duplication issues for overall accuracy and consistency of data quality.

    4. Create data integrity standards using stringent constraint and trigger techniques. These techniques will impose restrictions against accidental damage to your data.

    5. Create periodic training programs for all data stakeholders on the right practices to gather and handle data assets and the need to maintain data accuracy and consistency. A data-driven culture will ensure the who, what, when, and where of your organization’s data and help bring transparency in complex processes.

    6. Don’t focus only on existing data that is readily available but also focus on the process of creating or capturing new and useful data. Responsive businesses create a successful data-driven culture that encompasses people, process, as well as technology.

    7. Don’t take your customer for granted. Always choose ethical data partners.

    How to navigate your way around third-party data

    The COVID-19 crisis has clearly highlighted how prevention is better than a cure. To this effect, the need to maintain safe and minimal human contact has been stressed immensely. Applying the same logic when enterprises rely on third-party data, the risks also increase manifold. Enterprises cannot ensure that a third-party data partner/vendor follows proper data quality processes and procedures.

    The questions that should keep your lights on at night are:

    • Will my third-party data partner disclose their data assessment and audit processes?
    • What are the risks involved, and how can they be best assessed, addressed, mitigated, and monitored?
    • Does my data partner have an adequate security response plan in case of a data breach?
    • Will a vendor agreement suffice in protecting my business interests?
    • Can an enterprise hold a third-party vendor accountable for data quality and data integrity lapses?  

    Smart data distancing for managing third-party data

    The third-party data risk landscape is complex. If the third-party’s data integrity is compromised, your organization stands to lose vital business data. However, here are a few steps you can take to protect your business:

    • Create a thorough information-sharing policy for protection against data leakage.
    • Streamline data dictionaries and metadata repositories to formulate a single cohesive data management policy that furthers the organization’s objectives.
    • Maintain quality of enterprise metadata to ensure its consistency across all organizational units to increase its trust value.
    • Integrate the linkage between business goals and the enterprise information running across the organization with the help of a robust metadata management system.
    • Schedule periodic training programs that emphasize the value of data integrity and its role in decision-making.

    The functional importance of a data steward in the data management and governance framework is often overlooked. The hallmark of a good data governance framework lies in how well the role of the data steward has been etched and fashioned within an organization. The data steward (or a custodian) determines the fitness levels of your data elements, the establishment of control, and the evaluation of vulnerabilities, and they remain on the frontline in managing any data breach. As a conduit between the IT and end-users, a data steward offers you a transparent overview of an organization’s critical data assets that can help you have nuanced conversations with your customers. 

    Unlock the benefits of smart data distancing

    Smart and unadulterated data is instrumental to the success of data governance. However, many enterprises often are content to just meet the bare minimum standards of compliance and regulation and tend to overlook the priority it deserves. Smart data means cleaner, high-quality data, which in turn means sharper analytics that directly translates to better decisions for better outcomes.

    Gartner says corporate data is valued at 20-25% of the enterprise value. Organizations should learn to monetize and use it wisely. Organizations can reap the benefits of the historical and current data that has been amassed over the years by harnessing and linking them to new business initiatives and projects. Data governance based on smart enterprise data will offer you the strategic competence to gain a competitive edge and improve operational efficiency.


    It is an accepted fact that an enterprise with poor data management will suffer an impact on its bottom line. Not having a properly defined data management framework can create regulatory compliance issues and impact business revenue.

    Enterprises are beginning to see the value of data in driving better outcomes and hence are rushing their efforts in setting up robust data governance initiatives. There are a lot of technology solutions and platforms available. Towards this endeavor, the first step for an enterprise is to develop a mindset of being data-driven and being receptive to a transformative culture.

     he objective is to ensure that the enterprise data serves the cross-functional business initiatives with insightful information, and for that to happen, the data needs to be accurate, meaningful, and trustworthy. Setting out to be a successful data-driven enterprise can be a daunting objective with a long transformational journey. Take a step in the right direction today with smart data distancing!

    Author: Sowmya Kandregula

    Source: Dataversity

  • What are data silos and why are they problematic?

    What are data silos and why are they problematic?

    Times are changing. We are breaking new thresholds in managing data. But getting rid of old habits is easier said than done. Data silos, an institutional phenomenon, are still mushrooming in today’s increasingly connected and shared world focused on accessibility. Top companies are now busy breaking down data silos to converge operations and experiences. Various factors help emerge data silos at enterprises, including technical, organizational, and cultural. In any case, they endanger data security severely. We will probe what data silos are, how they arise, and their risks for enterprises.

    What are data silos?

    Silos are a challenge for modern data policies. A data silo is a collection of data kept by one department that is not readily or fully accessible by other departments in the same organization. They occur because departments store the data they need in separate locations. These silos are often isolated from the rest of the organization and only accessible to a particular department and group.

    The number of data silos grows as the amount and diversity of an organization’s data assets increases. However, even though data silos sound like a practical approach adopted by departments with different goals, priorities, and budgets, they are not as innocent as they seem.

    Where do data silos come from?

    Data silos often occur in organizations without a well-planned data management strategy. But a department or user may establish its data silo even in a company with solid data management processes. However, data silos are most often the result of how an organization is structured and managed.

    Many businesses allow departments and business units to make their own IT purchases. This decision frequently results in databases and applications that aren’t compatible with or linked to other systems, resulting in data silos. Another ideal scenario for data silos is where business units are wholly decentralized and managed as separate entities. While this is often common in big enterprises with many divisions and operating companies, it can also occur in smaller organizations with a comparable structure and management technique.

    Company culture and principles can also cause the emergence of data silos. Company cultures where data sharing is not a norm and the organization lacks common goals and principles in data management, create data silos. Worse, departments may see their data as a valuable asset that they own and control in this culture, encouraging the formation of data silos.

    Ironically, success can also lead to silos if not managed well. That’s why data silos are typical in growing enterprises. Expanding organizations must rapidly meet new business needs and form additional business divisions. Both of those situations are common causes for data silo development. Mergers and acquisitions also bring silos into an organization, and some may stay very well hidden for a long time.

    What is the problem with data silos?

    Data silos jeopardize the overall management of how data is gathered and analyzed, putting organizations at greater risk of data breaches. There’s a higher danger that the information will be lost or otherwise damaged since employees would be keeping data on non-approved applications and devices.

    Siloed data frequently signals an isolated workplace and a corporate culture where divisions operate independently and no information is shared outside the department. Integrating corporate data can help bring down overly strict team structures where data isn’t shared and utilized to the company’s full potential.

    When there’s limited visibility across an organization, members of different teams can do the same work in parallel. A shared, transparent data culture can avoid wasting time and resources.

    When there are data silos, you may confuse permissions and information access hierarchy. The level and type of security provided might vary, depending on the silo. This can create a significant lag factor when benchmarking data or constructing a longitudinal study that revisits past material or incorporates data from various company sections. It jeopardizes productivity and lowers the return on investment for projects.

    Silos can cause difficulties for data analysis since the data might be kept in non-compliant formats. Before any valuable insights may be obtained from it, standardizing the data and converting it into new interoperable formats is a time-consuming manual process.

    The financial cost of silos is determined by the organization’s size, the effectiveness of its efforts to eliminate them, and whether they continue to develop. The most apparent cost is increased IT and data management expenditures.

    How to dismantle data silos?

    While data silos are easy to spot in small companies, it can be challenging to understand the number and full impact of data silos in large organizations. A brief survey sent to important data stakeholders throughout the organization might help identify siloes at their source.

    Although cultural habits or hierarchical HR structures sometimes cause data silos, the technology an IT department employs might also contribute. Many existing systems may not be set up for data sharing or compatible with modern formats, and technological solutions might differ depending on departments. The key is to bring your data on a contemporary platform for sharing and collaboration via a simple interface. This may be a long-term initiative rather than a short-term fix, but it could pay off as an organization expands.

    A great example of handling data from various data silos

    For a long term fix, polypoly MultiBrand comes to your help. Let’s take customer data management as an example.

    Today, companies have multiple touchpoints with customers. From all these points, channels and various sources, lots and lots of data flow. Data-privacy regulations such as GDPR prevents the group-wide customer journey from being recorded. This leaves companies in a one-way street, where they create maintenance-intensive and costly data silos, which is also a common problem for companies that own multiple brands.

    What would you think if I told you that the companies can dismantle these data silos with the help of their customers?

    By using the polyPod, a Super App infrastructure, this is possible. Let me explain to you step by step.

    • Companies provide their customers the polyPod app.
    • Users download their data from various data silos to their device. Thus, a detailed data set for this user is created across departmental and corporate boundaries. On the other hand, integrated consent management helps the user have more control over personal data.
    • The company creates an incentive for the customers, encouraging them to add data from platforms such as social media, and correct their own data.
    • This way, a sloppy data silo can turn into well-structred data, creating savings which then the company can pass on in parts as incentives or extra benefits.

    By using polyPod app, companies can have additional benefits, like big data analyzing power from the same end devices its customers use. The app’s resource sharing function allows the consented party to use these resources for computing, which in turn lowers data intermediary and data center costs. The final benefit is the increased customer satisfaction due to transparency and data privacy.

    Author: Thorsten Dittmar

    Source: Dataconomy

  • What exactly is a Data Fabric? Definitions and uses

    What exactly is a Data Fabric? Definitions and uses

    A Data Fabric “Is a distributed Data Management platform whose objective is to combine various types of data storage, access, preparation, analytics, and security tools in a fully compliant manner to support seamless Data Management.” His concept has gained traction as technologies, such as the Internet of Things, need to have a consistent way of making data available to specific workloads or applications. It is key for retrieving data across multiple locations spanning the globe, since many companies use a variety of storage system configurations and cloud providers.

    Other definitions of a Data Fabric Include:

    • “A solution to the phenomenon where datasets get so large that they become physically impossible to move.” (Will Ochandarena)
    • “A comprehensive way to integrate all an organization’s data into a single, scalable platform.” (MAPR)
    •  “An enabler of frictionless access of data sharing in a distributed data environment.” (Gartner)
    • “An information network implemented on a grand scale across physical and virtual boundaries – focus on the data aspect of cloud computing as the unifying factor.” (Forbes)
    • A design allowing for “a single, consistent data management framework, allowing easier data access and sharing in a distributed environment” (TechRepublic)

    Businesses use a Data Fabric to:

    • Handle very large data sets across multiple locations quicker.
    • Make data more accessible
    • Optimize the entire data lifecycle– to enable applications that require real-time analytics.
    • Integrate data silos across an environment
    • Deliver a higher value from data assets
    • Allow machine learning and AI to work more efficiently

    Author: Michelle Knight

    Source: Dataversity

  • What to expect in data management? 5 trends  

    What to expect in data management? 5 trends

    We all know the world is changing in profound ways. In the last few years, we’ve seen businesses, teams, and people all adapting — showing incredible resilience to keep moving forward despite the headwinds.  

    To shed some light on what to expect in 2022 and beyond, let’s look at five major trends with regard to data. We’ve been watching these particular data trends since before the pandemic and seen them gain steam across sectors in the post-pandemic world.  

    Trend 1: Accelerated move to the cloud(s) 

    We’ve seen a rush of movement to the cloud in recent years. Organizations are no longer evaluating whether or not cloud data management will help them; they’re evaluating how to do it. They are charting their way to the cloud via cloud data warehouses, cloud data lakes, and cloud data ecosystems.  

    What’s driving the move to the cloud(s)? 

    On-prem hardware comes with steep infrastructure costs: database administrators, data engineering costs, flow sweeps, and management of on-prem infrastructure itself. In a post-pandemic world, that’s all unnecessarily cumbersome. Organizations that move their databases and applications to the cloud reap significant benefits in cost optimization and productivity. 

    What to know about moving to the cloud(s) in 2022: 

    Note that I’m saying “cloud(s)” for a reason: the vast majority of organizations opt for multi-cloud and hybrid cloud solutions. Why? To avoid putting all their data eggs in one cloud basket.  

    While cloud data management services make it easy to move the data to their cloud, they also make it easiest to stay in their cloud — and sometimes downright hard to move data from it. Remember, a cloud vendor is typically aiming to achieve a closed system where you’ll use their products for all your cloud needs. But if you rely on a single provider in that way, a service change or price increase could catch you off-guard.  
    To stay flexible, many organizations are using best-fit capabilities of multiple cloud providers; for example, one cloud service for data science and another for applications. Integrating data across a multi-cloud or hybrid ecosystem like this helps organizations maintain the flexibility to manage their data independently.  

    Trend 2: Augmented or automated data management 

    Every organization relies on data — even those without an army of data engineers or data scientists. It’s very important for organizations of any size to be able to implement data management capabilities.  

    According to Gartner, “data integration (49%) and data preparation (37%) are among the top three technologies that organizations would like to automate by the end of 2022." 

    What’s driving the shift to augmented or automated data management? 

    Data management has traditionally taken a lot of manual effort. Data pipelines, especially hand-coded ones, can be brittle. They may break for all kinds of reasons: schema drifts when there are changes between source and target schema; applications that get turned off; databases that go out of sync; or network connectivity problems. Those failures can bring a business to a halt — not to mention that they are time-consuming and expensive to track down and fix.  

    Automating data management also frees up engineering resources. Gartner also says that by 2023, AI-enabled automation in data management and integration will reduce the need for IT specialists by 20%. 

    What to know about data management in 2022: 

    By tapping into data services, even small and under-resourced data teams can implement data management and integration — by automating pipelines, quality, and governance on demand. Automation supports flexible pipeline creation, management, and retirement, granting organizations of any size or stage of growth the data observability they need in a continuous integration, continuous deployment (CICD) environment. 

    Trend 3: Metadata management 

    Since metadata is the glue that holds necessary data management pieces together, it’s no wonder that organizations are aiming to improve their handle on it.  

    As different lines of business develop their own shadow IT, the ecosystem grows in complexity: many companies end up buying multiple solutions and tools and then often need to pay consultants to make them work together.  

    What’s driving interest in metadata management? 

    Business agility is a requirement in today’s chaotic business landscape, which creates enormous demand for analytics. Healthy data is now a must-have for users with varied levels of technical skill. It’s impossible to expect them to become data analysts and engineers overnight in order to find, share, clean, and use the data they need.  

    What to know about metadata management in 2022: 

    Many companies have multiple data integration tools, quality tools, databases, governance tools, and so on. As data ecosystems become increasingly complex, it’s more important than ever that all those tools can speak to each other. Applications must support bi-directional data exchange. According to Gartner, data fabric architecture is key to modernizing data management. It’s the secret sauce that allows people with different skill sets — like data experts in the business and highly skilled developers in IT — to work together to create data solutions. 

    Trend 4: Real-time data access  

    Real-time data is no longer a nice-to-have; it is vital to operations ranging from manufacturing to utilities to retail customer experience. In addition, every company needs operational intelligence.  

    Any time an event is created, you should be able to provide that event in real time to support real-time analytics. 

    What’s driving interest in real-time data access? 

    We haven’t just seen the arrival of the Internet of Things (IoT) and Industrial Internet of Things (IIoT) — businesses are now reliant on them. In a world fueled by real-time data, batch integration and bulk integration are no longer enough to keep up.  

    What to know about real-time data access in 2022: 

    Extract Transfer Load (ETL) has to be supported by other integration styles including streaming data integration to capture event streams from the logs, sensors, and events that power your business. Make sure you’re building an architecture that supports both batch streaming in real time, and also virtual data access such as data replication and change data capture. That way you won’t have to move the data when you don’t want to.   

    Trend 5: Line of business ownership of data 

    Data is no longer tightly controlled in the back end by a central IT or data organization. In more and more businesses, the organization reporting to a CDO or CIO focuses on governance and compliance while business users process data within their own lines of business.  

    What’s driving line of business ownership of data? 

    As data becomes the language of business, we’re seeing the proliferation of citizen data scientists, citizen data integrators, citizen engineers, citizen analysts, and more.  

    What to know about line of business ownership of data in 2022: 

    Low-code and no-code data preparation and self-service data integration tools equip data users on the front end to ingest, prepare, and model the data for their business needs. These new “citizen” data workers are business experts who don’t have a PhD in statistics or engineering. They don’t know R, Python, Scala, Java, C Sharp, or Spark — and they shouldn’t have to. On the other hand, decentralizing data management can create data governance, compliance, and security headaches.  

    As more and more data software sits with the line of business, organizations should look for a data fabric that will enable central data engineering teams to monitor what the data preparation teams prepare. That way, data experts can improve data governance and compliance while lines of business maintain ownership of the data itself.   

    Author: Jamie Fiorda

    Source: Talend

  • Why cloud solutions are the way to go when dealing with global data management

    Why cloud solutions are the way to go when dealing with global data management

    To manage geographically distributed data at scale worldwide, global organizations are turning to cloud and hybrid deployments.

    Enterprises that operate worldwide typically need to manage data both on the local level and globally across all geographies. Local business units and subsidiaries must address region-specific data standards, national regulations, accounting standards, unique customer requirements, and market drivers. At the same time, corporate headquarters must share data broadly and maintain a complete view of performance for the whole multinational enterprise.

    Furthermore, in many multinational firms, data is the business. In worldwide e-commerce, travel services, logistics, and international finance for example. So it behooves each company to have state-of-the-art data management to remain innovative and competitive. These same organizations must also govern data locally and globally to comply with many legislated regulations, privacy policies, security measures, and data standards. Hence, global businesses are facing a long list of new business and technical requirements for modern data management in multinational markets.

    For maximum business value, how do you manage and govern data that resides on multiple premises, clouds, applications, and data platforms (literally) worldwide? Global data management based on cloud and hybrid deployments is how.

    Defining global data management in the cloud

    The distinguishing characteristic of global data management is its ever-broadening scope, which has numerous drivers and consequences:

    Multiple physical premises, each with unique IT systems and data assets. Multinational firms consist of geographically dispersed departments, business units, and subsidiaries that may integrate data with clients and partners. All these entities and their applications generate and use data with varying degrees of data sharing.

    Multiple clouds and cloud-based tools or platforms. In recent years, organizations of all sizes have aggressively modernized and extended their IT portfolios of operational applications. Although on-premises applications will be with us into the foreseeable future, organizations increasingly prefer cloud-based applications, licensed and deployed on the software-as-a-service (SaaS) model. Similarly, when organizations develop their own applications (which is the preferred approach with data-driven use cases, such as data warehousing and analytics), the trend is away from on-premises computing platforms in favor of cloud-based ones from Amazon, Google, Microsoft, and others. Hybrid IT and data management environments result from the mix of systems and data that exist both on premises and in the cloud.

    Extremely diverse data with equally diverse management requirements. Data in global organizations is certainly big, but it is also diverse in terms of its schema, latencies, containers, and domains. The leading driver of data diversity is the arrival of new data sources, including SaaS applications, social media, the Internet of Things (IoT), and recently digitized business functions such as the online supply chain and marketing channels. On the one hand, data is diversifying. On the other hand, global organizations are also diversifying the use cases that demand large volumes of integrated and repurposed data, ranging from advanced analytics to real-time business management.

    Multiple platforms and tools to address diverse global data requirements. Given the diversity of data that global organizations manage, it is impossible to optimize one platform (or a short list of platforms) to meet all data requirements. Diverse data needs diverse data platforms. This is one reason global firms are leaders in adopting new computing platforms (clouds, on-premises clusters) and new data platforms (cloud DBMSs, Hadoop, NoSQL).

    The point of global data management in the cloud

    The right data is captured, stored, processed, and presented in the right way. An eclectic portfolio of data platforms and tools (managing extremely diverse data in support of diverse use cases) can lead to highly complex deployments where multiple platforms must interoperate at scale with high performance. Users embrace the complexity and succeed with it because the eclectic portfolio gives them numerous options for capturing, storing, processing, and presenting data in ways that a smaller and simpler portfolio cannot satisfy.

    Depend on the cloud to achieve the key goals of global data management. For example, global data can scale via unlimited cloud storage, which is a key data requirement for multinational firms and other very large organizations with terabyte- and petabyte-scale data assets. Similarly, clouds are known to assure high performance via elastic resource management; adopting a uniform cloud infrastructure worldwide can help create consistent performance for most users and applications across geographies. In addition, global organizations tell TDWI that they consider the cloud a 'neutral Switzerland' that sets proper expectations for shared data assets and open access. This, in turn, fosters the intraenterprise and interenterprise communication and collaboration that global organizations require for daily operations and innovation.

    Cloud has general benefits that contribute to global data management. Regardless of how global your organization is, it can benefit from the low administrative costs of a cloud platform due to the minimal system integration, capacity planning, and performance tweaking required of cloud deployments. Similarly, a cloud platform alleviates the need for capital spending, so up-front investments are not an impediment to entry. Furthermore, most public cloud providers have an established track record for security, data protection, and high availability as well as support for microservices and managed services.

    Strive to thrive, not merely survive. Let’s not forget the obvious. Where data exists, it must be managed properly in the context of specific business processes. In other words, global organizations have little choice but to step up to the scale, speed, diversity, complexity, and sophistication of global data management. Likewise, cloud is an obvious and viable platform for achieving these demanding goals. Even so, global data management should not be about merely surviving global data. It should also be about thriving as a global organization by leveraging global data for innovative use cases in analytics, operations, compliance, and communications across organizational boundaries.

    Author: Philip Russom

    Source: TDWI

  • Why data management is key to retailers in times of the pandemic

    Why data management is key to retailers in times of the pandemic

    Jamie Kiser, COO and CCO at Talend, explains why retailers, striving to ensure they’re not missing out on future opportunities, must leverage one thing: data. By utilizing customer intelligence and better data management, retailers can collect supply chain data in real-time, make better orders to suppliers based on customer intelligence.

    While major industries from tech to the public sector felt COVID’s pain points, not many felt them as acutely as retail. Retailers must now contend with everything from unreliable supply chains to a limit on the number of customers in-store at any given time, as consumer behavior shifted with social distancing guidelines and new needs.

    For example, e-commerce grew by 44% in 2020. As we begin recovering from the pandemic, retailers increasingly push to deliver their customers an in-store shopping experience as seamless as it is online. However, these new digital strategies rely on precise inventory management, which remains a pain point for many brick-and-mortar stores.

    For retailers to ensure they’re not missing out on future opportunities, they need to leverage one thing: data. By utilizing customer intelligence and better data management, retailers can collect supply chain data in real-time, make better orders to suppliers based on customer intelligence. Data will help retailers fully integrate their supply chain, customer, and in-store data to ensure they’re creating an in-store experience that’s competitive to shopping online and other new shopping behaviors.

    Eyes on the supply (chain)

    The pandemic has revealed the fragility of the supply chain. With unprecedented unpredictability in what products stores will and will not have access to and when retailers need to integrate real-time data management into their online operations. Investing in supply chain data strategies enables retailers to adapt and adjust to sudden breakdowns from their suppliers.

    Like Wayfair and Dick’s Sporting Goods, some companies leverage real-time inventory data to make their supply chain transparent to their customers, so they are always up to date on what is and isn’t on the shelves. Investing in data management tools to collect supply chain data in real-time empowers retailers to create better customer experiences and saves stores the estimated billions in lost sales to customers discovering their desired item is out of stock.

    However, supply chain erosion is not the only supply problem retailers will have to overcome. Some issues facing supply and inventory come from consumers, like last March’s run on toilet paper or the PS5 selling out before they even made it to shelves. Seemingly instantaneous changes in customer behavior can instantly impact what items retailers are prioritizing in orders from suppliers. But without understanding customer behavior, retailers can overcorrect and wind up with dead inventory on their hands.

    Customer analytics influence orders

    To avoid collecting dead inventory and ensure orders to suppliers are accurate to their customers’ desires, retailers need to integrate customer intelligence with their supply chain information systems. Combining this information empowers retailers to place orders from suppliers based on precise predictive models of customer behavior. This way, retailers will keep up with rapid consumer behavior changes and keep supply chains up to speed with the latest trends in brick and mortar shopping.

    For example, buy online, pick up in-store (BOPIS) shopping experiences have been a growing trend among retailers and consumers the past few years, and this trend shows no sign of slowing down. One survey found BOPIS sales grew by 208% in 2020. But BOPIS is not the only trend growing during the pandemic. It has also accelerated research online, purchase offline (ROPO). Finding success in both BOPIS and ROPO is entirely contingent on understanding what’s on the shelf, what suppliers bring in, and which items are unpopular and creating dead inventory. By collecting specific customer intelligence, such as the products customers are researching, retailers can build predictive models for when online research turns into an in-store sale.

    Leveraging customer intelligence not only helps brick and mortar retailers keep shelves stocked with the products their customers wish to purchase but can also be integrated with supply chain data to optimize operations. Investment in data management and integration can positively impact retailers’ profits by allowing them to make purchasing decisions from suppliers based on supplier circumstances and customer demand. Pooling data from both suppliers and customers into a single source of truth gives retailers the ability to operate under intelligent predictive business models. It also will prevent direct profit loss to competitors. Research shows an estimated $36.3 billion is lost to brick-and-mortar competition annually to customers purchasing elsewhere upon discovering their desired item is out of stock.

    An integrated approach to supply chain management

    Integrating real-time supply chain data with customer intelligence can prevent customer walkouts and increase profits by mitigating the risks to the supply chain created by the pandemic. However, when this external data combines with internal data — like sales, restocking times, demand surges, and more – brick and mortar retailers can position their supply chain to be in sync with the real-time shopping occurring online and in-store. Better business intelligence and supply chain data management empower retailers to offer customers a competitive experience to find online. Doing this requires a robust data management system and a business-wide data strategy that integrates data across all verticals.

    For example, European clothing retailer Tape à l’oeil, replaced an aging ERP system with a new SAP and Snowflake-based infrastructure to better capture digital traffic data as they made operations digital due to the pandemic. This addition to their existing platform allows Tape à l’oeil to capture customer feedback through surveys to capture satisfaction with a new collection. Now digital campaign results are easily retrieved from Facebook and Instagram to cross-reference them in Snowflake and share comprehensive reports with management. Making data the heart of their business strategy.

    This new data strategy has allowed Tape à l’oeil to find success during these tumultuous times by integrating customer data into predictive models to help them act faster and mitigate risks in their supply chain. Tape à l’oei’s CIO said that leveraging data has allowed them to improve operations overall and give them the “agility” to react to disruptions in the supply chain swiftly.

    The brick and mortar way forward

    A year into the pandemic and the retail industry remains in an ever precarious state. However, consumer trends show there are still growth opportunities for the brick-and-mortar stores prepared to meet customer demand.

    Making data management an integral part of retail operations will help companies meet the supply chain challenges presented by COVID-19 and empower them to keep their business growing.

    Author: Jamie Kiser

    Source: Talend

  • Will the battle on data between Business and IT be ended?


    Business users have growing customer expectations, changing market dynamics, increasing competition, and evolving regulatory conditions to deal with. These factors compound the pressure on business decision makers to act now. Unfortunately, they often can’t get the data they need when they need it.

    Research shows that business managers often have to make data-driven decisions within one day. However, the time to build a single report using traditional BI methods can take six weeks or longer and a typical business intelligence deployment can take up to 18 mobusiness and ITnths.

    On the IT side, teams are feeling the pressure. They have a long list of items to do for the short run and long run. Regarding data management, IT has to try to combine data from multiple sources, ensure that data is secure and accurate, and deliver the data to the business user as requested.

    Given the need for “data now,” in relation to the bandwidth concerns placed on IT, many organizations find that their enterprise lacks the skills, technology, and support to use their corporate data to keep up with competitors, customer needs, and the marketplace.

    Adding to this existing challenge is the notion that companies are continuously adding new data sources, but each new data integration can take weeks or even months. By the time the work is complete, it’s likely that a newer, better source has already taken its place.

    Automation is a force that is driving change throughout the entire BI stack. Just look at the proliferation of self-service data visualization tools. But self-service analytics can quickly go awry without adequate governance.

    Companies that can integrate self-service BI and still maintain governance, security, and data quality will empower business users to make decisions on-demand, while relieving IT from these internal stakeholder pressures.

    Having the ability to store data in a place or a hub where it can be cleansed, reconciled, and made available as a consistent resource, on demand resource to business users can help solve the issue.

    When quality issues arise, or bad data is found, the error can be corrected once in the hub for all users – resulting in one single source of the truth. It is a place where data quality and consistency are maintained. This central repository enables the right person to have access to the right data at the right time.

    Business executives, managers, and frontline users in operations want the power to move beyond the limits of spreadsheets so that they can engage in deeper analysis by leveraging data insights to strengthen all types of decision needs. Today, newer tools and methods are making it possible for organizations to meet the demands of nontechnical users by enabling them to access, integrate, transform, and visualize data without traditional IT handholding.

    The age of self-service demands that business users have full and flexible access to their data. It also demands that business users be the ones who determine that data should be included in the system. And while business users need the expert help of IT to ensure the quality, consistency, and contextual validity of the data, business and IT can now work together more closely and more easily than ever before.

    Organizations can effectively “democratize” data by addressing the needs of nontechnical users including business executives, managers, and frontline users. This can transpire If they grant more power to those users, not just in terms of access and discovery, but also in terms of sourcing what goes into a central hub.

    In the end, giving more power to the people is one surefire way to help end the battle between business and IT.

    Author: Heine Krog Iversen

    source: Information management

EasyTagCloud v2.8