4 items tagged "data governance"

  • BI topics to tackle when migrating to the cloud

    BI topics to tackle when migrating to the cloud

    When your organization decides to pull the trigger on a cloud migration, a lot of stuff will start happening all at once. Regardless of how long the planning process has been, once data starts being relocated, a variety of competing factors that have all been theoretical earlier become devastatingly real: frontline business users still want to be able to run analyses while the migration is happening, your data engineers are concerned with the switch from whatever database you were using before, and the development org has its own data needs. With a comprehensive, BI-focused data strategy, you and your stakeholders will know what your ideal data model should look like once all your data is moved over. This way, as you’re managing the process and trying to keep everyone happy, you end in a stronger place when your migration is over than you were at the start, and isn’t that the goal?

    BI focus and your data infrastructure

    “What does all this have to do with my data model?” you might be wondering. “And for that matter, my BI solution?”

    I’m glad you asked, internet stranger. The answer is everything. Your data infrastructure underpins your data model and powers all of your business-critical IT systems. The form it takes can have immense ramifications for your organization, your product, and the new things you want to do with it. Your data infrastructure is hooked into your BI solution via connectors, so it’ll work no matter where the data is stored. Picking the right data model, once all your data is in its new home, is the final piece that will allow you to get the most out of it with your BI solution. If you don’t have a BI solution, the perfect time to implement one is once all your data is moved over and your model is built. This should all be part of your organization’s holistic cloud strategy, with buy-in from major partners who are handling the migration.

    Picking the right database model for you

    So you’re giving your data a new home and maybe implementing a BI solution when it’s all done. Now, what database model is right for your company and your use case? There are a wide array of ways to organize data, depending on what you want to do with it.

    One of the broadest is a conceptual model, which focuses on representing the objects that matter most to the business and the relationships between them. This database model is designed principally for business users. Compare this to a physical model, which is all about the structure of the data. In this model, you’ll be dealing with tables, columns, relationships, graphs, etc. And foreign keys, which distinguish the connections between the tables.

    Now, let’s say you’re only focused on representing your data organization and architecture graphically, putting aside the physical usage or database management framework. In cases like these, a logical model could be the way to go. Examples of these types of databases include relational (dealing with data as tables or relations), network (putting data in the form of records), and hierarchical (which is a progressive tree-type structure, with each branch of the tree showing related records). These models all feature a high degree of standardization and cover all entities in the dataset and the relationships between them.

    Got a wide array of different objects and types of data to deal with? Consider an object-oriented database model, sometimes called a “hybrid model.” These models look at their contained data as a collection of reusable software pieces, all with related features. They also consolidate tables but aren’t limited to the tables, giving you freedom when dealing with lots of varied data. You can use this kind of model for multimedia items you can’t put in a relational database or to create a hypertext database to connect to another object and sort out divergent information.

    Lastly, we can’t help but mention the star schema here, which has elements arranged around a central core and looks like an asterisk. This model is great for querying informational indexes as part of a larger data pool. It’s used to dig up insights for business users, OLAP cubes, analytics apps, and ad-hoc analyses. It’s a simple, yet powerful, structure that sees a lot of usage, despite its simplicity.

    Now what?

    Whether you’re building awesome analytics into your app or empowering in-house users to get more out of your data, knowing what you’re doing with your data is key to maintaining the right models. Once you’ve picked your database, it’s time to pick your data model, with an eye towards what you want to do with it once it’s hooked into your BI solution.

    Worried about losing customers? A predictive churn model can help you get ahead of the curve by putting time and attention into relationships that are at risk of going sour. On the other side of the coin, predictive up- and cross-sell models can show you where you can get more money out of a customer and which ones are ripe to deepen your financial relationship.

    What about your marketing efforts? A customer segmentation data model can help you understand the buying behaviors of your current customers and target groups and which marketing plays are having the desired effect. Or go beyond marketing with “next-best-action models” that take into account life events, purchasing behaviors, social media, and anything else you can get your hands on so that you can figure out what’s the next action with a given target (email, ads, phone call, etc.) to have the greatest impact. And predictive analyses aren’t just for humancentric activities, manufacturing and logistics companies can take advantage of maintenance models that can let you circumvent machine breakdowns based on historical data. Don’t get caught without a vital piece of equipment again.

    Bringing it all together with BI

    Staying focused on your long-term goals is an important key to success. Whether you’re building a game-changing product or rebuilding your data model, having a well defined goal makes all the difference in the world when it comes to the success of your enterprise. If you’re already migrating your data to the cloud, then you’re at the perfect juncture to pick the right database and data models for your eventual use cases. Once these are set up, they’ll integrate seamlessly with your BI tool (and if you don’t have one yet, it’ll be the perfect time to implement one). Big moves like this represent big challenges, but also big opportunities to make lay the foundation for whatever you’re planning on building. Then you just have to build it!

    Author: Jack Cieslak

    Source: Sisense

  • BI trends: What to expect for retail in 2019?

    BI trends: What to expect in retail in 2019?

    To help retailers and brands plan for 2019, Researcher Claudia Tajima and Fiona Swerdlow are interviewing experts within Forrester for their series, ´Applying 2019 Predictions To Retail´. This week, Claudia interviewed Jennifer Belissent, Ph.D. and principal analyst on Forrester’s consumer insights team, on their 2019 BI predictions report. Here’s what Jennifer thinks retailers and brands can expect and should focus on regarding BI for the rest of 2019.

    Claudia:Your first BI prediction for 2019 states that companies cannot be successful simply selling raw data and that self-service data marketplaces will struggle. BI tools need to start delivering data insights and services. How does this shift affect retailers?

    Jennifer: For retailers today, there is a vast amount of data that you could use to improve business and better understand your customers. Many retailers already use their data to understand their customers and forecast trends. But today is a rapidly evolving landscape of new, alternative data sources. Opportunities to enrich data with new sources are appealing. However, retailers must evaluate those opportunities carefully. Why? The time to value is longer when buying raw data instead of buying data insights. For example, buying a customer’s credit score would be faster time to value than buying the raw customer data to ultimately find their credit score. My recommendation is that retailers should not rush to buy data or expect to be able to buy data from a marketplace and get all the answers they’re searching for. In some cases, retailers need insights service providers to interpret this data.

    Claudia:How will the demand for data storytelling skills impact retailers’ talent acquisition strategies?

    Jennifer: This demand exists because there is a gap between technology users and data scientists. They don’t always speak the same language, but a storyteller can bridge the gap. Organizations need a storyteller who can talk to the business team, data team, and the technology team and help them reach a common understanding. This balance is critical for BI teams to be able to both organize data and deliver the data in a compelling way. Forrester’s research suggests that more mature companies, those that are more ´insights-driven´, have these skills.

    Claudia:Organizations are predicted to abandon unactionable BI reporting and dashboards. How should retailers respond to growing derelict dashboard graveyards?

    Jennifer: Retailers are seeking answers to questions such as: How does one store compare to others? How does it compare to regional sales? However, retailer leaders’ interest in specific reports or dashboards eventually goes down over time. Creating a data center of excellence increases more data awareness, but it also brings about a frenzy of requests for new dashboards and reports. Ultimately, many of these requests end up as orphaned dashboards. It is important for retailers to be careful of how they embrace data democratization. Take time to step back and rationalize, prioritize, and determine which data from reports and dashboards you need and don’t need.

    Claudia:Why should retailers consider adopting data fabrics in place of data lakes?

    Jennifer: In the past, many organizations chose to put their data into massive data lakes. However, these organizations did not fully think through how their data lakes should be organized and used. Today, organizations are starting to realize that there is no major benefit to putting all of their data into one centralized data lake. The new trend is to create a data fabric of woven data from across different parts of the organization that sits somewhere central. Data fabric stores maintain their own individual data, but there is a central data point where it can all be accessed.

    Claudia:What recommendations would you give to retail leaders looking into investing in BI tools in the coming 12 to 18 months?

    Jennifer: Data catalogues, which serve as a knowledge repository, are becoming very popular. Organizations typically have one centralized data catalogue. Interesting data catalogue outputs include: use cases, algorithms, as well as which reports the data has been used for and where has it been tested in sales. In terms of ambient data governance tools, retailers should look for BI tools that have data governance built directly into them.

    Author: Fiona Swerdlow

    Source: Forrester

  • How to create a trusted data environment in 3 essential steps

    How to create a trusted data environment in 3 essential steps

    We are in the era of the information economy. Nowadays, more than ever, companies have the capabilities to optimize their processes through the use of data and analytics. While there are endless possibilities wjen it comes to data analysis, there are still challenges with maintaining, integrating, and cleaning data to ensure that it will empower people to take decisions.

    Bottom up, top down? What is the best?

    As IT teams begin to tackle the data deluge, a question often asked is: should this problem be approached from the bottom up or top down? There is no “one-size-fits-all” answer here, but all data teams need a high-level view to help you get a quick view of your data subject areas. Think of this high-level view as a map you create to define priorities and identify problem areas for your business within the modern day data-based economy. This map will allow you to set up a phased approach to optimize your most value contributing data assets.

    The high-level view unfortunately is not enough to turn your data into valuable assets. You also need to know the details of your data.

    Getting the details from your data is where a data profile comes into play. This profile tells you what your data is from the technical perspective. The high-level view (the enterprise information model), gives you the view from the business perspective. Real business value comes from the combination of both views. A transversal, holistic view on your data assets, allowing to zoom in or zoom out. The high-level view with technical details (even without the profiling) allows to start with the most important phase in the digital transformation: Discovery of your data assets.

    Not only data integration, but data integrity

    With all the data travelling around in different types and sizes, integrating the data streams across various partners, apps and sources have become critical. But it’s more complex than ever.

    Due to the sizes and variety of data being generated, not to mention the ever-increasing speed in go to market scenarios, companies should look for technology partners that can help them achieve this integration and integrity, either on premise or in the cloud.

    Your 3 step plan to trusted data

    Step 1: Discover and cleanse your data

    A recent IDC study found that only 19% of a data professional’s time is spent analyzing information and delivering valuable business outcomes. They spend 37% of their time preparing data and 24% of their time goes to protecting data. The challenge is to overcome these obstacles by bringing clarity, transparency, and accessibility to your data assets.

    Building this discovery platform, which at the same time allows you to profile your data, to understand the quality of your data and build a confidence score to build trust with the business using the data assets, comes under the form of an auto-profiling data catalog.

    Thanks to the application of Artificial Intelligence (AI) and Machine Learning (ML) in the data catalogs, data profiling can be provided as self-service towards power users.

    Bringing transparency, understanding, and trust to the business brings out the value of the data assets.

    Step 2: Organize data you can trust and empower people

    According to the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms, 2017: “By 2020, organizations that offer users access to a curated catalog of internal and external data will realize twice the business value from analytics investments than those that do not.”

    An important phase in a successful data governance framework is establishing a single point of trust. From the technical perspective this translates to collecting all the data sets together in a single point of control. The governance aspect is the capability to assign roles and responsibilities directly in the central point of control, which allows to instantly operationalize your governance from the place the data originates.

    The organization of your data assets goes along with the business understanding of the data, transparency and provenance. The end to end view of your data lineage ensures compliance and risk mitigation.

    With the central compass in place and the roles and responsibilities assigned, it’s time to empower the people for data curation and remediation, in which an ongoing communication is from vital importance for adoption of a data driven strategy.

    Step 3: Automate your data pipelines & enable data access

    Different layers and technologies make our lives more complex. It is important to keep our data flows and streams aligned and adopt to swift and quick changes in business needs.

    The needed transitions, data quality profiling and reporting can extensively be automated.

    Start small and scale big. A part of intelligence these days can be achieved by applying AI and ML. These algorithms can take the cumbersome work out of the hands of analysts and can also be better and easier scaled. This automation gives the analysts faster understanding of the data and build better faster and more insights in a given time.

    Putting data at the center of everything, implementing automation and provisioning it through one single platform is one of the key success factors in your digital transformation and become a real data-driven organization.

    Source: Talend

  • On-premise or cloud-based? A guide to appropriate data governance

    On-premise or cloud-based? A guide to appropriate data governance

    Data governance involves developing strategies and practices to ensure high-quality data throughout its lifecycle.

    However, besides deciding how to manage data governance, you must choose whether to apply the respective principles in an on-premise setting or the cloud.

    Here are four pointers to help:

    1. Choose on-premise when third-party misconduct is a prevalent concern

    One of the goals of data governance is to determine the best ways to keep data safe. That's why data safety comes into the picture when people choose cloud-based or on-premise solutions. If your company holds sensitive data like health information and you're worried about a third-party not abiding by your data governance policies, an on-premise solution could be right for you.

    Third-party cloud providers must abide by regulations for storing health data, but they still make mistakes. Some companies offer tools that let you determine a cloud company's level of risk and see the safeguards it has in place to prevent data breaches. You may consider using one of those to assess whether third-party misconduct is a valid concern as you strive to maintain data governance best practices.

    One thing to keep in mind is that the shortcomings of third-party companies could cause long-term damage for your company's reputation. For example, in a case where a cloud provider has a misconfigured server that allows a data breach to happen, they're to blame. But, the headlines about the incident will likely primarily feature your brand and may only mention the outside company in a passing sentence.

    If you opt for on-premise data governance, your company alone is in the spotlight if something goes wrong, but it's also possible to exert more control over all facets of data governance to promote consistency. When you need scalability, cloud-based technology typically allows you to ramp up faster, but you shouldn't do that at the expense of a possible third-party blunder.

    2. Select cloud-based data governance if you lack data governance maturity

    Implementing a data governance program is a time-consuming but worthwhile process. A data governance maturity assessment model can be useful for seeing how your company's approach to data governance stacks up to industry-wide best practices. It can also identify gaps to illuminate what has to happen for ongoing progress to occur.

    Using a data governance maturity assessment model can also signal to stakeholders that data governance is a priority within your organization. However, if your assessments show the company has a long way to go before it can adhere to best practices, cloud-based data governance could be the right choice.

    That's because the leading cloud providers have their own in-house data governance strategies in place. They shouldn't replace the ones used in-house at your company, but they could help you fill in the known gaps while improving company-wide data governance.

    3. Go with on-premise if you want ownership

    One of the things that companies often don't like about using a cloud provider for data governance is that they don't have ownership of the software. Instead, they usually enter into a leasing agreement, similarly to leasing an automobile. So, if you want complete control over the software used to manage your data, on-premise is the only possibility which allows that ownership.

    One thing to keep in mind about on-premise data governance is that you are responsible for data security. As such, you must have protocols in place to keep your software updated against the latest security threats.

    Cloud providers usually update their software more frequently than you might in an on-premise scenario. That means you have to be especially proactive about dealing with known security flaws in outdated software. Indeed, on-premise data governance has the benefit of ownership, but your organization has to be ready to accept all the responsibility that option brings.

    4. Know that specialized data governance tools are advantageous in both cases

    You've already learned a few of the pros and cons of on-premise versus cloud-based solutions to meet your data governance requirements. Don't forget that no matter which of those you choose, specialty software can help you get a handle on data access, storage, usage and more. For example, software exists to help companies manage their data lakes whether they are on the premises or in the cloud.

    Those tools can sync with third-party sources of data to allow monitoring of all the data from a single interface. Moreover, they can track metadata changes, allowing users to become more aware of data categorization strategies.

    Regardless of whether you ultimately decide it's best to manage data governance through an on-premise solution or in the cloud, take the necessary time to investigate data governance tools. They could give your company insights that are particularly useful during compliance audits or as your company starts using data in new ways.

    Evaluate the tradeoffs

    As you figure out if it's better to entrust data governance to a cloud company or handle it on-site, don't forget that each option has pros and cons.

    Cloud companies offer convenience, but only if their data governance principles align with your needs. And, if customization is one of your top concerns, on-premise data governance gives you the most flexibility to make tweaks as your company evolves.

    Studying the advantages and disadvantages of these options carefully before making a decision should allow you to get maximally informed about how to accommodate for your company's present and future needs. 

    Author: Kayla Matthews

    Source: Information-management

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