5 items tagged "unstructured data"

  • Conquering the 4 Key Data Integration Challenges

    Conquering the 4 Key Data Integration Challenges

    The ability to integrate data successfully into a single platform can be a challenge. Well-integrated data makes it easy for the appropriate staff to access and work with it. Poorly integrated data creates problems. Data integration can be described as the process of collecting data from a variety of sources and transforming it into a format compatible with the data storage system – typically, a database or a data warehouse. The use of integrated data, when making business decisions, has become a common practice for many organizations. Unfortunately, the data integration process can be troublesome, making it difficult to use the data when it is needed.

    Successful data integration allows researchers to develop meaningful insights and useful business intelligence.

    Integrated data creates a layer of informational connectivity that lays a base for research and analytics. Data integration maximizes the value of a business’s data, but the integration process requires the right tools and strategies. It allows a business to increase its returns, optimize its resources, and improve customer satisfaction. Data integration promotes high-quality data and useful business intelligence. 

    With the amount of data consistently growing in volume, and the variety of data formats, data integration tools (such as data pipelines) become a necessity. 

    By sharing this high-quality data across departments, organizations can streamline their processes and improve customer satisfaction. Other benefits of integrated data include:

    • Improved communication and collaboration
    • Increased data value 
    • Faster, better decisions based on accurate data
    • Increased sales and profits

    For data to be useful, it must be available for analysis, which means it must be in a readable format. 

    A Variety of Sources

    Data can be gathered from internal sources, plus a variety of external sources. The data taken from internal sources is referred to as “primary data,” and “secondary data” is often collected from outside sources, but not always. The sources of data selected can vary depending on the needs of the research, and each data storage system is unique and different.

    Secondary data is not limited to that from a different organization. It can also come from within an organization itself. Additionally, there are open data sources. 

    With the growing volume of data, the large number of data sources, and their varying formats, data integration has become a necessity for doing useful research. It has become an integral part of developing business intelligence. Some examples of data sources are listed below.

    Primary Data

    • Sensors: Recorded data from a sensor, such as a camera or thermometer
    • Survey: Answers to business and quality of service questions
    • User Input: Often used to record customer behavior (clicks, time spent)
    • Geographical Data: The location of an entity (a person or machine) using equipment at a point in time
    • Transactions: Business transactions (typically online)
    • Event Data: Recording of the data is triggered by an event (email arriving, sensor detecting motion)

    Secondary Data

    • World Bank Open Data
    • Data.gov (studies by the U.S. government)
    • NYU Libraries Research Guides (Science)

    Internal Secondary Data

    • Quickbooks (for expense management)
    • Salesforce (for customer information/sales data)
    • Quarterly sales figures
    • Emails 
    • Metadata
    • Website cookies

    Purchased, third-party data can also be a concern. Two fairly safe sources of third-party data are the Data Supermarket and Databroker. This type of data is purchased by businesses having no direct relationship with the consumers.

    Top Data Integration Challenges

    Data integration is an ongoing process that will evolve as the organization grows. Integrating data effectively is essential to improve the customer experience, or to gain a better understanding of the areas in the business that need improving. There are a number of prominent data integration problems that businesses commonly encounter:

    1. Data is not where it should be: This common problem occurs when the data is not stored in a central location. Instead, data is spread throughout the organization’s various departments. This situation promotes the risk of missing crucial information during research.

    A simple solution is to store all data in a single location (or perhaps two, the primary database and a data warehouse). Apart from personal information that is protected by law, departments must share their information, and data silos would be forbidden. 

    2. Data collection delays: Often, data must be processed in real time to provide accurate and meaningful insights. However, if data technicians must be involved to manually complete the data integration process, real-time processing is not possible. This, in turn, leads to delays in customer processing and analytics. 

    The solution to this problem is automated data integration tools. They have been developed specifically to process data in real time, prompting efficiency and customer satisfaction.

    3. Unstructured data formatting issues: A common challenge for data integration is the use of unstructured data (photos, video, audio, social media). A continuously growing amount of unstructured data is being generated and collected by businesses. Unstructured data often contains useful information that can impact business decisions. Unfortunately, unstructured data is difficult for computers to read and analyze. 

    There are new software tools that can assist in translating unstructured data (e.g., MonkeyLearn, which uses machine learning for finding patterns and Cogito, which uses natural language processing).

    4. Poor-quality data: Poor-quality data has a negative impact on research, and can promote poor decision-making. In some cases, there is an abundance of data, but huge amounts reflect “old” information that is no longer relevant, or directly conflicts current information. In other cases, duplicated data, and partially duplicated data, can provide an inaccurate representation of customer behavior. Inputting large amounts of data manually can also lead to mistakes.

    The quality of data determines how valuable an organization’s business intelligence will be. If an organization has an abundance of poor-quality data, it must be assumed there is no Data Governance program in place, or the Data Governance program is poorly designed. The solution to poor data quality is the implementation of a well-designed Data Governance program. (A first step in developing a Data Governance program is cleaning up the data. This can be done in-house with the help of data quality tools or with the more expensive solution of hiring outside help.)

    The Future of Data Integration

    Data integration methods are shifting from ETL (extract-transform-load) to automated ELT (extract-load-transform) and cloud-based data integration. Machine learning (ML) and artificial intelligence (AI) are in the early stages of development for working with data integration. 

    An ELT system loads raw data directly to a data warehouse (or a data lake), shifting the transformation process to the end of the pipeline. This allows the data to be examined before being transformed and possibly altered. This process is very efficient when processing significant amounts of data for analytics and business intelligence.

    A cloud-based data integration system helps businesses merge data from various sources, typically sending it to a cloud-based data warehouse. This integration system improves operational efficiency and supports real-time data processing. As more businesses use Software-as-a-Service, experts predict more than 90% of data-driven businesses will eventually shift to cloud-based data integration. From the cloud, integrated data can be accessed with a variety of devices.

    Using machine learning and artificial intelligence to integrate data is a recent development, and still evolving. AI- and ML-powered data integration requires less human intervention and handles semi-structured or unstructured data formats with relative ease. AI can automate the data transformation mapping process with machine learning algorithms.

    Author: Keith D. Foote

    Source: Dataversity

  • 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

  • Natural Language Processing is the New Revolution in Healthcare AI  

    Natural Language Processing is the New Revolution in Healthcare AI

    After a late start, Artificial Intelligence (AI) has made massive inroads in penetrating the pharmaceutical and healthcare industries. Nearly all aspects of the industry have felt impacts from the increasing uptake of the technologies – from visual and image diagnostics, to Machine Learning and Neural Networks for drug discovery, data analysis and other models. One of the more recently ascendant forms of AI in healthcare has been Natural Language Processing (NLP) – which promises to revolutionize digitization, communication and analyzing human-generated data. We explore the foundations of the technology and its current and future applications in the industry.

    What is Natural Language Processing?

    NLP refers to the practice of using computer methods to process language in the form generated by humans – often disordered, flexible and adaptable. The phenomenon is not limited to AI technology; it first originated in the 1950s with symbolic NLP. This was a rudimentary concept which was originally intended for machine translation applications – first demonstrated by the 1954 Georgetown Experiment. Symbolic NLP was largely founded on complex, manually defined rulesets: it soon became apparent that the unpredictability and fluidity of human language could not truly be defined by concise rules. 

    With the exponential growth in computing power, symbolic NLP soon gave rise to statistical NLP – largely pioneered by IBM and their Alignment Models for machine translation. IBM developed six such models, which enabled a more flexible approach to machine-based translation. Other companies soon followed, and statistical NLP evolved into what we know today as self-learning NLP, underpinned by machine learning and neural networks. The developments in its ability to recognize and process language have put it to use in fields far more diverse than translation – although it continues to make improvements there too. 

    While symbolic NLP is still often employed when datasets are small, statistical NLP has been largely replaced by neural network-enabled NLP. This is due to how neural networks can simplify the process of constructing functional models. The trade-off lies in the opacity of how they operate – while statistical methods will always be fully transparent and the path to how they obtain their results will be fully visible, neural network models are often more of a “black box”. Their power in interpreting human language is not to be underestimated however – from speech recognition, including the smart assistants we have come to rely on, to translations and text analytics, NLP promises to bridge many gaps.

    Current Models in Healthcare

    One of the most obvious applications of NLP in the healthcare industry is processing written text – whether that be analog or digital. A leading source of data heterogeneity, which often prevents downstream analysis models from directly utilizing datasets, is the different terminology and communication used by healthcare practitioners. Neural-enabled NLPs can condense such unstructured data into directly comparable terms suitable for use in data analysis. This can be seen in models inferring International Classification of Diseases (ICD) codes based on records and medical texts.

    Medical records present rich datasets that can be harnessed for a plethora of applications with the appropriate NLP models. Medical text classification using NLPs can assist in classifying disease based on common symptoms – such as identifying the underlying conditions causing obesity in a patient. Models such as this can then be later used to predict disease in patients with similar symptoms. This could prove particularly revolutionary in diseases such as sepsis – which has early symptoms that are very common across a number of conditions. Recurrent Neural Network NLP models using patient records to predict sepsis showed a high accuracy with lower false alarm rates than current benchmarks. 

    These implementations are also critical in clinical development. But clinical operations also generate another source of unstructured data: adverse event reports, which form the basis of pharmacovigilance and drug safety. We already explored the applications of AI models in pharmacovigilance in a different article – but their introduction to that field particularly highlights the need for increased cooperation with regulatory authorities to ensure all stakeholders remain in lockstep as we increasingly adopt AI. 

    Beyond Healthcare

    But NLP can also be applied beyond human language. Exploratory studies have also shown its potential in understanding artificial languages, such as protein coding. Proteins, strings of the same 20 amino acids presenting in variable order, share many similarities with human language. Research has shown that language generation models trained on proteins can be used for many diverse applications, such as predicting protein structures that can evade antibodies. 

    There are other sources of unstructured, heterogeneous data that are simply too big to pore over with human eyes in cost-efficient manners. Science literature can be one of these – with countless journal articles floating around libraries and the web. NLP models have previously been employed by companies such as Linguamatics to capture valuable information from throughout the corpus of scientific literature to prioritize drug discovery efforts.

    Quantum computing also represents a major growing technology, and firms are already seeking to combine it with NLP. One example is Quantinuum’s λambeq library, which can convert natural language sentences to quantum circuits for use in advanced computing applications. Future endeavors in the area promise massive advancements in text mining, bioinformatics and all the other applications of NLP.

    Research conducted by Gradient Flow has shown that Natural Language Processing is the leading trend in AI technologies for healthcare and pharma. This is for good reason – AI can prove useful in a cornucopia of different implementations, but NLP is what facilitates the use of heterogeneous, fragmented datasets in the same model. Integrating existing and historical datasets, or new datasets generated in inherently unstructured manners – articles, records and medical observations, will remain crucial in the progress of other AI technologies. NLP is what enables that – and future advancements are likely to see its prominence rise on its own, as well.

    Author: Nick Zoukas

    Source: PharmaFeatures


  • Staying on the right track in the era of big data

    Staying on the right track in the era of big data

    Volume dominates the multidimensional big data world. The challenge many organizations today are facing is harnessing the potential of the data and applying all of the usual methods and technologies at scale. After all, data growth is only increasing and is currently being produced at 2.5 quintillion bytes of data per day. Unfortunately, a large portion of this data is unstructured, making it even harder to categorize.

    Compounding the problem, most businesses expect that decisions made based on data will be more effective and successful in the long run. However, with big data often comes big noise. After all, the more information you have, the more chance that some of that information might be incorrect, duplicated, outdated, or otherwise flawed. This is a challenge that most data analysts are prepared for, but one that IT teams need to consider and factor into their downstream processing and decision making to ensure that any bad data does not skew the resulting insights.

    This is why overarching big data analytics solutions alone are not enough to ensure data integrity in the era of big data. In addition, while new technologies like AI and machine learning can help make sense of the data en masse, often these rely on a certain amount of cleaning and condensing going on behind the scenes to be effective and able to run at scale. While accounting for some errors in the data is fine, being able to find and eliminate mistakes where possible is a valuable capability, which can have a catastrophic effect in terms of derailing effective analysis and delaying the time to value. In particular if there is a configuration error or problem with a single data source creating a stream of bad data. Without the right tools, these kinds of errors can create unexpected results and leave data professionals with an unwieldy mass of data to sort through to try and find the culprit.

    This problem is compounded when data is ingested from multiple different sources and systems, each of which may have treated the data in a different way. The sheer complexity of big data architecture can turn the challenge from finding a single needle in a haystack to one more akin to finding a single needle in a whole barn.

    Meanwhile, this problem has become one that doesn’t just affect the IT function and business decision making, but is becoming a legal requirement to overcome. Legislation like the European Union’s General Data Protection Regulation (GDPR) mandates that businesses find a way to manage and track all of their personal data, no matter how complicated the infrastructure or unstructured information. In addition, upon receiving a valid request, organizations need to be able to delete information pertaining to an individual or collect and share it as part of an individual’s right to data portability.

    So, what’s the solution? One of the best solutions for managing the beast of big data overall is also one that builds in a way to ensure data integrity, ensuring a full data lineage by automating data ingestion. This creates a clear path showing how data has been used over time, as well as its origins. In addition, this process is done automatically, making it much easier and more reliable. However, it is important to ensure that lineage is done at the fine detail level.

    With the right data lineage tools, ensuring data integrity in a big data environment becomes far easier. The right tracking means that data scientists can track data back through the process to explain what data was used, from where, and why. Meanwhile, businesses can track down the data of a single individual, sorting through all the noise to fulfill subject access requests without disrupting the big data pipeline as a whole, or diverting significant business resources. As a result, analysis of big data can deliver more insight, and thus more value, faster, despite its multidimensional complexity.

    Author: Neil Barton

    Source: Dataversity

  • The difference between structured and unstructured data

    The difference between structured and unstructured data

    Structured data and unstructured data are both forms of data, but the first uses a single standardized format for storage, and the second does not. Structured data must be appropriately formatted (or reformatted) to provide a standardized data format before being stored, which is not a necessary step when storing unstructured data.

    The relational database provides an excellent example of how structured data is used and stored. The data is normally formatted into specific fields (for example, credit card numbers or addresses), allowing the data to be easily found using SQL.

    Non-relational databases, also called NoSQL, provide a way to work with unstructured data.

    Edgar F. Codd invented relational databases (RDBMs) in 1970, and they became popular during the 1980s. Relational databases allow users to access data and write in SQL (Structured Query Language). RDBMs and SQL gave organizations the ability to analyze stored data on demand, providing a significant advantage against the competition of those times. 

    Relational databases are user-friendly, and very, very efficient at maintaining accurate records. Regrettably, they are also quite rigid and cannot work with other languages or data formats.

    Unfortunately for relational databases, during the mid-1990s, the internet gained significantly in popularity, and the rigidity of relational databases could not handle the variety of languages and formats that became accessible. This made research difficult, and NoSQL was developed as a solution between 2007 and 2009. 

    A NoSQL database translates data written in different languages and formats efficiently and quickly and avoids the rigidity of SQL. Structured data is often stored in relational databases and data warehouses, while unstructured data is often stored in NoSQL databases and data lakes.

    For broad research, unstructured data used by NoSQL databases, compared to relational databases, are the better choice because of their speed and flexibility.

    The Expanded Use of the Internet and Unstructured Data

    During the late 1980s, the low prices of hard disks, combined with the development of data warehouses, resulted in remarkably inexpensive data storage. This, in turn, resulted in organizations and individuals embracing the habit of storing all data gathered from customers, and all the data collected from the internet for research purposes. A data warehouse allows analysts to access research data more quickly and efficiently.

    Unlike a relational database, which is used for a variety of purposes, a data warehouse is specifically designed for a quick response to queries.

    Data warehouses can be cloud-based, or part of a business’s in-house mainframe server. They are compatible with SQL systems because by design, they rely on structured datasets. Generally speaking, data warehouses are not compatible with unstructured, or NoSQL, databases. Before the 2000s, businesses focused only on extracting and analyzing information from structured data. 

    The internet began to offer unique data analysis opportunities and data collections in the early 2000s. With the growth of web research and online shopping, businesses such as Amazon, Yahoo, and eBay began analyzing their customer’s behavior by including such things as search logs, click-rates, and IP-specific location data. This abruptly opened up a whole new world of research possibilities. The profits resulting from their research prompted other organizations to begin their own expanded business intelligence research.

    Data lakes came about as a way to deal with unstructured data in roughly 2015. Currently, data lakes can be set up both in-house and in the cloud (the cloud version eliminates in-house installation difficulties and costs). The advantages of moving a data lake from an in-house location to the cloud for analyzing unstructured data can include:

    • Cloud-based tools that are more efficient: The tools available on the cloud can build data pipelines much more efficiently than in-house tools. Often, the data pipeline is pre-integrated, offering a working solution while saving hundreds of hours of in-house set up costs.
    • Scaling as needed: A cloud provider can provide and manage scaling for stored data, as opposed to an in-house system, which would require adding machines or managing clusters.
    • A flexible infrastructure: Cloud services provide a flexible, on-demand infrastructure that is charged for based on time used. Additional services can also be accessed. (However, confusion and inexperience will result in wasted time and money.) 
    • Backup copies: Cloud providers strive to prevent service interruptions, so they store redundant copies of the data, using physically different servers, just in case your data gets lost.

    Data lakes, sadly, have not become the perfect solution for working with unstructured data. The data lake industry is about seven years old and is not yet mature – unlike structured/SQL data systems. 

    Cloud-based data lakes may be easy to deploy but can be difficult to manage, resulting in unexpected costs. Data reliability issues can develop when combining batch and streaming data and corrupted data. A lack of experienced data lake professionals is also a significant problem.

    Data lakehouses, which are still in the development stage, have the goal of storing and accessing unstructured data, while providing the benefits of structured data/SQL systems. 

    The Benefits of Using Structured Data

    Basically, the primary benefit of structured data is its ease of use. This benefit is expressed in three ways:

    • A great selection of tools: Because this popular way of organizing data has been around for a while, a significant number of tools have been developed for structured/SQL databases.
    • Machine learning algorithms: Structured data works remarkably well for training machine learning algorithms. The clearly defined nature of structured data provides a language machine learning can understand and work with.
    • Business transactions: Structured data can be used for business purposes by the average person because it’s easy to use. There is no need for an understanding of different types of data.

    The Benefits of Using Unstructured Data 

    Examples of unstructured data include such things as social media posts, chats, email, presentations, photographs, music, and IoT sensor data. The primary strength of NoSQL and data lakes working with unstructured data is their flexibility in working with a variety of data formats. The benefits of working with NoSql databases or data lakes are:

    • Faster accumulation rates: Because there is no need to transform different types of data into a standardized format, it can be gathered quickly and efficiently.
    • More efficient research: A broader base of data taken from a variety of sources typically provides more accurate predictions of human behavior.

    The Future of Structured and Unstructured Data

    Over the next decade, the use of unstructured data will become much easier to work with, and much more commonplace. It will have no problems working with structured data. Tools for structured data will continue to be developed, and it will continue to be used for business purposes. 

    Although very much in the early stages of development, artificial intelligence algorithms have been developed that help find meaning automatically when searching unstructured data.

    Currently, Microsoft’s Azure AI is using a combination of optical character recognition, voice recognition, text analysis, and machine vision to scan and understand unstructured collections of data that may be made up of text or images. 

    Google offers a wide range of tools using AI algorithms that are ideal for working with unstructured data. For example, Vision AI can decode text, analyze images, and even recognize the emotions of people in photos.

    In the next decade, we can predict that AI will play a significant role in processing unstructured data. There will be an urgent need for “recognition algorithms.” (We currently seem to be limited to image recognitionpattern recognition, and facial recognition.) As artificial intelligence evolves, it will be used to make working with unstructured data much easier.

    Author: Keith D. Foote

    Source: Dataversity

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