What is Data Profiling?

Data profiling means business. Companies that use data profiling to organize and analyze their data uncover new potential for success, and give themselves a clear, competitive advantage in the marketplace.

What Is Data Profiling?

Data profiling is the process of examining, analyzing, and creating useful summaries of data. The process yields a high-level overview which aids in the discovery of data quality issues, risks, and overall trends. Data profiling produces critical insights into data that companies can then leverage to their advantage.

More specifically, data profiling sifts through data in order to determine its legitimacy and quality. Analytical algorithms detect data set characteristics such as mean, minimum, maximum, percentile, and frequency in order to examine data in minute detail. It then uses that information to expose how those factors align with your business’ standards and goals.

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Typical discrepancies that data profiling can uncover include missing values, values that shouldn’t be included, values with unusually high or low frequency, values that don’t follow expected patterns, and values outside the normal range. These kinds of errors can lead to problems with customer databases,

Benefits of Data Profiling

Data quality problems cost U.S. businesses more than $3 trillion a year. For many companies that means millions of dollars wasted, strategies that have to be recalculated, and tarnished reputations. So how do data quality problems arise?

Often the culprit is oversight. Companies can become so busy collecting data and managing operations that the efficacy and quality of data becomes compromised. That could mean lost productivity, missed sales opportunities, and missed chances to improve the bottom line. That’s where a data profiling application comes in.

Once a data profiling application is engaged, it continually analyzes, cleans, and updates data in order to provide critical insights that are available right from your laptop. In particular, data profiling provides:

  • Better Data Quality and Credibility — Once data has been analyzed, the application can help eliminate duplications or anomalies. It can determine useful information that could affect business choices, identify quality problems that exist within an organization’s system, and be used to draw certain conclusions about future health of a company.
  • Predictive Decision Making — Profiled information can be used to stop small mistakes from becoming big problems. It can also reveal possible outcomes for new scenarios. Data profiling helps create an accurate snapshot of a company’s health to better inform the decision making process.
  • Proactive Crisis Management — Data profiling can help quickly identify and address problems, often before they arise.
  • Organized Sorting — Most databases interact with a diverse set of data that could include blogs, social media, and other big data markets. Profiling can trace data to its original source and ensure proper encryption for safety. A data profiler can then analyze those different databases, source applications or tables, and assure that the data meets standard statistical measures and specific business rules.

Understanding the relationship between available data, missing data, and required data helps an organization chart its future strategy and determine long-term goals. Access to a data profiling application can streamline these efforts.

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Data Profiling Techniques

In general, data profiling applications analyze a database by organizing and collecting information about it. But there are also three distinct components of data profiling:

  • Structure Discovery — Structure discovery (or analysis) helps determine whether your data is consistent and formatted correctly. It uses basic statistics to provide information about the validity of data.
  • Content Discovery — Content discovery focuses on data quality. Data needs to be formatted, standardized, and properly integrated with existing data in a timely and efficient manner. For example, if a street address is incorrectly formatted it could mean that certain customers can’t be reached or a delivery becomes misplaced.
  • Relationship Discovery — Relationship discovery identifies connections between different data sets. 

Data Profiling in Action

With the enormous amount of data available today, companies sometimes get overwhelmed by all the information they’ve collected. As a result, they fail to take full advantage of their data so its value and usefulness diminish. Data profiling organizes and manages big data to unlock its full potential and deliver powerful insights. Talend is helping companies do exactly that.

Domino’s Data Avalanche

With almost 14,000 locations, Domino’s was already the largest pizza company in the world by 2015. But when the company launched its AnyWare ordering system, they were suddenly faced with an avalanche of data. Users could now place orders through virtually any type of device or app, including smart watches, TVs, car entertainment systems, and social media platforms.

That meant Domino’s had data coming at them from all sides. By putting reliable data profiling to work, Domino’s now collects and analyzes data from all of the company’s point of sales systems in order to streamline analysis and improve data quality. As a result, Domino’s has gained deeper insights into their customer base, enhanced fraud detection processes, boosted operational efficiency, and increased sales.

Data Quality for Customer Loyalty

Office Depot combines an online presence with continued, offline strategies. Integration of data is crucial, combining information from three channels: the offline catalog, the online website, and customer call centers.

Among other things, Office Depot uses data profiling to perform checks and quality control on data before it is entered into the company’s data lake. Integrated online and offline data results in a complete 360-degree view of customers. It also provides big-quality data to back-office function throughout the company.

Data Profiling with Data Lakes and the Cloud

As more companies store enormous amounts of data in the cloud, the need for effective data profiling is more important than ever. Cloud-based data lakes already allow companies to store petabytes of data, and the Internet of Things is expanding our capacity for data by collecting vast amounts of information from an ever-evolving range of sources including our homes, what we wear, and the technologies we use.

Staying competitive in the modern marketplace—increasingly driven by cloud-native big data capabilities—means being equipped to harness all that data. From maintaining compliance standards, to creating a brand known for outstanding customer service, data profiling is the hinge between success and failure when it comes to managing data stores.

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Ready, Set, Profile!

Talend’s Data Quality platform offers an open source set of profiling tools that simplify the extraction, loading, transformation and process for managing large and diverse data sets.

Easy to learn and use, Data Quality provides accessible support with quality user documentation, on-demand tutorials, webinars, and a large and active Talend user community.

With Talend Data Preparation, data engineers will delegate data discovery to business users that can easily do basic profiling like data discovery. They can then identify data errors and then ask IT to solve identified issues in Talend’s Data Quality Platform.

Data Quality from Talend also includes a data assessment tool that provides benefits such as enhanced customer relationships, supply chain efficiencies, compliance efforts, and decision making within your company. Other features

  • Easy access to a wide range of databases, file types, and applications from the same graphical console with built-in data connectors.
  • Use of Data Explorer to drill down into individual data sources to view specific records.
  • Analysis of statistical data profiling ranging from simple record counts by category, to specific text or numeric fields, to advanced indexing based on phonetics and sounds.
  • Application of custom business rules to data for identifying records that cross certain thresholds, or that fall inside or outside of defined ranges.
  • Identifying data that fails to conform to specified internal standards such as SKU or part number forms, or external reference standards such as email address format or international postal codes.

Whether you’re taking on a new data project or want to improve the functionality of an established database, Talend’s Data Quality tool can help you take control of your data.Try Data Quality for free or explore Talend Open Studio for Data Quality to see what data profiling can do for you.

| Last Updated: October 4th, 2018