What is Data Profiling?
The value of your data depends on how well you profile it. Today, only about 3% of data meets quality standards. That means poorly managed data is costing companies millions of dollars in wasted time, money, and untapped potential.
Data profiling helps your team organize and analyze your data in order to yield its maximum value and give you a clear, competitive advantage in the marketplace. In this article, we explore the process of data profiling and look at the ways it can help you turn raw data into business intelligence and actionable insights.
Basics of 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.
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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.
Data profiling can eliminate costly errors that are common in customer databases. These errors 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.
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Four 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.
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
Data profiling doesn't have to be done manually. In fact, the most efficient way to manage the profiling process is to automate it with a tool. Data profiling tools increase data integrity by eliminating errors and applying consistency to the data profiling process. Talend Data Integration Platform allows you to extract and process data from virtually any source to your data warehouse, without the painstaking process of hand-coding.
Download a free trial to find your fastest path to data integration.