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Data Profiling: The What and Why with Case Studies

Data profiling is a serious business. Companies that apply profiling capabilities to their data to structure and analyze them more effectively are finding new opportunities for success and gaining a clear competitive advantage in the marketplace.

Can you present the profiling of the data?

Data profiling is a process of examining data, analyzing it, and generating actionable summaries of that data. The profiling process provides an overview of the data that facilitates the discovery of certain aspects of data quality: issues, risks and general trends. Data profiling converts data into actionable knowledge.

More specifically, profiling examines data to determine their legitimacy and quality. To examine the data in great detail, the analytical algorithms detect the main characteristics of the datasets: average, minimum, maximum, percentile and frequency. They then use these values ​​to determine how well these factors align with the company’s standards and objectives.

Data profiling can eliminate costly and frequent errors in client databases. Examples include (but not limited to) missing values, values ​​that should not be included, values ​​that are abnormally high or low, values ​​that do not follow identified trends, and values ​​outside normal ranges.

Benefits of profiling data

Data quality issues cost US businesses more than $3 billion USD a year. For a lot of companies, it’s about millions of dollars lost, strategies that need to be recalculated and sometimes tarnished. How do data quality issues appear?

One of the main reasons is negligence: companies are so busy collecting data and managing their operations that the efficiency and quality of data is compromised. This can lead to lost productivity, missed business opportunities and the inability to improve financial results, hence the importance of a data profiling application.

When the profiling application is enabled, it continuously scans, cleans, and updates the data to provide essential information, including on a laptop. Data profiling provides the following benefits (non-exhaustive list):

  • Better quality and credibility of data – After analyzing the data, the application is ready to eliminate duplicates and anomalies. The application is able to identify the data that may influence the company’s choices, identify the quality issues that exist in the company’s system and draw some conclusions about the future health of the company’s business.
  • Predictive decision making – profiled data can be used to prevent small errors from turning into big problems. Profiling also helps to describe the potential results of new scenarios. With profiling data, you have a snapshot of the company’s health that helps you improve your decision-making process.
  • Proactive Crisis Management – Data profiling can help you quickly identify and resolve problems, often before they occur.
  • Data Tracking – Databases can handle different types of data: blogs, social media content and other big markets that generate Big Data. Profiling makes it possible to trace the history of this data to their original source and to apply the appropriate encryption to guarantee the security of the company’s activities. A profiling module can then analyze these different databases, source applications or tables and ensure that the data comply with the standard statistical measures and the specific business rules of the company.

Understanding the relationship between available data, missing data and required data allows the company to define its future strategy and long-term goals. Access to a data profiling application can optimize these operations.

Data profiling techniques

In general, data profiling applications analyze a database by organizing and collecting information about its content. Data profiling is based on three distinct activities:

  • Structure Discovery – Structure discovery (or analysis) helps you determine if your data is consistent and correctly formatted. This activity uses basic statistics to provide information about the validity of the data.
  • Content Discovery – Content discovery focuses on the quality of data. Data must be formatted, standardized and properly integrated with existing data in a timely and efficient manner. For example, if a mailing address is incorrectly formatted, the customer may not be contacted or his deliveries may be lost.
  • Relationship Discovery – Relationship discovery identifies connections between different datasets.

Data profiling in action

Companies are sometimes overwhelmed by the huge amounts of data they accumulate. As a result, they fail to make effective use of these data and their usefulness and value continues to decline. Data profiling solutions are responsible for structuring and managing Big Data to unlock their full potential and provide you with valuable insights.

Data Tsunami at Domino’s

With almost 14,000 establishments, Domino’s was already the largest chain of pizza restaurants in the world in 2015. But when this company launched its AnyWare control system, it suddenly faced a tsunami of data. Users could now place orders from most devices/apps, including smartwatch, TVs, embedded media systems and social media.

In a few weeks, Domino’s has seen the bursting of torrents of data from all walks of life. In response, Domino’s has deployed an effective data profiling solution and now the company can collect and analyze the data stored in its many outlets and optimize and improve the quality of this data. Through this initiative, Domino’s has transformed its business: in-depth knowledge of its customer base, the process of detecting more fraud, increasing operational efficiency and sales.

Data quality and customer loyalty

Office Depot complements its online presence with continuous offline strategies. In this company, data integration is crucial because it involves combining information from three channels: the physical (offline) catalog, the website and the call centers.

Office Depot uses data profiling to perform quality checks and controls on its data before injecting it into its Data Lake. Integrating data online and offline provides a true 360 ​​° view of customers and delivers high quality data to the back office functions of the business.

Profiling data in a Data Lake in the cloud

As organizations tend to store huge volumes of data in the cloud, the need for effective profiling is more important than ever. The data in the cloud lakes already allow companies to store multiple petabytes of data, and the Internet of Things (IoT) increased their assets by collecting large volumes of data from diverse sources and constantly evolving, including our homes, our clothes and the technologies we use.

To remain competitive in a market increasingly stimulated by Big Data processing capabilities in the cloud, it is necessary to have solutions capable of exploiting all this data. When it comes to managing large amounts of data and whatever your goal is (meeting compliance standards, creating a brand that will be recognized for excellence in customer service or otherwise), Data profiling is the hinge between success and failure.

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