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Data Profiling

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What Is Data Profiling?

Data profiling helps discover, understand, and organize data by identifying its characteristics and assessing its quality. The process can reveal if data is complete or unique, catch errors and unusual patterns, and determine usability. As a result, businesses benefit from more accurate analyses, better decisions, and large savings.

Why Is Data Profiling Important?

Across the US, bad data costs companies more than $3 trillion a year due to mistrust in data quality, repeated data cleaning, and hunting for additional data sources to confirm data accuracy. Profiling ensures data is high-quality and credible, allowing businesses to understand and verify characteristics of their data, identify data quality issues, and make sure data meets statistical and organizational standards.

Types of Data Profiling

There are many different types of data profiling techniques, but all fall within three major categories: structure, content, and relationship profiling. To understand the data profiling process and how these steps work together, imagine a company’s recent merger and the need to integrate data from one CRM system to another. Profiling will help understand the characteristics and quality of the source (the old system) and the target (the new system) by looking at the data’s format, information, and quality and the relationships between the different fields and tables in the database.

Data Profiling Process

Structure Discovery

The first step in profiling any data, whether an entire database or just one file, is to look at its structure and format. Some questions to ask during structure profiling:

  • What’s the overall size of the dataset?
  • What types of data does it contain? (E.g., strings, floats, datetime, Boolean, spatial objects)
  • Is data formatted consistently and correctly? This is important when it comes to migrating data to a new repository.

After addressing the above, label and tag data with the findings to improve usability.

Content Discovery

Looking at the content — both from a cognitive and visual perspective — can provide a better understanding of data and highlight where it has gaps or errors. During content profiling, one should:

  • Run a summary of statistics such as min/max values for numerical fields and frequency of values for categorical fields
  • Check for the number of null values, blanks, and unique values to gain insight into the range and quality of the data and whether a field is relevant
  • Look for systemic errors such as misspellings and variable representation of values (E.g., “Doctor” versus “Dr.”), which can derail an analytic process

Relationship Discovery
Identifying the key relationships across data can guide efforts in retention and spotlight where data might need to be transformed to be more effective. A relationship could be as simple as a formula in one spreadsheet cell that references another cell or as complex as a table that has aggregated sales data from a collection of regularly updated tables.

How Data Profiling Is Used

Companies collect more data than ever, but without the right processes and tools, they miss out on the chance to utilize it smartly. Profiling enables them to organize and manage data to reveal powerful, useful information. A few ways profiling can help:

  • Integrate data from various sources and determine the data quality before it’s entered into a company’s data lake
  • Provide insights on a customer base to boost efficiency, increase sales, and better detect fraud

Getting Started With Data Profiling

In many organizations, profiling falls to those with both technical and non-technical backgrounds. The Alteryx Analytic Process Automation (APA) Platform™ makes the task accessible with easy-to-use data profiling tools for structural, content, and relationship profiling including:

  • Input Data Tool to bring any kind of data into the Alteryx Designer interface
  • Basic Data Profile Tool to automatically analyze and provide metadata for each field
  • Browse Tool that uses charts and tables to show top values, key statistics, and the overall “shape” of a dataset

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