Data profiling is the process of evaluating your data to identify errors and defects that degrade the quality of your data.  

Consider data profiling synonymous to surveying a new house before making a purchase. You inspect the house, check it for any leakage, any defects and flaws that may affect its stability, inspect the gas lines, the electrical appliances, and a dozen other factors. Once done with the inspection, you assess the cost of making fixes and finally you begin the repairing process.  

In this quick blog post, I’ll walk you through the meaning of data profiling, why it’s important and how you can use a self-service solution to evaluate the quality of your data.  

Common Issues with Data that Can be Identified During the Profiling Phase  

Over the years, the nature of data has evolved and so has the process of gathering data. Companies are acquiring more and more diverse data from multiple sources, including sources like social media, web forms, third-party vendors and more. With this diversity in data comes a range of data quality issues. Some of the most common are:  

  • Non-standardized data. For instance, date of births may be written in multiple ways such as MM/DD/YY or DD/MM/YYYY. This discrepancy leads to confusion and inaccurate analysis. A date like 9/10 is challenging. How would you know which is the month and which is the day? 
     
  • Unwanted elements in the data. Accidental punctuation marks, letters or numbers in the wrong fields, extra spaces or characters etc are hard to catch manually. Although it doesn’t seem as big of a problem, issues like these piles up and before you know it, you have to scan through rows and rows of data to catch oddities. 
     
  • Missing or incomplete values. Do you know how many of your rows have missing ZIP codes or city codes? Do you know how many of them have missing last names or middle names? Incomplete data is one of the most problematic challenges that literally skews your insights and analytics. 
     
  • Use of nicknames, abbreviations and short forms. Abby instead of Abilene? Bee instead of Brighton? Abilene writes her name as Abby on Facebook, but when she makes a payment, your company saves her name as Abilene. When you want to match this user and consolidate her record, you’ll have a hard time verifying if it’s the same individual. These issues are hard to catch in the later stages.  

There are a dozen more problems like this which makes it difficult for companies to get accurate data for insights which is why it’s necessary to conduct an in-depth profiling before you set out to perform critical operations such as a CRM migration, creating an intelligence report or initiating a marketing/promotional campaign. 

Why Manual Profiling Methods are No Longer Effective  

Up until a few years ago, data profiling was performed manually by ETL tools or via SQL scripting, which was time-consuming and also ineffective in helping users identify and verify problems with data quality. Moreover, ETL tools were better suited for data that was already structured – meaning data that didn’t have much standardization issues and were consistent in format, if not in content. 

P.S: Data profiling is different from data cleansing. If you’re interested to know more, I recommend reading this extensive post on, Data Profiling vs Data Cleansing – Everything You Need to Know.’ 

But as data evolved in terms of variety, function, purpose, structure, volume and veracity, traditional ETL methods can no longer be used. It’s also important to understand that the ETL tools and methods work best for data stored in data warehouses. It is not effective to use on big data or data stored in modern CRMs.  

It would take a user months to profile a few thousand rows of data to identify common issues – even then, there will be significant gaps in what the user can find. Moreover, with manual methods your team is spending far more time in finding for fixes than in actually making the necessary fixes.  

Because this process is so hectic, most companies skip the profiling step altogether! But that’s where things get complicated – if you don’t profile your data, you wouldn’t know what’s hurting you.  

How Self-Service Solutions are Making it Easier for Business Users to Profile their Data  

As the term implies, self-service means the ability to use a tool to prepare data yourself without any need for coding or programming knowledge. Pretty much like DIY software solutions, self-service data profiling tools make it easier for the user to work with their data.  

Of the many reasons self-service solutions are recommended, one key reason is the ability to empower business users to take charge of their data. Usually, it’s only IT users that work with data and this leaves business users dependent on IT for every little task. Whether it’s fixing data in a CRM, it’s pulling out reports, it’s matching or consolidating multiple records, a business user has to depend on IT.  This sometimes leads to conflicts especially if IT users do not understand the context or requirements as well as a business user. Additionally, it also causes problems with timelines, and prioritization of tasks. What’s a priority for business users may not be a priority for IT. Conflicts are inevitable!  

With a self-service tool, business users will be able to profile and clean their data without requiring IT help or using any code. This would also enable them to understand the types of errors they are dealing with and the kind of data governance or data controls that can be used to prevent these errors from happening.  

For instance, while profiling, the user realizes that most phone fields have missing city codes. With this insight, the user can then instruct the front-end team (assuming this data is collected via a web form) to make city codes a mandatory field when entering phone numbers. In the future, this seemingly small step will help marketing teams assess region-based insights and allow them to create targeted campaigns.  

The whole idea of using a self-service tool is to cut down time with mediocre tasks and pay more attention to improvements in data quality or in obtaining insights that matters. If your teams are working with poor data, pretty soon, you’ll see huge gaps in your revenue.  

To Conclude – Automate Your Data Profiling and Cleansing Tasks & Empower Your Teams  

If you consider yourself a data-driven organization, automation is key to deriving value from your data. In an age when customers expect companies to be on their feet with new initiatives, you can’t afford to fall back just because your team doesn’t have accurate contact information or address data to make the right decisions or are too busy making fixes instead of hunting for the next opportunity!  

Author Bio:

Javeria Gauhar, an experienced B2B/SaaS writer specializing in writing for Data Ladder. She is also a programmer with 2 years of experience in developing, testing and maintaining enterprise software applications.

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