Why you should prioritize Data Profiling more

Have you ever wondered why some data records or lists that you stored isn’t accurate after a few months or year? Well, I bet you already know why. It’s because Data’s can change over a period of time.

In marketing, Data profiling is essential. If you’re more in the email marketing strategy, you need accurate Email lists. If you’re in telemarketing, you need accurate lists. Everything must be accurate for you to satisfy your client.

Data profiling is the most common data quality-related activity that some companies uses. The only problem you may face is that when you started with data profiling, you can get into some bad practice. To avoid this bad practice, I’ve outlined the simple techniques that you’ll find useful as you develop your data profiling skill.

Segment Your Data Along Information Chains For Faster, More Relevant Results

Data profiling tools don’t have the performance challenges we used to face; they can typically cope with the full volume of data. However, this doesn’t mean it’s in your best interest to always perform an exhaustive profile of the entire data set, particularly when reporting back your findings.

By joining up the data along the information chain and creating an “end-to-end” profiling story, you’ll be adding far more value to the business. You’ve also created the basis for ongoing data quality rules management and monitoring.

Adopt the Right Profiling Approach for the Right Type of Data

When profiling, you have to be mindful of the impacts you’re witnessing based on the underlying type of data.

Adopt an “Inside-Out” and “Outside-In” Mindset

A data profiling tool can give you lots of information and insights into where problems exist, but it also gives you a lot of noise. If you’re examining a legacy system for the first time, you can often find literally thousands of seemingly erroneous data