People generate 2.5 quintillion bytes of data every single day. That’s 1.7 megabytes generated every second for each of the 7.8 billion residents of Earth. A lot of that information is junk that somebody can easily discard, but just as much can prove to be vital. How do you tell the difference?
According to industry experts, poor quality information costs the U.S. economy upwards of $3.1 trillion annually. That is why data quality assessments (DQAs) are so important.
A Brief Explanation of Data Quality Assessments
With companies around the globe generating massive amounts of data every second of the day, it’s essential to have tools that help you sort through it all. Data quality assessments are usually carried out by software programmed with a predefined set of rules. They can compare the incoming information to those guidelines and provide reports.
This is a simplified explanation, but the goal of these DQA programs is to separate the wheat from the chaff. They eliminate any unnecessary or redundant data, leaving only the highest quality information.
The biggest challenge here is figuring who will determine what is considered quality. Data quality depends on three things: the individual or team that creates the requirements, how they complete that task, and how flexible the program meets those obligations.
How to Perform a DQA
Once you have your DQA program in place, performing an assessment is relatively simple. The challenge lies in establishing the program. The first step is to determine the scope of the data you’re trying to assess. The details of this step will depend on your system and the amount of information you have to sort through. You can set up a program to assess a single data point at a time, but if your system generates a lot of info, this isn’t effective from an efficiency standpoint.
Define your scope carefully to ensure the program does the job correctly without wasting time sorting through bytes one at a time.
Now that you have a framework to work from, you can move on to monitoring and cleansing data. Analyze your information against the scope and details you’ve established. Validate each point against your existing statistical measures, and determine its quality.
Next, ensure all the data requirements are available and correctly formatted. You may wish to provide training for any new team members entering information to ensure it’s in a format that the DQA system can understand.
Finally, make it a point to verify that your data is consistent with the rules you’ve established, as well as your business goals. DQAs aren’t a one-and-done kind of program. Monitoring needs to be an ongoing process to prevent things from falling through the cracks and keeping bad information from potentially costing you millions of dollars.
Benefits of DQA
A data quality assessment has various benefits, both on the commercial and consumer side of your business. Accuracy is essential. It’s valuable for marketers who purchase demographic data, with 84% stating it plays a large role in their purchase decisions. Targeted marketing is one of the most popular forms of advertisement, and while it’s not always practical, its efficacy drops even further if the demographic data is incorrect.
High-quality data should be accurate, complete, relevant, valid, timely and consistent. Maintaining frequent and comprehensive quality assessments can help you do that and more. The goal of collecting this information is to produce results. The higher quality your data is, the easier and faster your system will work, with better results than you might manage without DQAs.
Data Quality Assessment vs. Data Profiling
When talking about data quality, you’ll often see the terms assessment and profiling used interchangeably. While the concepts are similar, they are not the same. Data profiling is a valuable tool for setting up your quality assessment program, giving you the information you’ll need to build your program in the future. It isn’t a step you can perform independently and expect to get the same results.
If you don’t already have a DQA in place, start with profiling to create the foundation for a comprehensive data quality assessment program.
The Growing Importance of Data Quality
Data quality has always been important. However, as the population generates more information every year, learning how to separate value from junk is more critical than ever.