In today’s business world, organizations collect customer data through several channels and maintain contact records across different databases. But what good does this customer data provide you if it’s inaccurate or out of date? Proactively ensuring you have accurate, up-to-date contact data that’s ready for use allows your organization to make agile business decisions backed by high-quality data.
What is data quality?
Having data that is of high data quality means the data is fit for intended uses—like improved analysis and reporting or an innovative marketing strategy. Data-driven decisions in your business are only as good as the data that guides them. With a data quality solution, you can validate, standardize, enrich, and profile your data to unlock its full potential.
Why is it important to test data quality?
Poor data quality can have a significant impact on business performance, making it more difficult to build customer loyalty and trust, understand customer preferences, maintain accurate communication, and execute effective marketing strategies. While ensuring your customer data is reliable may seem like a complex task, Experian’s data quality solutions simplify the process by helping you collect, validate, and maintain trusted data. We’re here to help you take the right steps to test and improve your data quality.
What are the steps to data quality testing?
Step 1: Define specific data quality metrics
Your organization needs specific metrics to test against to understand what you are targeting and need to improve.
Think about how your business uses data and what problems higher quality data can solve for. Some examples include:
- Amount of returned mail
- Number of individuals with complete contact information
- Number of personalized offers accepted
Data quality metrics that matter will vary based on your job role or focus area. If you’re part of a shipping and logistics team, you want to ensure your organization is collecting valid addresses at checkout, so you are not retroactively dealing with return packages and wasted warehouse resources. If you are an email marketer, your gauge for data quality may be how many email addresses on your list are reachable.
Step 2: Conduct a test to find your baseline
Driving data quality improvement throughout your organization won’t be possible unless you have defined a baseline and identified the gaps in your data that you want to improve.
In our warehouse example, there are specific tools available to help you easily validate addresses before the outbound packages leave your facility. For the email marketer, our tools allow you to validate your email list data quality before launching an email campaign and watering down your engagement metrics.
Step 3: Try a solution
Once you have determined what business areas you need to improve your data to meet your goals, you can start addressing your specific data quality issue.
In the case of the poor address data quality example, you have options, including an immediate one to fix your existing problem (batch address verification) and one that is more long term, avoiding bad addresses before they even enter your database (real-time validation at point of capture).
Step 4: Assess your results
After you have implemented your data quality solution, is it important to run another test against your initial metrics to understand where you saw positive improvement or to identify where you need to continue refining. The results will determine how you adjust your data quality solution.
Data quality can be mean something different from one organization to the next. But as long as you are defining criteria that make sense for your business and testing against those, you can be sure you’ll be able to find ways to drive improvement.
How Experian can help with data quality testing
At Experian, we believe in empowering business users to better understand their data assets to transform their businesses. With Experian’s data quality tools, we provide comprehensive solutions to help your business maintain the accuracy of your customer errors, reduce errors, and avoid additional costs associated with bad data.