Incorrect customer addresses and empty data fields can be extremely frustrating. But unless you are able to prove a business case for why improving data quality should be a company mission, it's unlikely that you will convince anyone to fund your efforts or get involved. Additionally, it is important to have direction and clear goals for what you hope to achieve by addressing contact data quality issues.
"Before any data quality project, you have to go beyond the immediate issues of duplicate records or bad addresses and understand the fundamental business needs of the organization" and how higher quality data will improve your ability to make business decisions, Christina Shaw writes for the Clean Data Blog.
Shaw notes that companies should identify which aspects of operations are currently being impacted by poor data quality, and should then outline a plan of action for the data once the address validation or database cleansing program has begun.
The initiative may have been inspired by one particular problem caused by low-quality data, but she explains that it can often have widespread consequences across various departments in the organization. From there, take stock of the challenges experienced previously with data quality, as well as those in the future and the ones you currently face, Shaw says. Business shifts such as a merger or major labor force change could present new problems, so it's important to anticipate and adapt in order to avoid data-related catastrophes down the line.
Testing point-of-entry verification systems and other methods of inputting data that your company uses when dealing with customers will help you get a sense of the client experience. It may also help you pinpoint some customer-facing complications that you did not find earlier.
It's also key to investigate the data that is missing from a database. What information would employees need to do their jobs better? Try to collect and enter those names, phone numbers or other details as you go. Shaw also advises tracking how corporate data flows through the organization. At what points is it edited? Where is it stored? Who views it, and how do they use it?
"The challenges with identifying, evaluating and implementing an effective data quality solution are fairly predictable, but problems almost always begin with incorrect assumptions and understanding of the overall needs of the organization," she concludes. As such, it's important that companies combine adequate planning, technology and governance policies in order to overcome the challenges presented by data quality.