Once properly investigated, it usually doesn’t take much further cajoling to get business leaders to take action to resolve data quality issues. However, due to the scale at which individual departments and companies as a whole have adopted data-driven business plans, it can be difficult for leaders to then pump the proverbial brakes to ask serious questions about their data. In order to help leaders take this step, here are a few simple places to start to get the ball rolling towards meaningful data quality improvements.
Find a Data Quality Partner
It may seem simplistic, but working with a data quality company from the off will offer the most immediate positive impact on your efforts. Finding qualified data quality professionals in the labor market is difficult and costly, but working with a third-party means paying for a solution and expertise without adding to your payroll. If hiring a third-party is off the table, don’t panic. There are some other ways to start addressing your data quality issues in-house.
Define Data Quality
84% percent of CEOs are concerned about their company’s data quality. However, some of these leaders would be hard-pressed to define what that term means to their business. We can’t imagine a better place to start the conversation than here. So, which components of data quality are most important to you and your team? Accuracy, timeliness, completeness, validity? Start defining the guidelines for what makes your data accurate, timely, complete, and valid. Once these guidelines are set, you can start making changes to your processes and assessments to ensure your data is more useful to your company.
Standardization of Incoming Data
The point at which external data enters a company’s applications and databases is one of the most crucial points in the data quality process. Taking action to standardize this incoming data, before it enters your analytics, is hugely beneficial to any organization. Changing forms to require all data fields be complete, digit requirements for phone numbers, file numbers, and social security numbers, etc., utilizing captcha forms to eliminate bots; all are techniques that can be used to improve data quality prior to it altering your databases for the worse. Keep in mind, though, that manually going through past data records will need to be undertaken to ensure full complicity. Our data quality solution allows users to set parameters on their data to immediately show poor data sets, which eliminates the need to do this painstaking task manually.
If you’re still hesitant about addressing your data quality needs, go ahead and schedule a data quality consultation. Here are a few reasons why you should do that. We also have an in-depth post on taking Four Steps to Data Quality Confidence which can also be a big help for those of you wishing to take this challenge head-on.