In a data-driven world, you can’t compete with the best if your data is not of the highest quality.
Geoff Grow enumerates the four Data Quality Best Practices in this article for TDWI.org:
One good thing about all this growth is that several clear best practices for data quality have emerged. In this article, I will highlight four of the most important ones.
Best Practice #1: API integration. We are in the middle of an API revolution where cloud-based tools link your CRM, marketing automation, or other platforms to tools such as USPS databases, geolocation, lead validation, and much more. IBM and others have dubbed this trend the “API economy.” Using an API strategy lets you engineer these tools directly into your data flow at the time of data entry or use.
Best Practice #2: Trust but verify. Contact data goes bad at a frightfully rapid rate. According to at least one source, over 70 percent of B2B contact data decays over the course of a year as people move, change jobs, or get new addresses or emails. This doesn’t even count how much of it is wrong, fake, or fraudulent in the first place.
Best Practice #3: Add value to your contact data assets. With the advent of inexpensive cloud-based data quality tools, basic contact data is now simply a starting point for a wealth of associated data.
Best Practice #4: Implement a data governance strategy. Formal oversight of data quality isn’t just the domain of the Fortune 1000 anymore. Even the smallest organizations need to make sure that people and processes are in place to maintain consistent data quality procedures as well as regulatory compliance.
