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TDWI Upside - Where Data Means Business

Data Quality Best Practices for Today’s Data-Driven Organization

These four best practices will help ensure your data is of the highest quality.

Just a few years ago, data quality was about as exciting for many people as watching broccoli grow. Today it has become a critical strategic issue for two key reasons. First, a wave of strict new data privacy regulations has put compliance high on most CIO’s lists. Second, in an increasingly data-driven business environment, you can’t compete effectively without high-quality data.

For Further Reading:

4 Keys for Managing Contact Data

The Essential Role Data Quality Plays in Compliance

Reducing the Impact of Bad Data on Your Business

Our field has grown a great deal in a short time. A recent Forbes article notes that nearly two-thirds (63.4 percent) of Fortune 1000 firms now have a formal chief data officer (CDO) compared to just 12 percent in 2012. The data governance market is predicted to grow to over USD $2 billion by 2022.

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

I won’t try to tell you what the best data quality solution for you is, but I can tell you what the worst one is: the one that didn’t get used when it should have been. This is why it is critical to have a solution that integrates into your current automation environment instead of relying on standalone capabilities for such tasks as data validation and verification.

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. Your inbound contact data unfortunately includes everything from mistyped addresses to people supplying bogus information to get your latest free marketing giveaway.

This means you must validate data at the point of entry and time of use each and every time you use it. This requires having the right tools and the right processes. For example, having an API that catches bad contact data within your data entry environment is great, but not enough -- you also need a strategy for processing entire lists before every marketing or contact campaign. Otherwise you risk diminishing your ROI, annoying customers, or worse, running afoul of data privacy compliance laws by using bad data.

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. Today’s market targeting and business intelligence tools include capabilities such as integrating geolocation and demographic data, appending missing contact data to have a broader range of touchpoints for your prospects and customers, or using published data to rate the quality of your leads, among many others.

Whatever capabilities you consider, the important point is that your competitors are working from a richer set of contact data than they were just a few years ago. Market targeting is getting more precise, contact data analysis is going visual with the aid of tools such as GIS and data visualization, and the amount of business intelligence you can extract from your contact data assets continues to grow. Increasingly, associated data is becoming important for regulatory compliance in areas such as lending and housing.

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.

A key goal here is to avoid duplication of effort within your business. You can’t afford to have different departments using different tools and strategies or silos of data that don’t talk to each other. This affects your external brand as well as your internal costs because customers increasingly expect to have a seamless experience across your entire organization.

Looking to the Future

If the last few years are any indication, we should expect an even greater focus on data privacy and security. More regulations are likely. At the same time, the availability of better tools and more data means that the value and revenue potential of our contact data assets will also continue to increase. Currently, 13.5 percent of CDOs now have revenue responsibilities, and I predict that number will rise. Only by implementing data quality best practices will your business succeed in our constantly evolving business environment.

About the Author

Geoff Grow is the founder and CEO of Service Objects. Originally founded in 2001 to solve problems of inefficiency and waste through mathematical equations, Service Objects has validated and improved more than 3 billion contact records for over 2,500 clients. You can contact the author here.

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