Data Integrity — Why Your Data May Be Wrong

November 11, 2016 Kara Frazier

When it comes to your organization’s analytics, it’s easy to become complacent—to settle for “good enough” and move on. But if your analytics implementations are off by an inch, then your data is off by a foot, making your data-driven decisions off by a mile.

You have to keep an eye on every part of the process—from your analytics solution implementation to data collection and internal dissemination to driving strategies—to make sure the data retains its integrity and is always available to the right people so they can make the right decisions.

Data integrity is a process that requires  long-term vision to create, implement and maintain in order to build a data quality legacy that can be continued.

In an  industry that has lived so long with the “good enough” mentality, practitioners often  develop an idea of what they  think data integrity looks like, but more often than not, their idea is far removed from reality—and the reality can be daunting.

According to Matt Gellis, Founder and CEO at Keystone Solutions, “You’re never going to be able to get anything done if you don’t just start. Grab a hamper—start the laundry.”

In his session at the 2016 Analytics Summit, “Data Integrity – Why Your Data May Be Wrong,” (video now available on-demand) Gellis will be taking a practical look at the true impact of data credibility issues on the cost of analytics within an organization, and how the problem is only magnified by non-credible external data sources.

Gellis further explains, “True data credibility is achieved through constant refinement of the entire measurement framework, but with so many variables impacting data integrity, it’s more important than ever to ensure your data is protected and trusted through correct testing, automation and expertise.”

To view Gellis’ session and to see the full agenda, visit:


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