Following data quality best practices requires establishing metrics for the cost of errors so that your business can approach customer data analysis strategically. Both false positives—the linking of unrelated records—and false negatives—leaving related records unlinked—can carry a substantial cost for your business’s reputation as well as unrealized marketing opportunities. Uncaught errors…
Black Oak Presents at the International Conference on Information Quality 2016 in Ciudad Real, Spain
In the news so far this summer we’ve been reading about the 2016 Olympics in Rio. While Black Oak hasn’t yet been able to send any of our team members to the Olympics, we do have our CSO Dr. Talburt in Ciudad Real, Spain for the next few days attending…
Black Oak’s HiPER: Data Quality on Big Data
Black Oak recently did two things: We sent our CSO Dr. Talburt to the White House to participate in a round table of 80 industry thought leaders who were discussing how to improve data quality in open data. We also just published a blog about how Collection is Not Enough;…
Dr. Talburt Is Heading to the White House
On Wednesday, April 27, the White House Office of Science and Technology Policy and the Center for Open Data Enterprise are co-hosting an Open Data Roundtable on Improving Data Quality and they have invited Dr. Talburt. This discussion is the second of four Open Data Roundtables being scheduled this year,…
Quality vs Quantity: What Matters Most in Analyzing Enterprise Data?
By Dr. John R. TalburtIn academia we say both quality and quantity are equally important to enterprise data. In business we would say, “It depends.” However, the real answer is that this is a false choice like the question of “what happened to the missing dollar?” in the bellboy story….