A previous article established that the primary expectation of Data Product consumers is Reliability. This week we’ll look more closely at how we can provide that reliability. The five responsibilities of a Data Product provider are Content, Curation, Catalog, Certification, and Maintenance. Content: Data Products are created to satisfy anticipated and/or Read more…
You had blood drawn in the doctor’s office as part of your annual physical and just received a printout with a bunch of numbers. Cholesterol 142. Platelets 203. Glucose 93. And so on. And you probably believe them. Why? Do you even think about it? Consider all of the assumptions Read more…
From Day One, data warehouses and their offspring—data marts, operational data stores, data lakes, lakehouses, and the like—have been technological work-arounds. In Building the Data Warehouse, the 1992 book that launched modern decision support, Bill Inmon recognized the need for a decision support architecture that was different from the existing Read more…
Important lessons can be learned in one of two ways: the easy way or the hard way. I prefer the easy way, but when it comes to Data Quality, it seems like I’m in the minority. Last week we wondered aloud whether data quality really matters. This week we’ll see a Read more…
Maybe we’ve been going about this all wrong. It seems that everybody in the information management field continually emphasizes the importance of Data Quality, and everybody not in the information management field couldn’t care less. Maybe the latter group has it right. After all, product is flowing out and revenue Read more…
In a 1993 Seinfeld episode, Jerry describes the psychological leverage that doctors use on their patients. They tell you to go into the little room and take your pants off and wait. Then, “anybody that comes in with pants on seems like they know what they’re talking about. In any Read more…
Disruption is imminent. Is your company prepared? Your IT department? You? What do bank tellers, assembly line workers, retail clerks, blacksmiths, and travel agents have in common? They have all been largely displaced by technology. Application developers are next. In just a few years Generative AI will produce most of Read more…
For as long as I can remember, I’ve had a very simple metric to assess how serious a company is about its information management practices: The number of software releases delayed solely because the data has not been properly curated. I have yet to see a single example. Software releases Read more…
Everybody likes a pithy definition. Marketers describe them as “sticky,” meaning that they’re easy to remember. Of course, that doesn’t always mean they’re useful or completely accurate. Information Management has a couple. Metadata is almost universally described as “Data About Data,” but I’d be willing to bet that you rolled Read more…
For those who don’t want to click from page to page, here are all five articles in the Data Chasm series. In it, we explore the question of why we continue to see overwhelming numbers of analytics, artificial intelligence, machine learning, information management, and data warehouse project failures despite the Read more…