Crossing the Data Divide

Something has been bothering me about data products. It’s the point at which the consumer of a data product immediately switches to also being a producer. We talk a lot about the binary roles of either consumer or producer when, in reality, the vast majority of people are both at the same time. I know data product and data mesh purists may argue that data products are more formal and it should not be possible for someone to easily create a derivative set of data from origina

We believe data is a critical corporate asset, right? Of course, but you would expect that, after all, we are the data people. We also see a lot of business leaders’ heads nod when the cliches ‘data is the new oil’ or ‘data is a core corporate asset’ are used. I want to test this, almost reflexive, response by drilling down and examining what that means compared to other corporate assets.

Something interesting has been happening to me over the last few months that I’ve not experienced in a while. Smart and experienced CIOs and their data leaders have been asking me for input regarding how to sell the value of a data program. The question is a clear sign of the economic times where every dollar is being heavily scrutinized. While it’s about as fun as going to the dental hygienist, overall, I think it’s refreshing and healthy to sharpen up our vision, value p

As a career data professional and having spent most of the last 10 years with Collibra, Alation, and now as an independent, I’ve come to a somewhat painful, yet exciting realization that large language models (LLM) will completely disrupt and displace data cataloging as we know it!

The focus of my last column, titled Crossing the Data Divide: Data Catalogs and the Generative AI Wave, was on the impact of large language models (LLM) and generative artificial intelligence (AI) and how we disseminate knowledge throughout the enterprise and the future role of the data catalogs. Spoiler alert if you have not read it: My position is that they are complementary, but the catalog and its proprietary user interface cease to be what we try to get users to adopt. 

If you haven’t heard about metrics stores yet, they’re “newish,” so you likely will. They are interesting to an extent, but mostly, they feel like a late-night re-run and remind me that data work is hard.  So, what is a metrics store? Most of the young vendors trying to create this category will tell you that it’s a unified way to define and manage business metrics and abstract the confusion and sprawl of layers of BI tools into a common semantic meaning. It sounds like a