The primary goals of beekeeping are straightforward. Make sure they have enough space. Make sure they have enough food and water. Control nasty bugs. And get them through the winter. Hives that make it through the winter are the most reliable honey producers the next summer. I prefer to harvest Read more…
Do you gravitate toward the most difficult problems? Do you seek out the most interesting and impactful challenges? If so, then 6 Secrets for Delivering Impossible Projects will give you the tools to successfully deliver your own Impossible Projects On Tuesday, October 29th, family, friends, and colleagues gathered at the Read more…
Data Value. OK. Hot topic. I’d be remiss if I didn’t say something about how data is the new oil or the new bacon or the new hot fudge lava cake with hand-churned vanilla bean ice cream drizzled with chocolate caramel sauce. Got it. So, let’s see. I have this Read more…
What’s not to love about a good enterprise analytical data consolidation project? You can just see the PowerPoint slide, can’t you? On the left, boxes like stars in a clear night sky, each representing some number of analytical repositories or data sources. On the right, one nice big box. Neat, Read more…
Part 1 of this Data Product Architecture series covered the “where” of Data Product data implementation: federated or consolidated or some combination of the two. Here in Part 2, we’ll get into the “how” of Data Product data implementation: virtual or materialized or some combination of the two. Which to Read more…
Awesome! You’re moving forward with a modern analytics architecture built upon Data Products. Maybe you’re going to implement a Data Mesh and/or Data Fabric. Maybe you’re just looking to make your enterprise analytics more scalable and sustainable. Whatever the reason, you now have a couple of architectural decisions to make; Read more…
Through the years I’ve done several surveys. Sometimes it’s collecting business value. Sometimes it’s connecting talent and need. Sometimes it’s inventorying tools. Sometimes it’s understanding resource utilization patterns. Regardless the purpose or venue, I have a very strict rule that applies to every question in every survey: Before the question Read more…
One Saturday afternoon in the late-1970s, I was watching the Pittsburgh Pirates on the MLB Game of the Week. Partway through the game, rain started falling at the stadium. Umbrellas sprouted in the stands, but play continued, the players seemingly unaffected. A few minutes later the rain appeared to be Read more…
I hate paying more than I have to for something. And I really hate paying interest, whether it’s on a credit card … or a loan … or Data Debt. Data Debt is the accumulation of unresolved data-related issues and unfinished information management activities. Think shortcuts taken in the interest Read more…
Great question. After all, a Data Product Provider is responsible for: producing the content, ensuring its availability, and validating its correctness collecting the metadata and publishing it through the Data Product Catalog responding to questions and enhancement requests Or, more succinctly: content, curation, catalog, certification, and maintenance. Along the way Read more…