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…
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…
… and your data warehouse / data lake / data lakehouse. A few months ago, I talked about how nearly all of our analytics architectures are Stuck in the 1990s. Maybe an executive at your company read that article, and now you have a mandate to “Modernize Analytics.” Let’s say Read more…
This week we continue our Data Product dissection by looking at the data itself. What could be more fundamental? After all, it’s in the name! Data Products come in three flavors: Foundational, Composed, and Packaged. It’s important for providers to understand these different types because they are conceived, implemented, and Read more…
It’s amazing how quickly Data Product joined Metadata, Data Quality, and Data Governance as a functionally useless term. Consult ten different sources and you’ll get fifteen different definitions. A Data Product is variously defined as: The theory of applying a product mindset to data sets. Any tool or application that Read more…
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…
With increased recognition that the new AI models and applications need high-quality, well understood data, Data Products are enjoying renewed attention. The concept has been fleshed out in recent years with definitions, reference architectures, and platforms. They consist of …. Actually, let’s not worry about what Data Products consist of. Read more…
I could stand to lose a few pounds and I know what I need to do: watch my diet and exercise regularly. Knowing, of course, is not the same as doing, and the doing takes effort. Maybe a spoonful of branding and marketing sugar will help the medicine go down. Read more…