In my first post on Data Readiness, I introduced the notion of a Data Readiness activity to provide the AI development team with the political power to make the resources (typically data and subject matter…
In my previous post, I outlined five key pillars: Scope, Compliance, Trustworthiness, Understandability, and Cost. While these pillars provide a design framework, moving an AI application from an interesting experiment to a production-grade tool is not a…
Scientific data includes context information (metadata) to make its creation reusable in downstream analysis. This metadata comes with a vocabulary that is helpful in understanding how business data – that is data from the systems…
It’s widely recognized that data preparation is critical for AI projects. But where does it actually fit in the development process? In my previous post, AI Application Development – Building with Context Intelligence, we explored…
From Planning to Building In our last installment, we discussed project definition and the context and clarity it can provide. In this installment, we’re going to jump into the part that most techies hanker for—the…
December, 2025 Dear clients, partners, and friends, As we reach the end of 2025, our fourth year of operations, it is once again time to reflect on our achievements and learning over the past year,…
Many hopes and dreams are being fueled by the wonders and hype of AI systems. Yet those of us that have been through these technology-hype cycles before recognize the inevitable. AI technologies will help in…
Mandy Chessell Next steps in digital products … PDR’s research in to data products, and the infrastructure needed to support them, continues. Our original white paper , makes two points: Recently we have been focused…