Cloud Lighthouse Labs

"Ecosystem Map" is both one of the 25 dimensions of the AI Strategy Framework and a foundational concept in ecosystem-oriented architecture (EOA), which makes this article doubly important reading for strategic thinkers on both fronts. The “map” metaphor is instructive here. It is used to distinguish an ecosystem map from the various forms of architectural diagrams, nearly all of which tend to include more technical minutiae than a typical ecosystem map. Whereas an architectural diagram provides specific parameters for specific technical solutions, an ecosystem map presents a higher-level, more visionary view of an organization’s cloud ecosystem. This analogy is fundamental to understanding and practicing EOA.

We were working with eight to ten years between major disruptions from the dawn of the consumer internet. But these “wave periods”, that is, the time between the crest of two waves, have shortened to three to five years since the rise of the public cloud. It makes sense: As the evolution of computing technology and capacity picks up steam, it similarly accelerates. Innovation begets innovation. Generative AI was only made possible by the incredible computing power and connectivity available in the cloud. Now, AI is further accelerating this pace of change, shortening the time we have available before new waves crash upon the shore. The grace period for organizations to get their act together and position themselves for the next wave is growing much shorter; the margin for error is much narrower.

There’s an incredibly important transition in the broad information technology space that is often lost in the furor and excitement over generative AI. Simply “wanting AI” doesn’t cut it. So, the AI Strategy Framework begins with the Strategy and Vision pillar that sets forth five dimensions beginning with vision, extending to creating the actionable roadmap and architecture necessary to actualize that vision, and finally establishing the programmatic elements necessary to drive that vision to fruition. These dimensions help organizations formulate and take action on their big ideas.

We’ve learned a great deal about maturity and readiness for - and responsibility to the ethics of - AI over the past year, as well. It’s now time for a proper model through which organizations may realistically assess their readiness to adopt and scale artificial intelligence, and then identify specific areas to invest time, talent, and funding along their journey. This AI Strategy Framework guides organizations as they construct their AI strategy atop five pillars, each with five dimensions to be considered, matured, and regularly evaluated.

When taking on the question of how Power Platform integrates with Azure data services, Point-to-Point, Data Consolidation, Master Data Node, and Data Distribution evolve a similar theme. Specifically, each focuses primarily on transactional data during any given users interaction with it. “Data Distribution” is different, focusing more on data distributed for analytics, enterprise search, integration with third-party or external sources via API, data science workloads, or training or augmenting a large language model (LLM). This blog overviews the Data Distribution pattern.