Why Architecture Matters for Data and Analytics – Part II

When it comes to Data Architecture, there’s a lot to consider. In this blog, we’ll look at Gap Analysis and Impact across architecture landscape.

If you missed it, read our first installment on Data Architecture – where we demonstrate how Data Architecture provides an understanding of what data exists, where it’s stored, and how it flows throughout the organizations and/or systems. The article also shows how understanding system mapping, connection, and configuration helps formulate and deploy a data architecture that fits your company’s objectives.

If you’re caught up with Part I in this series, you should have no problem applying what we cover here. So, let’s move on to Gap Analysis and Impact across architecture landscape.

Key Considerations for Data Architecture

Gap Analysis

When an organization undertakes a data-driven project, you must validate the architecture prior to full adoption. A gap analysis compares the baseline against the target architecture for shortfalls that were omitted or not defined. An architect also steps back at this stage to determine if any gathered requirements could be considered too lofty or unworkable goals.

At this point the principles, objectives, and constraints of the business validate the adopted model. What does this mean in simple form? The model examines what gaps exist from a business or data domain. The figure below represents potential gap sources to look for:

Adopt a holistic approach prior to making a decision on the appropriate model from the views already created as the target architecture. Avoiding a siloed view ensures the solution covers the proposed area being researched and into the wider picture. To illustrate, ask this simple questions – Storage capacity match and requirement for growth? Another is granularity monitoring – Does the model allow for predictability trend analysis? If this is the final model, can it be consistently maintained and/or monitored appropriately by the current knowledge-base?

Impact Across Architectural Landscape

The final phase in this process is analyzing and understanding the overall impact of the chosen model. You will remember, data architecture should align with the strategic vision of the business, thus creating a seamless experience. This requires an intimate alignment with the organizational goals, business process, and people. Using the metrics and measurements created, you can understand the possible business and landscape impact resulting from the model adoption. The figure below is a depiction of how to ensure this blueprint fits into the overall strategy.

At this stage we consider the impact of the model on existing systems. Plus, we look at how other entities in the organization will affect this model. Remember, the impact analysis should not only be limited in the context of the current state, but visionary. Working with the business groups, you must bring to life both states as it relates to the model you plan to adopt.

In our final installment, we will deep-dive into each of the diagrams depicted here and extend our discussion into each of the components contained therein.