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A Decision Architecture Whitepaper – Part 1

By June 23, 2017Articles

From the Editor: This new article comes to us from David G. Ullman, who specializes in Design Process, Decision Making and Aeronautics Consulting.

Have you ever wondered about the process, structure, information and implications of decisions? They’re being made constantly, all around you, and yet most don’t stop and think that there is a better way to look at these inflection points, to understand how to ensure that the right decisions are being made, and how you can validate not only that the right choice has occurred, but also how you can measure the outcomes.

This is the first in a two part white paper focusing on Decision Architecture, a much ignored but critically important aspect of change within organizations, which puts it firmly within the sphere of Enterprise Architecture.

Here’s a quick excerpt from the paper, which is entitled:

“What Is Decision Architecture And Why Is It Important to Making Agile, Acquisition, Gap Resolution and Other EA Decisions.”

Decision Architecture Background and Definition

Enterprise Architecture (EA) is the practice of planning, analyzing, designing and implementing business strategies. It requires the analysis of complex structures and processes under potentially uncertain operating conditions with the goal of helping organizations make the best possible business, application and technology decisions. Over the past twenty years the EA community has built many methodologies aimed at understanding and managing system complexity and business alignment.

During each phase of architecture development and application, EA success is a direct function of the alternatives considered and the decisions made. While EA methods are strong on developing models, they are generally weak from a decision architecture viewpoint as is shown in the follow-on white paper.

Decision Architecture sees modeling, analysis and information management as needed activities that support the decision-making process. This process, whether automated or human based, requires the comparison of multiple alternatives relative to identified measures with the goal of either choosing the best alternative, ranking the potential alternatives, or deciding what else to do next to improve the likelihood that the best choice can be made. This applies equally to large acquisition decisions and small agile decisions.

There are two broad classes of decisions made in EA: structured and unstructured. Structured decisions are made in situations which are fully understood and can rely on deductive reasoning: If-then-else. Many of the decisions made during the EA process can be reduced to business rules and structured. Unstructured decisions, by contrast, rely on abductive reasoning, the testing of hypotheses to discover what is true. Unstructured decisions require human intelligence to create and manage the uncertain and ambiguous information, and associated risks.

Where structured decisions are generally made for routine, operational tasks, unstructured decisions are made for one-off tactical or strategic situations. In EA, structured decisions are generally reduced to business rules. Much effort has put into the development and management of business rules. However, the critical decisions in EA are generally unstructured.

To continue reading, click on the button below to download the full whitepaper. Part 2 will be released soon, but for now, we hope you enjoy reading this first installment. Don’t forget to sign up and comment if you would like to discuss this material, or any other articles published on the EAPJ website.

Download Part 1 (PDF)
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