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Book Review: Knowledge Automation: How to implement Decision Management in Business Processes


Some time ago I got a pre-release copy of Knowledge Automation: How to Implement Decision Management in Business Processes, Alan Fish’s new book on the analysis and design techniques of decision management. I was delighted to write a foreword for Alan and with the arrival of a printed copy I wanted to extend this with a review. Alan’s book lays out the core analysis techniques you need to model and manage decisions. I use these techniques in my decision discovery work with clients and wove them into the approach I describe in chapter 5 of Decision Management Systems.

The book begins with an overview of the knowledge economy, why systems need to embed knowledge and why decisions matter in this context. Having established a clear case for decision management he follows with an excellent discussion of the role of decisions and decision management in process management. Decision Management and Process Management go hand in hand and most business problems will require an effective combination. As Alan says, Decision Management involves more than just identifying operational decisions, you must also

codify the knowledge used to make them, and encapsulate the knowledge in automated decision-making systems

Alan shows that it is essential not to simply replicate what you do today, but to improve it. Using Decision Management to automate and improve decision making changes the processes of which these decisions are a part, making them simpler smarter and more agile. Alan’s focus on decisions as a means to drive process innovation is therefore particularly welcome. His hierarchy of a customer journey supported by a business process and a set of decisions is an effective model, especially when the decisions are implemented in decision services that encapsulate the decision making logic required. This chapter is full of good advice including some great discussion of roles in decision making and his emphasis of organizational issues and constraints is likewise central to effective modeling of decisions.

Chapter 3 gives a nice summary of the available technology and then the book moves into the core techniques of Decision Requirements Analysis and their application in building automated decision-making systems. The first of these focuses on decisions and decision services. As Alan says

Decision Services make decisions

which sounds trivial but is core to his approach and to my focus on Decision Management Systems. The decisions being implemented in Decision Services should be modeled and managed top-down and Alan works his way through an effective set of techniques to do this, covering both modeling and requirements gathering. The 3 kinds of information needed to make a decision – data, knowledge and prior decisions – are well explained and he makes great points about the interactions of processes with decisions and role of rules in defining decision logic and hence knowledge. A succinct and effective description of how to map all this analysis to design and implementation using a business rules management system and related technology follows. He wraps up with some useful decision patterns.

As I said in my foreword

I have been working in Decision Management for most of the last decade, spending much of that helping companies use business rules and predictive analytic technology to automate and improve business decisions. Alan’s approach to gathering, modeling and managing decision requirements immediately struck me as the right way to approach this problem. I have been using it with my clients ever since.

This approach works, which is why I use it, and if you are interested in building Decision Management Systems or doing effective decision-centric analysis before using business rules, then this book should be on your bookshelf.


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