What company doesn’t want to earn more revenue from information assets that they’ve spent money to accumulate and are spending more money to maintain? The book, Monetizing Your Data, describes the authors’ methodology, [p. 8] named Decision Architecture, that is designed to exploit data analytics to support decision-making that actually produces revenue. The authors’ goal is hugely valuable because data analytics often produces interesting analysis that is not actionable or delivers disappointing results.

This methodology is designed to tie together a set of disciplines, methods, tools, and skillsets into a structured process that delivers meaningful insights for decision-making. I’m a fan of methodologies because they tend to lead to more comprehensive and rigorous deliverables that reduce risk and increase benefits.

The Decision Architecture Methodology covers a large number of topics that will be useful for data analysts and data scientists to consider as they construct data analytics to support decision-making that actually produces revenue. These are explained to varying degrees of detail in the 325 pages of the book. Much of the book is very practical while some parts are more academic. The book includes a number of case studies that illustrate the concepts quite well.

The problem with methodologies is that they are often intellectually demanding for the end-user. This methodology is no exception. For example, the book describes 12 guiding principles and how to address them in considerable detail. Producing actionable recommendations from data analytics requires thoughtful analysis of many data sources. Simplifying the methodology or short-cutting the process greatly increases the risk of inaccurate recommendations and disappointing results.

This methodology definitely focuses the work of the data analyst on:

  1. Producing revenue.
  2. Avoiding the trap of becoming enamored with the analysis itself.
  3. Ensuring the analysis is comprehensive and defensible.

However, we generally want to reach important recommendations with less effort and less thought. This methodology is not for those looking for a simple, dare I say simplistic, recipe for success. Perhaps the authors make this same point more diplomatically when they say: p. 4: “It is no longer acceptable to equip organizational leaders, managers, and analysts with one-off training courses and conferences, expecting them to make quality decisions based on limited knowledge and gut feel.”

I think end-users of methodologies are interested in a defined series of phases with each phase consisting of a well-defined number of steps or deliverables that all contribute to the goal of the phase and ultimately contribute to the goal of the methodology. I believe this methodology falls somewhat short of this definition of a methodology because some of its steps and phases are not sufficiently crisply defined.

All methodologies rely on diagrams to explain their concepts and components to end-users. The diagrams of this methodology exhibit:

  1. Circular arrows that make it difficult to know how or when you’ve reached the end of a phase.
  2. Steps that are not linked to either other steps or to subsequent phases.
  3. A phase order that’s not clear.
  4. Multiple paths through the methodology with no indication of which path applies under which circumstances.
  5. Missing steps that are revealed only by their descriptions in the text.

You can read articles related to the book, Monetizing Your Data, and learn more about the authors at this website. Naturally, there’s a link to Amazon where you can purchase the book.

What is your experience with using analytics that is focused on earning revenue from the information assets for your company?

Monetizing Your Data – A Guide to Turning Data into Profit Driving Strategies and Solutions

Authors: Andrew Wells and Kathy Chiang

Publisher: John Wiley & Sons, Inc.



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