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Book Review: Big Data for Big Decisions


I got my hands of a copy of Krishna Pera’s new book, Big Data for Big Decisions recently. I met Krishna several years ago when he published some articles on being decision-driven not data-driven and on why it’s essential to prioritize decisions for your analytic efforts. He’d found some of my articles on being decision-centric and we connected. Now, one pandemic later, I’m delighted to be able to review the book that resulted from his experience in this topic.

The book’s subtitle is “Building a Data-Driven Organization” and it covers how to begin the journey, how to focus on the right (“big”) decisions, the challenges in getting value from analytics, data strategy and much more. His focus throughout is on building a robust roadmap and an enterprise-level plan. Crucially, he wants those establishing data-driven organizations to focus on the decisions that add the most value to the organization. This specific focus on decisions and on selecting the right decisions is key to the book and, indeed, to succeeding at becoming data-driven.

He encourages an assessment of your current state and the development of a coherent roadmap. A new operating model is going to be required to become insights-driven and understanding this new model will give you a sense of where to make strategic investments. He focuses immediately on explicitly assessing and improving decision-making (rather than asserting that better data will lead inexorably to better decisions as so many do). Furthermore, he emphasizes tying improvements in this decision-making to concrete business value.

His prescription for creating the organization begins, as it should, with a discussion of decisions and the importance of beginning “with the decision in mind” when considering data and analytics. He dives right in, pointing out that most organizations lack any clear understanding of their decisions, except perhaps for purchasing and investment decisions. They don’t really know what their decisions are, who has what role in those decisions, how those decisions are made or how they could be improved. To address this, he recommends an immediate investment in understanding these decisions by modeling them (ideally using the Decision Model and Notation or DMN standard).

His chapter on finding the “big decisions” of the title is particularly worthwhile. He provides some good insights on how to prioritize decisions – comparing high impact but rare decisions with those that offer low value per decision but very high volumes for instance. The key, he says, is to find a core set of decisions that offer your organization the most value. If you can make those decisions data-driven, you’ll realize most of the analytic value available to you.

He goes on to discuss the potentially elusive value of analytics in decision making. To address this, he encourages a focus on incremental improvement to known problems rather than pure research-oriented analytic projects. Then you can prioritize decisions based on both the potential for analytic improvement and the likely cost and complexity of data-driven improvement.

With the value of analytic improvements in decisions clearly identified, he transitions to discussing data challenges and an IT strategy to support data driven decision-making. There are many elements to such a strategy, and he does a nice job of outlining how these elements come together to support a data-driven organization. I particularly liked the discussion of an information supply chain and his ideas around mapping system and IT maturity to the data and analytic needs identified. He wraps up with solid chapters on data strategy, data governance and on data-driven marketing as an example.

One of the great things about Krishna’s book is that he cites a huge number of books, papers and articles, giving you a rich set of information to drill into. He also leverages established ways of documenting business, data and IT plans and strategy. He shows how these established techniques can be used to benchmark, analyze and re-design an organization to become data-driven and apply (big) data and analytics to critical decisions.

If you are trying to make your organization data-driven and striving to use analytics, machine learning and AI to improve your business decisions, this book should be on your bookshelf.

Here at Decision Management Solutions, we’re helping our clients become truly data-driven through decision automation. Automating and improving the most common decisions in your organization creates immediate business value because these operational decisions literally run the business. While some of our clients have already identified the best use cases for decisions automation, we often help clients with an assessment to help them prioritize their investments in decision automation and data infrastructure. Krishna outlines an approach very similar to the one we use – so I am confident it works!

Buy Krishna’s book to help you put a plan together and get in touch if you need some help!