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Having worked in futures for the past 20 years, I am thrilled that Martin decided to document his extensive knowledge and proven experience. This book is a valuable resource for anyone looking to make sense of an increasingly complicated, volatile world. It’s a starter guide for the uninitiated, and a reference for those in the know.
Why? The future is impossible to predict - reliably at least! But having experienced (first hand and otherwise) my fair share of short-term, blinkered corporate decisionmaking, you cannot ignore the future. More people - especially business leaders - need to develop an anticipatory futures mindset.
With training, one can learn to pick up on the most important things early, and thus create additional time and space in which to plan what to do. The frameworks that Schwirn explains enable you to identify the big issues of tomorrow, today. And as he reminds us, we need to use this lead time wisely, to ensure actions are taken. ‘Small Data, Bid Disruptions’ will help you do all this.
Crucially, this book is a timely reminder that much of the supposed chaos we see in the corporate, geopolitical, and environmental landscape today is neither random, nor particularly surprising. At least, not for me.
Only rarely does a new business book appear that so perfectly matches the global moment. As we begin the recovery from the COVID-19 pandemic, a slow-moving global catastrophe that was both predictable and almost entirely unexpected, companies and organizations are (or should be) looking out at an even more uncertain world and asking themselves, “What’s next?” In Small Data, Big Disruptions, Martin Schwirn shows them how to anticipate and prepare for the future by finding “little oddities” in the daily avalanche of small data and connecting the dots between them in order to “identify emerging opportunities and foresee future threats.” The four-step foresighting process that Schwirn teaches here is called scanning. It is a well-established and proven method for gathering and analyzing information from an organization’s external environment (the author himself has conducted or directed scanning for more than two decades). But, to my knowledge, this is the first book that has laid out the scanning process in light of the challenges of the twenty-first century. More importantly, it is a clear, careful, and compelling guide to a very powerful tool for embracing uncertainty, understanding future issues, and arriving at the best possible strategic decisions. And Schwirn demonstrates convincingly that any organization can adopt the scanning discipline and get started today. The book presents a number of impressive scanning success stories. It is also filled with valuable insights gleaned from the author’s long experience at scanning. Scanning requires small data from “credible and diverse sources.” Just as importantly, it requires team members who are themselves diverse and open-minded. He cautions to avoid the temptation of shiny objects. Scanning is not about finding “golden nuggets.” Single data points about self-driving flying cars or geoengineering may be exciting but they are far less useful than less sensational but still unusual data points that can be woven into more powerful patterns. Small Data, Big Disruptions is, above anything else, a carefully drawn and easy to follow blueprint for a highly disciplined and valuable scanning process for your organization. In less than 200 pages it lays out clearly the whys and hows of scanning in order to navigate the future. If your organization is already doing scanning, this book will teach you how to do it better. If you are not yet doing scanning, it is probably time to get started.
This book perfectly captures the reasons why you can't tell the future from an industry report: It explains and illuminates through multiple examples how "scanning" enables the prediction of future events without having a large amount of structured data. Rather, it suggests a method of quantifying anecdotal evidence and individual news stories to triangulate those unstructured data points into a prediction of a new trend of behavior, long before these trends are obvious or quantifiable.
Through multiple examples, this book shows how the scanning process was able to identify, years in advance, many of the "black swan" events that came largely unexpectedly.