Nowadays most large companies have a future department or a forecasting method that they use or buy in to give them guidance for future strategy and planning. The question is however which kind of forecasting suits which company, which method is the right one and what can it do for you. If you want to find out about the different types of forecasting Future Savvy by Adam Gordon is a good place to start.
Whether you work in forecasting, are studying it, or want to help understand the limitations of an outsourced forecast, this is a book to help you through a tricky subject. Gordon takes a no nonsense, systematic approach to explaining forecasting - first of all clearly defining the different approaches from so-called future-aligning to future-influencing methods, to that age-old battle of the pessimists vs the optimists.
Having cleared the key basics, he moves on to one of the eternal challenges and stumbling blocks in forecasting - that your forecast is only as good as your data (working with statistics at university our motto was "lies, damn lies and statistics" ). Gordon also tackles the tricky question of quality of data, how good is your data and what are the typical traps to be avoided. From improper use of data to out-of-date data, there are many ways to make mistakes and perpetuate misunderstandings. It makes however for somewhat depressing conclusions - as you begin to wonder if there are any statistics or data sources that can really ever be trusted. As Gordon points out, "all numbers lie a bit, and some lie a lot".
But data itself is not the only problem to be addressed. He warns, with ample examples, about the many bias traps that also play a role in forecasting - such as sponsored forecasts, insider forecasts and technophile bias. But as if that isn't enough to worry about, Gordon moves onto " Zeitgeist and Perception: How we can't escape our own mind". This addresses the powerful (often problematic) influence of the Zeitgeist on forecasting as well as partly what Bruce Sterling calls the Futurists Monkey Trap - the parts of prediction that are down to the forecasters own mindset, peccadillos and little personal hang-ups.
But there is hope for the future of forecasting - if you can't entirely fight against bias or bad data, better to understand it, identify the problem and then learn to work with it. And so the book takes us on to more optimistic proposals for forecasting with a chapter cheekily entitled, "How it's better to be vaguely right than exactly wrong." If you have limited time, then Chapter 11, entitled "questions to ask of any forecast" is a good summary of the issues and leaves us with the reminder that "good forecasting is as much about seeing what won't change in the future."