5 ways to critically evaluate a trend or forecast

Guest editorial from Adam Gordon, a strategic foresight specialist and the author of Future Savvy. Business leaders, especially those of us interested in technology as a way of propelling business, are often inundated with information about the next “big thing”.  Existing in the blogosphere are herculean chunks of information about upcoming tools on the technology tarmac that will be changing lives and jobs in the next decade. Of course, being the tech savvy crew we are, we understand the advantage of living on the cusp of technology.  As a business driver, technology advances have certainly increased efficiency and indeed made “buy-in” to technology a must for anyone wishing to compete, regardless of industry. And there is much to be said for being an early adopter, especially when the early adoption leaves the competition to play catch-up. But, along with the spot-on forecasts, there are many trend predictions that are hailed as credible when they in fact, have no merit at all. So how do we know when a predicted trend or forecast has value?  There is no cast-iron system for trend evaluation, but there is a set of criteria one can follow to help discern a quality forecast from a baseless guess or worse, a source with a hidden agenda. Here are five keys to unlocking trend quality:

1. Understand intentions. There are dozens of ways to classify forecast materials, but the most useful way of categorizing them is by stated or implied intention, in other words, why the forecast has been made in the first place.  What can be gleaned about why it exists, who put it out, or what the intention of the forecaster was? Is the forecast upfront about its purpose? 

   All forecasting is done for benefit. By recognizing the interests at work behind a forecast, one can make a better judgment as to potential strengths and weaknesses. We may ask, what action or concerns is the forecast trying to arouse? How is it legitimating a view that the forecaster or forecast organization holds?

2. Check the data is real. Data is never as solid as it seems. Among the problems are validity of definitions, validity of sampling, how research is skewed by the form of questioning, and so on. A particular problem in forecasting is that sometimes data points used in discussion are not real recorded figures but “future” data points that have been projected from past data, which raises obvious questions about how this projection has been done and how valid the process is. A good forecast will carefully distinguish real data from projected data.
3. Be critical of insiders and 'experts'. The “expert” spearheading a particular future study may not be the most intuitive thought-leader when looking at forecasts. Experts are necessary in a specialized world and expertise and credentials are important in forecasting, but experts are wrong as much as anyone. This is because a field’s experts are particularly likely to be heavily invested in the status quo, and be expert precisely in its existing procedures, attitudes, and prejudices. Change often comes from outside and experts – blinkered by their knowledge of today – are often the last to see it.
4. Beware of attempts to influence the future.  Forecasts fall into two main categories: future-aligning, where forecasters anticipate change in order to adapt early and successfully to it; or future-influencing, where forecasters are trying to influence events. Future-aligning approaches aim to be objective. They may fail, but the intention is there, so, on balance, this approach will be more accurate. Future-influencing forecasts aim to succeed on other terms – alerting and shaping opinion, changing minds, and harnessing action. Forecasts that are trying to lobby or change industry conditions make themselves known by seeking publicity, and often being a forecast of extreme optimistic or pessimistic outcomes (to be aspired to or negated).
5. Consider blocking forces.  All drivers of change work against the frictional resistance of the status quo—the systems and solutions that people are currently invested in and comfortable with. They also face direct ‘blockers’ and ‘turners,’ which are forces that have a vested interest in the status quo and don’t want to see change, or that have an interest in another type of change.

   A good forecast will assess the strength of resistance to change and anticipate specifically if and how this resistance will be overcome, if indeed it will be, and account for the resources required to achieve this. Rather than running with the breathless wow-of-the-new, the forecast will display a measured pragmatism in the face of constraints, and adjust the forecast direction and/or timing accordingly.