How to Get Forecasting Right

Do you understand the distinction inbetween precise forecasting and reliable forecasting?

Technology forecaster Paul Saffo says precise forecasting is almost difficult to do. But if you objective for reliable forecasting, he keepsinmind, then at least you’re thinkingabout the complete variety of affordable possibilities for the future, rather than leaping to one conclusion and preparing just for that result. “If you head straight towards precise forecasting, you might end up in a much evenworse circumstance by ignoring things that ultimately come to pass,” Saffo argues.

In this episode, he shares his 6 guidelines for reliable forecasting—from envisioning future unpredictability to critical patterns in past information—and discusses why it’s crucial to be your own worst critic of your projections.

Key episode subjects consistof: method, tactical preparation, danger management, choice making and issue fixing, forecasting, innovation. 

HBR On Strategy curates the finest case researchstudies and discussions with the world’s top company and management specialists, to aid you unlock brand-new methods of doing service. New episodes every week.

• Listen to the complete HBR IdeaCast episode: Six Rules for Effective Forecasting (2007)
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HANNAH BATES: Welcome to HBR On Strategy, case researchstudies and discussions with the world’s top service and management professionals, hand-selected to assistance you unlock brand-new methods of doing company. Do you understand the distinction inbetween reliable forecasting and precise forecasting? Technology forecaster Paul Saffo states precise forecasting can be almost difficult to do. But if you objective for efficient forecasting, then at least you’re thinkingabout the complete variety of affordable possibilities for the future.

In this episode you’ll discover Saffo’s 6 guidelines for reliable forecasting – from picturing future unpredictability, to critical patterns in past information, and why it’s essential to be your own worst critic of your projections. This episode initially aired on HBR IdeaCast in July2007 And simply a note – we taped this by phone. While the audio quality is not excellent, the discussion is. I believe you’ll takepleasurein it. Here it is.

ANNOUNCER: Hello and welcome to the HBR IdeaCast from Harvard Business Online. In this week’s episode, Cathy Olofson talks with Silicon Valley-based forecaster Paul Saffo about his post in the July/August concern of the publication, “Six Rules for Effective Forecasting.”

CATHY OLOFSON: Hi, I’m Cathy Olofson from Harvard Business Review, and I’m here today with Paul Saffo. Paul is a veteran Silicon Valley innovation forecaster and the author of “Six Rules for Effective Forecasting,” which appears in the July/August concern of Harvard Business Review. Thanks for signingupwith us, Paul.

PAUL SAFFO: Pleasure to be here.

CATHY OLOFSON: So, in the shortarticle you state that executives oughtto objective for reliable forecasting, not always precise forecasting. I would have idea they were the verysame thing.

PAUL SAFFO: If you’re extremely fortunate, they can be the exactsame thing. But it is possible to be efficient even when you’re not precise. Accurate is extremely, extremely hard and at times difficult, simply because of the unpredictability of whatever circumstance is that you’re dealingwith. If you head straight towards precise forecasting, you might end up in a much evenworse circumstance by ignoring things that ultimately come to pass. So, for me, efficient forecasting suggests, rather than racing to a particular response, you’re looking at the complete variety of unpredictability. Effective forecasting is all about understanding the unpredictability that lies ahead, and not hurrying to a conclusion.

CATHY OLOFSON: So in the shortarticle you have 6 quite fascinating guidelines, and I idea we might go through those one by one. Principle number one is specify a cone of unpredictability. What do you indicate by that?

PAUL SAFFO: By the cone of unpredictability, it’s a typical sense method of imagining the unpredictability that lies ahead. As you’re standing in a offered minute in time, if somebody states, well what takesplace in 2 minutes? You can mostlikely specify a quite narrow cone of unpredictability. As time goes outwards, the unpredictability endsupbeing bigger. You end up with a cone-shaped area. And what you truly desire to do when you make an reliable projection is to draw that cone in a method that it incorporates all of the sensible possibilities that lie ahead. And keep that in mind as you’re working on your projection.

CATHY OLOFSON: And guideline number 2 is appearance for the S-curve.

PAUL SAFFO: Well it turns out whatever intriguing is shaped like an S-curve. Very coupleof things unfold in a direct method. They rather unfold extremely, extremely gradually at the start and then ultimately a crucial point is reached, and whatever begins altering quickly. And it ultimately settles and looks like an S-curve. The mom of all S-curves is the curve of Moore’s Law. That law explained by Gordon Moore in the mid-1960s when he keptinmind that the number of circuits we might put on a chip was doubling about every 12 to 18 months. And if you draw that out over time, it looks like an S-curve. So far it still looks like a hockey curve. But the bottom line is, it completely explains the entire digital transformation. It is why, for example, your watch– if you still wear one– has more computing power on board than existed in the whole world before the early 1970s.

CATHY OLOFSON: It’s intriguing here. You state that most concepts, even in Silicon Valley, take 20 years to endedupbeing an overnight success.

PAUL SAFFO: Yeah. As a forecaster, one guideline I live by is neverever error a clear view for a brief range. Just because it looks like something is actually interesting and about to occur, opportunities are it’ll take a lot longer to showup than you anticipate. And when it lastly does showup, it will unfold in unforeseen methods. And it’s real onceagain and onceagain and onceagain here in Silicon Valley. Quite just, most concepts do take 20 years to endedupbeing an overnight success. TELEVISION took 20 years from development in the 1930s plus time out for a war before launch in the 1950s. Radio took 20 years from its veryfirst creation to the broadcast of station KDKA in1921 The individual computersystem’s antecedent– it took the individual computersystem practically 20 years before it had departure. And of course the web was practically precisely 20 years old when it took off in the year that Al Gore created it, 198