Decision making in business is 80% gut and 20% fact. Facts and data are important. Business plans, forecasts, ROIs, etc. are all critical parts of decision making. But, breakthroughs, by definition, cannot happen based on facts. If the future is predictable and can be modelled, then we can’t have breakthroughs.

In our pursuit of these factual information, often to present to the management or other decision makers, we tend to miss this simple fact – the output is dependent on the input. Or simply put – Garbage in, Garbage out.

We all know how underestimated the market potential of computers or mobile phones were. I worked for a company in Singapore that was bidding for a mobile licence. We had a great foreign partner, the leading mobile operator then. After going through all the numbers and the business case, our partner decided that the company would not be viable. Today, we have three mobile operators in Singapore and doing reasonably well, thank you.

While heading a product program a few years back, I needed to include a business modeling tool in my sales pack. I looked amongst other units in the company and found that we had an expert in this field. In fact, he had been working on a business modelling tool for a while. I found he had a wonderful excel tool with over 10 sheets, 100s of input parameters and complex calculations. He had used the model in a number of cases and was proud of the level of accuracy in the predicted outputs.

I picked it up and played around with it for a while. And decided to apply it to my own needs. That is when I found that the majority of inputs needed to be estimated. What would be the take up of the service ? How long will consumers use the service, etc. I could make a guess based on other reference services, but it would remain exactly that – a guess !!

So, I had this wonderful tool that churned out some great outputs based on rather shady inputs. Essentially, I was getting accurately wrong outputs. And for my customers to make their decision, these outputs contributed only to the 20% part of the decision making. What they needed were sensitivities – the effect of changes in input assumptions, not necessarily the exact single point solution.

In short, they needed information that was approximately right rather than accurately wrong !

And this might be useful to remember when in an heated conversation with spouse. You don’t want to lose an argument by being accurately wrong 🙂