This time-frame between understanding and effective action is vitally important because if you find out something that happened in the past, and even if you find out why it happened, you can’t change it, so a glorious opportunity is lost. By my definition above, BI is the process by which we can learn something in time to change the future. I’m going to call this time-frame ‘reality time’ and it can be anything from a few seconds or less to a few weeks or longer.
Let’s pause here and examine this last statement by using a simple example.
Suppose I’m a marketing guy in a mobile phone company and I learn that one of our valuable customers is likely to switch from our cellular network to another. Now that is a very important piece of information, and it gives me the opportunity to do something to stop this happening before it does. Reality time may be measured in days or weeks, and in this example is the time between someone thinking of swapping mobile providers to when they actually do. Reality time is the operator’s opportunity to change someone’s behaviour, a brief opportunity to reshape the future!
A very common example today is the time between someone having their credit card stolen, realising it’s been stolen instead of just misplaced, contacting the bank and having their card stopped. In this case reality time is probably the time between the person realising that they’ve lost the card to someone else actually using it to commit fraud. This time-frame is the time-frame that the bank has to stop the card and to change the future.
Now the difference between the two examples above is that the first is a very tough job to do, as we shall see later. It involves real BI deployment in the world of analytics and predictive modelling, whilst the second example is a no-brainer with little intelligence involved at all, in more ways than one!
One interesting thing about reality time is that it’s limited, it is often not in abundant supply, and in many cases it can be treated as an asset. Let’s look at a fairly morbid case. Let’s imagine a hospital with many seriously injured people, each with a certain expectancy of surviving the night. Let’s factor in the staff available and their skills and we will see that dividing reality time up in order to be most effective in the saving of life is indeed a pretty tough BI problem.