How Much Can Predictive Analytics Help Business Continuity?

If you can see what will happen in the future, you can take steps to prepare for it – or avoid it, or even change it. That’s the promise of predictive analytics, a topic that naturally interests business continuity managers. While there’s no guarantee of exact predictions, predictive analytics can indicate change patterns and emerging trends. Sensibly constructed models can show areas of combined high uncertainty and influence, where particular attention should be paid in preparing to ensure continuity. However, predictive analytics as such fall short in two areas related to business continuity: one of them can be ‘fixed’ by using a similar approach, whereas the other cannot.

The first are is the evaluation of immediate or short term actions to be taken. This branch of business modelling is sometimes referred to as decision analytics. By using similar modelling techniques and ways of dealing with uncertainties, different options for action can then be evaluated along with the probabilities that they will yield the required results.  The second area is the unforeseeable event or so-called Black Swan event. Such events are game-changers: the arrival of the Internet is one example.  In this case, organisations need to remain alert and ready to investigate different options when such a Black Swan event is identified.

How then can you perform predictive and decision analytics? Theoretical tools abound: just browse a few operational research books or websites to see what’s on offer. Computer-based tools also exist. In particular, spreadsheet applications like Microsoft Excel with their ‘what if’ scenario testing capabilities provide a solution. The potential drawback with Excel models is that when they become complex, more errors creep in. Governments, banks and eminent economists have all been caught out in this way, sometimes embarrassingly so.  However, other software exists specifically designed for such modelling and analytics, and which make it easier for organisations to avoid making incorrect predictions for their business continuity.

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