Why data driven forecasting is vital, even in uncertain times.
Every organisation in the world relies on forecasts of one sort or another. For many, this involves collating last year’s data in a spreadsheet and applying some sort of multiplier. This might then be refined using individuals’ judgement and intuition, followed by a debate (and/or argument) with other teams.
However well-intentioned, such output is very likely to reflect the unconscious biases of those who created the forecast – and perhaps the very conscious desire to underplay a forecast so that they subsequently look good… And it’s neither a quick nor a simple process.
The alternative is predictive analytics: using data science to provide a rigorous, data-led forecast by extracting as much information from historical data as possible. This approach can generate robust, unbiased and accurate forecasts, which are a fundamental requirement of making good decisions. Without a clear understanding of what the future is likely to hold it’s extremely hard to choose how to allocate resources, anticipate bottlenecks or efficiently plan investment.
Of course no forecast is ever perfect, and no matter how advanced your analysis there is always an important role for experience and expertise. The value of a good forecast is that it’s a reliable baseline against which to plan activity, allowing you to anticipate challenges & opportunities in sufficient time to be able to prepare accordingly. At a broader level, the value of having a forecast that is trusted throughout an organisation is that it empowers individuals and teams to make their own proactive decisions (and removes the excuse of ‘well, we couldn’t foresee that happening’).
Forecasting in the time of coronavirus is especially challenging, and in certain sectors near-impossible. It is certainly true that the accuracy of all forecasts has decreased, but the benefits of a predictive analytics engine like Skarp remain. We have always incorporated external data sources into our model, and have now expanded that to include everything from the number of Covid-19 cases to measures of traffic & public transport flows. If anything, the post-coronavirus world is even harder to understand, thus strengthening the case for using data science techniques in generating forecasts.