This article from the Harvard Business Review was a short but interesting read. The article talks about defensive and offensive data management strategies – defensive being about minimizing downside risk, and being more common in regulated industries, whilst offensive data management strategies focus on supporting business objectives and are more common in un-regulated or less regulated industries. The authors rightly point out that the correct mix of defensive and offensive strategies will be specific to each individual organization.

Having worked in a number of commercial sectors, my experience is that the use of predictive analytics within organizations also divides into defensive and offensive activities, irrespective of whether that predictive analytics and data science activity is enabled by a well thought out data management strategy. There are good reasons for this, and again it is largely determined by whether or not the activity of an organization carries a large downside risk.

Consider a company, such as a bank, whose activity has a large downside risk, and where losses due to bad loans can almost wipe out a balance sheet very quickly. My experience of doing analytics in a UK retail bank is that the predictive analytics focus is on modelling that downside risk with a view to understanding it, forecasting it and ultimately mitigating against it. The analytics effort focuses on risk minimization (still an optimization), whilst optimization of the profit side of the P&L is less computationally based, e.g. by committees of human subject matter experts deciding mortgage or savings rates for the coming year.

In contrast, in companies where the downside risk is lower, such as those where transactions with the organization’s customers are on much shorter timescales than a bank, then the use of predictive modelling tends to focus more on the optimization of revenue and profits, rather than minimization of losses from liabilities. Take grocery supermarkets, where predictive demand models are used to set product prices in order to optimize profit. Whilst getting the pricing strategy wrong will impact revenues, it does not lead to the organization holding a long-term liability and is ultimately reversible. Mistakes when using predictive models in this domain are unlikely to take a company down.  

From what I have seen the use of predictive modelling within a business is typically almost binary, i.e. either predominantly on the downside risk, or predominantly on optimizing the business objectives, even though most businesses will have both upsides and downsides to their activity. I haven’t seen that many medium-scale organizations where predictive modelling is used at a mature level across the majority of the business areas or tasks. Even rarer in my experience are situations where predictive modelling of both downside risks and business objectives are done concurrently with the optimization taking into account both sides of the P&L. It would be interesting to find good examples outside, say the largest 50 companies in the FTSE100, DowJones, Nasdaq, or S&P500, where a more joined up approach is taken to using predictive analytics for optimizing the P&L.