Now there is, of course, considerable use of mathematical analytics, in many guises, right across the business world. Very clever people doing awesomely sophisticated things with huge amounts of detailed data to improve efficiency and effectiveness in every part of an organisation.
So how come, when it comes to figuring out how the overall business works and performs the way it does, executives are often reluctant to engage in basic numerical reasoning? I am not alone in thinking this is important – a recent Economist “Boss Class” podcast stresses the importance of “structured decision-making”, or working stuff out as we might call it.
What capability do we need?
We are not talking here about sophisticated math skills or the ability to do complex stats analysis or spreadsheet modeling. Rather, it’s just mental working-out of answers to simple questions. Examples:
- If we cut price by 5% to win X more customers per week, when will revenue get back to what it is now?
- How fast will we need to hire people if staff turnover is 15% and we want to grow staff by X?
- If we launch a new, simpler product, how many new customers, of what size must it win to double our revenue?
Every time such questions arise – as they always do when building digital-twin models, people try to move the discussion on without answering it. I remember giving one leadership team a worksheet for a few key questions about their strategy, and one guy commented “Wow, this is grown-up stuff!” To his, and the team’s credit, they did knuckle down and get it done, with great improvements to their strategy.
Why won’t people do it?
Reflecting on events like this, I don’t think it’s because people can’t do numerical working-out. It’s more that they don’t want to do it. Somehow, it seems, they learned at school that they “can’t do maths” so are scared of trying.
(This issue, by the way, is not at all limited to business issues. We can see hopelessly dumb working-out failures across all of public policy and – tragically – on the existential issue of climate change. Strategies to do things that simple numbers show are totally dumb, and choices not to do things that simple numbers show to be no-brainer great options.)
What to do?
Now I am blessed with a high level of numeracy – I really know how to ‘work stuff out’, in spite of having no higher maths skills. And reflecting on what that skill is, I think it’s a combination of, yes, being able to do basic calculations, but that is combined with strong logical reasoning. “What exactly is causing A to change like that?” .. “What happens to X if Y changes like this?” .. “If we decide to do X, what else will change, how much, apart from those outcomes that we want?”
So what I have learned is to be very patient with folk who don’t share this capability. That means – when our projects really need to work stuff out – taking people through the logical reasoning and simple calculations step by step. Like the case above, it often turns out the they can do it, and appreciate the value of actually doing it.
Good news – this huge challenge has been more widely recognised, and great resources are out there to help, at every level of education and beyond. For example, an educator working with home-schooling students just pointed me to DatasciencePrograms.com. This source does target, ultimately, high-level analytical skills, but pays strong attention to the earlier steps on the path – basic data literacy, or numeracy.