Big numerical data about how we will act in the future does not exist. How to make better business decisions when working with qualitative, incomplete and highly uncertain data?
This is the third in a series of five blog posts on future-proofing a company's strategic decision-making. If you want to start from the beginning, please check out part one here. The remaining posts will be published in January – stay tuned.
I’ve recently worked on two particularly interesting cases involving qualitative data modeling. The first one answered the question, “Which strategic actions to take – no matter how the future will unfold?” Our customer in this instance was a leading payments provider operating in Europe.
The second case I’ve already used as an example in parts one and two of this blog series. It was done together with three Finnish ministries, and the question was, “How to create a thriving digital business environment and equally distributed well-being in the EU?”
This third post will also focus on the EU case. A key part of that case was modeling uncertainties – such as dominant discourse in media, leadership, competitive and business environment, values and technology – into distinct scenarios.
But while doing that, we didn’t just calculate different scenario descriptions of how the EU will look like in ten years – we also identified what uncertainties have the biggest impact on the desired outcomes. The work concluded by identifying actions to target these critical uncertainties, such as concept renewal for the EU competition policy, or deregulation.
In business contexts, the idea of mathematical scenario modeling is to generate insights into where your company should focus its development efforts in order to create maximum impact – e.g. more revenue, improved employee wellbeing, environmental safety or happier customers.
While presenting the EU case at the Ecosystem forum meeting in October 2018 in Helsinki, Finland, I remarked that according to the analysis, focusing on supporting small and mid-sized companies, for example, will help the EU distribute its citizens’ well-being more evenly. If EU politicians are more interested in building a competitive business advantage, then they should focus on supporting ecosystems and networks. If large companies keep on growing, that will have a negative impact on the EU’s competitiveness.
A member of the audience asked, “Can we trust these results?”
For the purposes of this case, we modeled uncertain qualitative information and expert opinions. So essentially, if you trust our experts, then yes, you can also trust the results. But the real beef lies elsewhere, not in true/false labelling.
To summarize, there are several unique reasons for why vague data modeling pays off:
All in all, mathematical modeling helps make the logic behind human decision-making more transparent and accountable. In the EU, there is a strong interest to address issues related to algorithmic transparency, accountability and fairness. We must to make our AI decisions transparent – but I also want to make human decision-making more transparent, accountable, and fair.
There are a number of ways to make better strategic decisions. I encourage you to consider applied mathematics as a tool to understand your own expertise as well as that of your company’s leadership and board. Contact us to hear more, and don’t forget to download our Future Forces 2019 report.
Future-Proofing Strategic Business Decisions
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