‘The fox knows many things; the hedgehog one big thing’ is a quote attributed to Archilocus, an ancient Greek poet (them Greeks again!). In the 1950s, philosopher Isaiah Berlin1 expanded on this idea to divide writers and thinkers into two categories: hedgehogs, who look at the world through the lens of a single defining idea; and foxes, who draw on a wide variety of experiences and for whom the world cannot boiled down to a single idea.
What if we apply this this framework to the decision architecture within an organization? A good decision system would have the ability to deliver scalable and reliable solutions rapidly (i.e. the hedgehogs) for ‘kind’ problems and at the same time, the ability to defer to human judgment and intuition-based decisions (i.e. the foxes) when it comes to the ‘wicked’ problems. (refer the framework from this previous post). And as we see around us in the accelerating VUCA world that we live in, we are likely to see an increase in wicked problems – with an additional constraint: the data to learn and solve them.
The foxes and hedgehogs in the organization
Let’s start with hedgehogs. The progressive organizations have made great strides in creating a robust problem-solving infrastructure by investing aggressively in a Data & Analytics ecosystem. This has played out in three broad investment areas:
- BI/Reporting: going beyond plain dashboards to automated insights
- Ad-hoc analysis: a combination of data analysts and toolsets to answer questions that come up from business
- ML factory: design, train and operationalize models that scale and reliably provide answers to specific problems
There is one common theme that is common across these three areas: they are all designed to be really good within their specific domain areas and are optimized around a bounded problem space. Let’s take 2 examples of ‘hedgehogs’ in the organization:
- Transaction fraud risk assessment needs to be made for each transaction in real-time. We now have some really, really smart ML models that can deliver high accuracy at really high volumes (and keep on getting better). In this specific domain, the machines are ridiculously efficient
- Marketing Analytics teams have evolved with a combination of very smart (that is, if you get lucky) analysts and data scientists who have built the tacit knowledge, data sets and tools to help drive better marketing strategies – from deeper understanding of the customers to driving deep engagement. Again, these teams have grown to deliver very sophisticated solutions and drive impact
What is abundantly clear is that every organization’s decision architecture needs to have enough hedgehogs to drive the right kind of problem solving and data driven solution capabilities. The question we must ask ourselves is: is it enough?
Enter the Foxes
Take a look at the current situation. The events of the last 3 months triggered by the Covid-19 pandemic are forcing every organization to take a critical look at almost all their decision systems. Let’s take a look at the problem areas again and how they have morphed from being relatively ‘kind’ to manifestly ‘wicked’ in an astonishingly short period of time:
- Post Covid-19, transaction risk has clearly shown a ‘fat tail’ behavior. The risk profile of customers who have been worst hit by the economic impact is likely to change dramatically in a very short period of time. And to make it worse, there is clearly no historical precedence. How then is the ML model to learn and re-calibrate the risk scores?
- Post Covid-19, customers are likely to look at their spend patterns very closely as they re-orient their behaviors in response to the massive uncertainty. Will even the best ML models trained over the years on behavioral factors or even the analysts who have built the tacit knowledge on well-understood patterns be enough to drive the right insights?
There is a clear need for a capability that can tackle these classes or problems. In other words, we need foxes more than ever: problem solvers who are able to take the most intractable of problems and break them down into tractable sub-problems; balance an outside-in view (driven by first principles and an exploratory mindset) with an inside-out view (driven by exploiting the tacit knowledge that sits within the organization); and above all, work with an ‘active open mindedness’2. And so, the question is: can foxes be created or does an organization have to get lucky to have them? How should you think of an organization construct that creates this team of foxes? Topic for next week.
- To read the entire essay, here’s the link. Although Berlin did say that he meant to use the analogy in jest, it just stuck …
- This term was coined by Jonathan Burns, who as it turns out, is somewhat of a revered figure in Psychology. If you are really up to it, here is a summary – the topics he seems to have covered are fascinating. Reading a 600 page book on Psychology will be another thing though!