I'm not contrarian in the fact that I prefer to make decisions with as much data as possible. That said, I do think that the most important and difficult decisions aren't based on quantitative inputs alone but several hard-to-capture qualitative judgements. This is where I've found two types of people - those who are data-driven decision makers vs those who are data-informed decision makers.

When considering data-driven decision makers, this cohort often believe more data means better decisions but get stricken by analysis paralysis. The fact of the matter is that there's diminishing returns to data beyond a certain point in almost every decision making process. This all might feel like nuance but from my experience, a decision that can be made solely off numbers in a database or spreadsheet is likely not high stakes and likely also easy to answer. In fact, those types of questions are the ones best handled by machine rather than (wo)man.

Data-informed decision makers make decisions deeply rooted in data but know that not all answers exist in data alone. Such decision makers know when they have enough data and use data to inform action. To be data-informed is to appreciate the art as well as the science of decisions - it turns out this is less simple to hand-off to a computer. In fact, a lot of decisions one might default to considering as data-driven are better described as data-informed (at least to make a good decision). Consider:

• Valuations. Practically, comps or DCF analysis derive the answer but that's the easy part. Consider trends in market, a company's positioning, future outlook from management as some of the qualitative factors to consider. Also, what comps make the most sense to use? Why? What growth rates apply? Should the security at hand trade at a premium or discount? Not black/white whatsoever. Further, this then informs buy/sell decisions
• Managing a budget and household savings goals. This is one I face every month. As much as the budget might indicate my desire to save $x, the reality can be much different. Consider a surprise car repair- you can't not address that even if it shifts the savings target that's been derived from analyzing months of data. Budgeting requires as-you-go adjustments and accepting that life happens and some months savings might be well under what's planned or expected to be the norm. Equally, in other periods the opposite could be the case. Another instance where decisions are more gray vs black and white
• Talent management. Most of us have heard a version of "he gets stuff done but others really don't like working with him." The data alone would show an exemplary employee but not necessarily capture some of the soft points such as culture fit, empathy, and ability to be a team player. These decisions are often the most difficult to make and especially in case of poor culture fit, go against the data

I say all this to point out that the hardest decisions generally require a data-informed decision maker. Be rooted in data but appreciate the value of other inputs and the art of decision making.

Published: 11/24/2020