As algorithms get better at processing data (and as we have “thinking” LLMs), we need to focus on better thinking for decision making.
Good decisions combine available information with good thinking, sound reasoning. Then come assumptions to fill in the blanks left by incomplete information. The more reasonable the assumptions, the better the decision.
The same is true when analyzing data to support decision making. Modeling data involves assumptions, both method-specific and model-specific. If the assumptions are sound, a decision based on a model’s insights is more likely to be a good one.
Staying true to the actual data at hand while making decisions based on the data is data centricity. One way to achieve data centricity is to look for model-free (i.e., assumption-free) evidence before spending any red ink to connect the dots.