Most successful organizations treat data-driven decision making as a primary objective and pursue it with religious zeal. However, data-driven decision making, the steps leading to it and how AI is changing it are not well-defined.
In an AI-first company, data-driven decision making means
- Strategic decisions are made by a diverse group including executives that rely on a sufficiently comprehensive set of information. By definition, a bad strategic decision should be important enough to lead to failure of the company.
- Most operational decisions are handled by continuously learning machine learning models which produce explainable decisions. Operational decisions are frequent (once a week or more frequent) and not critical (a single mistake is unlikely to lead to failure of the company).
- Operational decisions which can not be automated with good accuracy are delegated to humans.
- If data is lacking, opinion based decisions are made.
- If data exists and has been analyzed, decision maker relies on analysis.
- If data exists but is not analyzed yet, cost of analysis determines whether an opinion or data based decision will be made.
Before settling on this framework for decision making for modern corporations, we need to identify how we can evaluate different decision making models. However, if you like you can directly skip to the sections that interest you: