In data science exercises, we usually focus on individual decision making where stakeholder management presents no issue at all.However, in real world applications, we often apply data science to decision making in organizational context with many stakeholders, hence we primarily work on corporate decision making.
Some want others to make decisions for them, some frame questions and interpret results, some can talk about database systems and modeling, and some just want data to prove existing belief.
Data scientists need to be prepared to encounter N decision makers out there.
Different from my imaginary decision making, decision making on the ground is a dynamic process that can either rely on or be completely independent of quantitatively problem solving.
Through my work, I am shocked by the mismatch between decision making and quantitative problem solving; many data scientists also admit their data results are not well taken by their business partners (link). This time, I want to walk outside the data science garden and look hard at decision making by itself.
Because dealing with stakeholders is never a thing among data scientists, those data scientists who have a channel interest in modeling techniques are likely to feel shocked by the complexity of corporate decision making.
In corporate decision making, stakeholders have competing perspectives and interests, rely on both quantitative and qualitative information for decision making.Individual decision making is focused, cost efficient, and has a clear accountability.Individual decision making takes place both in personal life and workplace; you can decide on your college major and you can decide on the best way to present at work.We can further classify decisions into operation decisions and strategy decisions.Operation decisions are characterized as recurring and structured; such decisions concern day to day running of the business and are made by middle managers and front line employees.In a simplified world, a data scientist need to convince one decision maker; in today’s corporate, a data scientists is just one of many to convince many decision makers. In N: N model, from the perspective of a data scientist, the data scientist need deliver data stories to multiple team leaders from multiple lens.In my work, a sign of success is not the applause at the end my presentation, instead a sign of success is the referral I get to present to another business leader.Think about house price estimation, loan application approval model and user retention prediction.These successful data science applications are operationalized as code and have limited stakeholders intervention.I have had this mental model for a long time, because this mental model not only mirrors my experience in how I approached textbook problems quantitatively at school, but also speaks directly to the popular concept of data driven decision making in workplace.This mental model treats decision making as a smooth continuum of quantitative problem solving, and makes me believe that I know decision making just because I know quantitative problem solving.