Problem Solving And Decision Making Presentation

One more presentation sounds alright, but it is not easy to tell persuasive data stories to N stakeholders, because the data science work has to be robust enough against N dimensions of scrutiny.

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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.

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.

Leadership structure is flat, projects are ran by cross functional teams, therefore we often do not have a single authority for decision making.

To support decentralized decision making, data scientists have a village to convince.

Individual decision making is familiar to most people because it is a part of everyday life.

Individual decision making has one or a small number of stakeholders, usually decision maker himself or herself.


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