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Strategy decisions are characterized as non-routine and unstructured; such decisions concern organizational policies and strategies, and are made by top management and executives.
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.
In the communication with N stakeholders, data scientists should not only know their different perspectives but also their different decision making preferences.
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.
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.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.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 usually has limited consequences.When the number of stakeholders is large, we have corporate decision making.For years I had a mental model that equated decision making to quantitative problem solving.In this mental model, a decision can be made when a quantitative problem is solved; for example, a new drug should not get an approval (decision making) if its treatment effect is not statistically better than a comparison drug (quantitative problem solving).Lack of data culture becomes an easy excuse, however I believe the hard truth is that decision making is not quantitative problem solving in the first place. Although we can universally define decision making as the action to select one option from alternatives, the process to agree on a selection differs greatly from one decision making to another.Using the number of distinct stakeholders, we can classify decision making into individual decision making and corporate decision making.