Using Simulated Experience to Make Sense of Big Data Case Solution And Analysis, HBR Case Study Solution & Analysis of Harvard Case Studies

In an increasingly complex societal and economic environment, access to vast quantities of data and information can help organizations and authorities make better policies, predictions and decisions. Truly, more and more decision makers rely on statistical findings and data-based decision models when tackling difficulties and forming strategies. Thus far, discussions of data-based decision making have centered largely on analysis: statistical methods, technological infrastructures and data collection. Yet additional problem that is vital receives much less examination: how analytical results are conveyed to decision makers. Data science, like medical diagnostics or scientific research, lies in the hands of expert analysts who must clarify their findings to executive decision makers that are often knowledgeable about proper, statistical reasoning. Yet many behavioral experiments have shown that when the same statistical information is expressed in various manners, people make radically distinct conclusions. Description, the writers note, is the default style of presenting statistical advice. This commonly involves a written report or a verbal statement.

In a recent experiment, decision requested 257 economics scholars to make forecasts and judgments predicated on a simple regression analysis. To the author’ssurprise, most of these experts had a hard time accurately deciphering and acting on the outcome of the kind of investigation they themselves often conduct. The authors assert that simulated experience enables intuitive interpretation of statistical information, thereby conveying analytical results even to decision makers who are not knowledgeable about numbers.

Using Simulated Experience to Make Sense of Big Data Case Study Solution

PUBLICATION DATE: January 01, 2015 PRODUCT #: SMR509-PDF-ENG

This is just an excerpt. This case is about TECHNOLOGY & OPERATIONS