Where data-driven decision-making can go wrong
By: Luca, Michael and Edmondson, Amy C
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Material type: 
BookPublisher: Harvard Business Review Description: 102(5), Sep-Oct, 2024: p.80-89.
In:
Harvard Business ReviewSummary: When considering internal data or the results of a study, often business leaders either take the evidence presented as gospel or dismiss it altogether. Both approaches are misguided. What leaders need to do instead is conduct rigorous discussions that assess any findings and whether they apply to the situation in question. Such conversations should explore the internal validity of any analysis (whether it accurately answers the question) as well as its external validity (the extent to which results can be generalized from one context to another). To avoid missteps, you need to separate causation from correlation and control for confounding factors. You should examine the sample size and setting of the research and the period over which it was conducted. You must ensure that you’re measuring an outcome that really matters instead of one that is simply easy to measure. And you need to look for—or undertake—other research that might confirm or contradict the evidence.
By employing a systematic approach to the collection and interpretation of information, you can more effectively reap the benefits of the ever-increasing mountain of external and internal data and make better decision. –Reproduced
https://hbr.org/2024/09/where-data-driven-decision-making-can-go-wrong
| Item type | Current location | Call number | Vol info | Status | Date due | Barcode |
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Articles
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Indian Institute of Public Administration | 102(5), Sep-Oct, 2024: p.80-89 | Available | AR133848 |
When considering internal data or the results of a study, often business leaders either take the evidence presented as gospel or dismiss it altogether. Both approaches are misguided. What leaders need to do instead is conduct rigorous discussions that assess any findings and whether they apply to the situation in question. Such conversations should explore the internal validity of any analysis (whether it accurately answers the question) as well as its external validity (the extent to which results can be generalized from one context to another). To avoid missteps, you need to separate causation from correlation and control for confounding factors. You should examine the sample size and setting of the research and the period over which it was conducted. You must ensure that you’re measuring an outcome that really matters instead of one that is simply easy to measure. And you need to look for—or undertake—other research that might confirm or contradict the evidence.
By employing a systematic approach to the collection and interpretation of information, you can more effectively reap the benefits of the ever-increasing mountain of external and internal data and make better decision. –Reproduced
https://hbr.org/2024/09/where-data-driven-decision-making-can-go-wrong
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