The number of emerging business technologies and the possibilities about the impact they will have on business performance seem endless. Naturally, all companies want more competitiveness and profitability. Thus, if new technology such as big data analytics can deliver superior performance, we might ask why they should not invest in data scientists, algorithms, and excellence centers. However, Gartner (2017) has reported that over 85 percent of big data analytics projects fail. A recent McKinsey survey found that only eight percent of respondent organizations have been able to scale analytics beyond limited and isolated cases (Fleming et al., 2018). We conducted a root cause analysis to examine why so many analytics projects fail. We discovered that we could group the reasons why these projects fail into at least six categories: data causes, modeling causes, tools causes, talent causes, management causes, and culture causes.
Andriole, S. J., & Barsky, N. P. (2022). Overdue Diligence: Questioning the Promise, Not the Premise, of Analytics. Communications of the Association for Information Systems, 50, pp-pp. https://doi.org/10.17705/1CAIS.05032
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