General IS Topics
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Paper Number
2301
Paper Type
Completed
Description
Data analytics and visualization techniques are vital for effective decision making. Decision makers can now have some certainty in otherwise uncertain situations. However, given what we know about humans and the data analysis and decision making process, is this certainty illusory? Data does not present and analyze itself – humans make judgement calls around sampling, measurement, algorithms, and confidence. Human decision-makers have biases, ideological commitments, and social amplification tendencies. Finally, technical solutions are subject to a variety of tradeoffs and diminishing returns on technical adjustments. We identify sources of uncertainty throughout the data analysis process and illustrate them through the COVID-19 pandemic. We propose a pragmatic, sociotechnical approach data-driven decision making in emergent situations that accounts for these sources of uncertainty. This approach involves four principles: defensibility, the restless dialectic, deadliest enemy, and design without final goals. We describe how the approach can augment social and technical elements of data-driven analytics processes.
Recommended Citation
Berente, Nicholas; Lalor, John P.; Somanchi, Sriram; and Abbasi, Ahmed, "The Illusion of Certainty and Data-Driven Decision Making in Emergent Situations" (2021). ICIS 2021 Proceedings. 10.
https://aisel.aisnet.org/icis2021/gen_topics/gen_topics/10
The Illusion of Certainty and Data-Driven Decision Making in Emergent Situations
Data analytics and visualization techniques are vital for effective decision making. Decision makers can now have some certainty in otherwise uncertain situations. However, given what we know about humans and the data analysis and decision making process, is this certainty illusory? Data does not present and analyze itself – humans make judgement calls around sampling, measurement, algorithms, and confidence. Human decision-makers have biases, ideological commitments, and social amplification tendencies. Finally, technical solutions are subject to a variety of tradeoffs and diminishing returns on technical adjustments. We identify sources of uncertainty throughout the data analysis process and illustrate them through the COVID-19 pandemic. We propose a pragmatic, sociotechnical approach data-driven decision making in emergent situations that accounts for these sources of uncertainty. This approach involves four principles: defensibility, the restless dialectic, deadliest enemy, and design without final goals. We describe how the approach can augment social and technical elements of data-driven analytics processes.
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