Paper Number

2183

Paper Type

CRP

Abstract

With the increase in the amount of data available to organisations, we hypothesise that the sensemaking process has changed compared to descriptions in earlier literature. In this case study, we investigate intelligence analysis at the Dutch Police, contextualizing it as a fundamental sensemaking process. The results reveal top-down, bottom-up, and ad hoc sensemaking processes at three organisational levels. Schematisation through predefined structures appeared to be a crucial part of the sensemaking loop. We found that for intelligence analysis, schemas are not just a tool but also a product of the sensemaking process. Further diverges from existing literature were found in the absence of a recurrent search for information, but rather appearing as a constant information flow that produces additional information needs. We have not observed an evident reliance on machine learning techniques in specific sensemaking processes. Finally, we identified 24 enablers for sensemaking, spanning data, software, organisation, and process domains.

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Jun 14th, 12:00 AM

Orchestrating Sensemaking: Investigating the Sensemaking Processes and Its Enablers at the Dutch Police

With the increase in the amount of data available to organisations, we hypothesise that the sensemaking process has changed compared to descriptions in earlier literature. In this case study, we investigate intelligence analysis at the Dutch Police, contextualizing it as a fundamental sensemaking process. The results reveal top-down, bottom-up, and ad hoc sensemaking processes at three organisational levels. Schematisation through predefined structures appeared to be a crucial part of the sensemaking loop. We found that for intelligence analysis, schemas are not just a tool but also a product of the sensemaking process. Further diverges from existing literature were found in the absence of a recurrent search for information, but rather appearing as a constant information flow that produces additional information needs. We have not observed an evident reliance on machine learning techniques in specific sensemaking processes. Finally, we identified 24 enablers for sensemaking, spanning data, software, organisation, and process domains.

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