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Beyond the Hype: Why Do Data-Driven Projects Fail?

 
: Ermakova, Tatiana; Blume, Julia; Fabian, Benjamin; Fomenko, Elena; Berlin, Marcus; Hauswirth, Manfred

:
Volltext urn:nbn:de:0011-n-6352284 (1.1 MByte PDF)
MD5 Fingerprint: 9d1bc11f284f6192b5638ce595c9580e
(CC) by-nc-nd
Erstellt am: 28.5.2021


54th Hawaii International Conference on System Sciences 2021. Proceedings : Grand Hyatt Kauai, Hawaii, USA
Honolulu/Hawaii: Univ. of Hawaii at Manoa, 2021
ISBN: 978-0-9981331-4-0
S.5081-5090
Hawaii International Conference on System Sciences (HICSS) <54, 2021, Online>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer FOKUS ()
Judgement, Big Data-Analytics and Decision-making; challenge; data-driven; Data Science; failure; non-success

Abstract
Despite substantial investments, data science has failed to deliver significant business value in many companies. So far, the reasons for this problem have not been explored systematically. This study tries to find possible explanations for this shortcoming and analyses the specific challenges in data-driven projects. To identify the reasons that make data-driven projects fall short of expectations, multiple rounds of qualitative semi-structured interviews with domain experts with different roles in data-driven projects were carried out. This was followed by a questionnaire surveying 112 experts with experience in data projects from eleven industries. Our results show that the main reasons for failure in data-driven projects are (1) the lack of understanding of the business context and user needs, (2) low data quality, and (3) data access problems. It is interesting, that 54% of respondents see a conceptual gap between business strategies and the implementation of analytics solutions. Based on our results, we give recommendations for how to overcome this conceptual distance and carrying out data-driven projects more successfully in the future.

: http://publica.fraunhofer.de/dokumente/N-635228.html