Data-driven Problem Identification

Data-Driven Problem Identification is a systematic approach to recognizing and formulating problems by analyzing empirical data, distinguished from intuition- or theory-driven methods through its reliance on observable patterns and quantitative evidence to detect anomalies, gaps, or inefficiencies requiring investigation or intervention.

40

Publications

1.7K

Citations

126

Authors

86

Institutions

Publications per year

2017–2026

29

Authors

126

  1. CJ
    Christopher J. Ruhm

    University of Virginia, United States

    2 publications · 73 citations

    2

    h-index

  2. SZ
    Sibylle Ziegler

    Technical University of Munich, Germany

    1 publications · 39 citations

    1

    h-index

  3. US
    Uluc̣ Saranlı

    Bilkent University, Türkiye

    1 publications · 17 citations

    1

    h-index

  4. KM
    Kevin M. Silva

    University of Michigan, United States

    1 publications · 96 citations

    1

    h-index

  5. HK
    Heeyeul Kwon

    Seoul National University, South Korea

    1 publications · 16 citations

    1

    h-index

1–5 of 126

Venues

  1. Energy

    4 publications · 157 citations

    4

    h-index

  2. Nature Machine Intelligence

    1 publications · 34 citations

    1

    h-index

  3. Journal of Navigation

    1 publications · 23 citations

    1

    h-index

  4. Critical Social Policy

    CSR · 1 publications · 23 citations

    1

    h-index

  5. Università del Salento

    Repository · 1 publications · 21 citations

    1

    h-index

1–5