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THE DATA HIERARCHY: factors influencing the adoption and implementation of data-driven decision making

Author

Listed:
  • Stefan Sleep

    (Georgia Gwinnett College)

  • John Hulland

    (University of Georgia)

  • Richard A. Gooner

    (University of Georgia)

Abstract

Marketing practitioners have access to a rapidly increasing quantity and variety of data from customers and other stakeholders. Managers use the term “Big Data” to describe this avalanche of information, which many view as critical to providing a better understanding of customers and markets. This research uses interviews with managers to examine the marketing function’s perspective on data-driven decision making within the firm. Based on informant responses, we develop a hierarchy of data-oriented decision making, describe the drivers that influence where a firm falls within this hierarchy, and detail several transition capabilities for marketing managers interested in becoming more data-driven. The key factors that influence the level of data driven decision making are: 1) firm environment; 2), competition, 3) executive commitment, 4) interdepartmental dynamics, and 5) organizational structure. This framework guides marketing managers both in evaluating the firm’s data capabilities and facilitating change.

Suggested Citation

  • Stefan Sleep & John Hulland & Richard A. Gooner, 2019. "THE DATA HIERARCHY: factors influencing the adoption and implementation of data-driven decision making," AMS Review, Springer;Academy of Marketing Science, vol. 9(3), pages 230-248, December.
    • Handle: RePEc:spr:amsrev:v:9:y:2019:i:3:d:10.1007_s13162-019-00146-8
    DOI: 10.1007/s13162-019-00146-8
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    References listed on IDEAS

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    Cited by:

    1. Sleep, Stefan & Gala, Prachi & Harrison, Dana E., 2023. "Removing silos to enable data-driven decisions: The importance of marketing and IT knowledge, cooperation, and information quality," Journal of Business Research, Elsevier, vol. 156(C).
    2. Constant Berkhout & Abhi Bhattacharya & Carlos Bauer & Ross W. Johnson, 2024. "Revisiting the construct of data-driven decision making: antecedents, scope, and boundaries," SN Business & Economics, Springer, vol. 4(10), pages 1-23, October.
    3. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    4. Manis, K.T. & Madhavaram, Sreedhar, 2023. "AI-Enabled marketing capabilities and the hierarchy of capabilities: Conceptualization, proposition development, and research avenues," Journal of Business Research, Elsevier, vol. 157(C).
    5. Luigi M. De Luca & Dennis Herhausen & Gabriele Troilo & Andrea Rossi, 2021. "How and when do big data investments pay off? The role of marketing affordances and service innovation," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 790-810, July.

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