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Defining analytics maturity indicators: A survey approach

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  • Lismont, Jasmien
  • Vanthienen, Jan
  • Baesens, Bart
  • Lemahieu, Wilfried

Abstract

The ability to derive new insights from data using advanced machine learning or analytics techniques can enhance the decision-making process in companies. Nevertheless, researchers have found that the actual application of analytics in companies is still in its initial stages. Therefore, this paper studies by means of a descriptive survey the application of analytics with regards to five different aspects as defined by the DELTA model: data, enterprise or organization, leadership, targets or techniques and applications, and the analysts who apply the techniques themselves. We found that the analytics organization in companies matures with regards to these aspects. As such, if companies started earlier with analytics, they apply nowadays more complex techniques such as neural networks, and more advanced applications such as HR analytics and predictive analytics. Moreover, analytics is differently propagated throughout companies as they mature with a larger focus on department-wide or organization-wide analytics and a more advanced data governance policy. Next, we research by means of clustering how these characteristics can indicate the analytics maturity stage of companies. As such, we discover four clusters with a clear growth path: no analytics, analytics bootstrappers, sustainable analytics adopters and disruptive analytics innovators.

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  • Lismont, Jasmien & Vanthienen, Jan & Baesens, Bart & Lemahieu, Wilfried, 2017. "Defining analytics maturity indicators: A survey approach," International Journal of Information Management, Elsevier, vol. 37(3), pages 114-124.
    • Handle: RePEc:eee:ininma:v:37:y:2017:i:3:p:114-124
    DOI: 10.1016/j.ijinfomgt.2016.12.003
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    Cited by:

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    2. Ziboud Van Veldhoven & Jan Vanthienen, 2022. "Digital transformation as an interaction-driven perspective between business, society, and technology," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(2), pages 629-644, June.
    3. Ashrafi, Amir & Zare Ravasan, Ahad & Trkman, Peter & Afshari, Samira, 2019. "The role of business analytics capabilities in bolstering firms’ agility and performance," International Journal of Information Management, Elsevier, vol. 47(C), pages 1-15.
    4. Reis, Carolina & Ruivo, Pedro & Oliveira, Tiago & Faroleiro, Paulo, 2020. "Assessing the drivers of machine learning business value," Journal of Business Research, Elsevier, vol. 117(C), pages 232-243.
    5. Anna Trunk & Hendrik Birkel & Evi Hartmann, 2020. "On the current state of combining human and artificial intelligence for strategic organizational decision making," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 875-919, November.
    6. Tursunbayeva, Aizhan & Di Lauro, Stefano & Pagliari, Claudia, 2018. "People analytics—A scoping review of conceptual boundaries and value propositions," International Journal of Information Management, Elsevier, vol. 43(C), pages 224-247.
    7. Carvalho, João Vidal & Rocha, Álvaro & Vasconcelos, José & Abreu, António, 2019. "A health data analytics maturity model for hospitals information systems," International Journal of Information Management, Elsevier, vol. 46(C), pages 278-285.
    8. Magni, Domitilla & Scuotto, Veronica & Pezzi, Alberto & Giudice, Manlio Del, 2021. "Employees’ acceptance of wearable devices: Towards a predictive model," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    9. Jasmien Lismont & Tine Van Calster & María Óskarsdóttir & Seppe vanden Broucke & Bart Baesens & Wilfried Lemahieu & Jan Vanthienen, 2019. "Closing the Gap Between Experts and Novices Using Analytics-as-a-Service: An Experimental Study," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(6), pages 679-693, December.
    10. Feng, Yi & Yin, Yunqiang & Wang, Dujuan & Dhamotharan, Lalitha, 2022. "A dynamic ensemble selection method for bank telemarketing sales prediction," Journal of Business Research, Elsevier, vol. 139(C), pages 368-382.
    11. Clotilde Coron, 2021. "Quantifying Human Resource Management: A Literature Review," Post-Print halshs-03212718, HAL.
    12. Kayabay, Kerem & Gökalp, Mert Onuralp & Gökalp, Ebru & Erhan Eren, P. & Koçyiğit, Altan, 2022. "Data science roadmapping: An architectural framework for facilitating transformation towards a data-driven organization," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    13. Jimenez-Marquez, Jose Luis & Gonzalez-Carrasco, Israel & Lopez-Cuadrado, Jose Luis & Ruiz-Mezcua, Belen, 2019. "Towards a big data framework for analyzing social media content," International Journal of Information Management, Elsevier, vol. 44(C), pages 1-12.
    14. Djonata Schiessl & Helison Bertoli Alves Dias & José Carlos Korelo, 2022. "Artificial intelligence in marketing: a network analysis and future agenda," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(3), pages 207-218, September.
    15. Hausladen, Iris & Schosser, Maximilian, 2020. "Towards a maturity model for big data analytics in airline network planning," Journal of Air Transport Management, Elsevier, vol. 82(C).

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