Defining analytics maturity indicators: A survey approach

Jasmien Lismont, Jan Vanthienen, Bart Baesens and Wilfried Lemahieu

International Journal of Information Management, 2017, vol. 37, issue 3, 114-124

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.

Keywords: Analytics maturity; Analytics techniques; Organizational characteristics; Survey research (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ininma:v:37:y:2017:i:3:p:114-124

DOI: 10.1016/j.ijinfomgt.2016.12.003

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