From Data Lakes to Business Value: A Capability-Based Framework for Analytics Maturity

Abstract

Data lakes are becoming more common in organizations, and while plenty of data is available, companies have a hard time convincing themselves that they are creating business value from that data. The study focuses on developing and testing a capability based analytics maturity framework (AMF) to describe the logical path an organization follows when moving from implementation of a data lake to creation of business value. Based on the resource-based view (RBV), knowledge-based view (KBV) and socio-technical systems theory, we suggest a four-dimensional framework consisting of basic capabilities (data management, technology, culture, and analytics), maturity stages (largesse, transformation, emergence, consolidation, and dissolution), and boundary conditions (industry type, organizational size, and digital culture). Structural equation modelling (SEM) was used for analyzing the survey data received from 105 organizations representing four industry sectors. Results for each of six hypotheses are summarized as follows: A data lake intensity positively influences capability development (β = 0.42, p < 0.001), capability has complementary effects on analytics maturity (β = 0.18, p < 0.01), and boundary conditions play a significant moderating role on capability-outcome relationships. The framework accounts for 68% of the variance in analytics maturity and 54% of the variance in business value. Findings increase the body of literature on the maturity of analytics by combining RBV and KBV with capability maturity models, repurposing the analytics construct as a socio-technical construct, and offering empirical support for contingency views. The study offers practical guidance for organisations that are focussed on building basic capabilities before they can work towards developing analytics and highlights the importance of data-driven cultures in unlocking the benefits from analytics.

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