Evaluation of Big Data Maturity Models – A Benchmarking Study to Support Big Data Maturity Assessment in Organizations

Abstract

Big Data is defined as high volume, high velocity and high variety information assets, a result of the explosive growth of data facilitated by the digitization of our society. Data has always had strategic value, but with Big Data and the new data handling solutions even more value creation opportunities have emerged. Studies have shown that adopting Big Data initiatives in organizations enhance data management and analytical capabilities that ultimately improve competitiveness, productivity as well as financial and operational results. There are differences between organizations in terms of Big Data capabilities, performance and to what effect Big Data can be utilized. To create value from Big Data, organizations must first assess their current situation and find solutions to advance to a higher Big Data capability level, also known as Big Data maturity. Conceptual artefacts called Big Data maturity models have been developed to help in this endeavor. They allow organizations to have their Big Data methods and processes assessed according to best practices. However, it is a tough job for an organization to select the most useful and appropriate model, as there are many available and each one differ in terms of extensiveness, quality, ease of use, and content. The objective of this research was to evaluate and compare available Big Data maturity models in terms of good practices of maturity modeling and Big Data value creation, ultimately supporting the organizational maturity assessment process. This was done by conducting a benchmarking study that quantitatively evaluated maturity model attributes against specific evaluation criteria. As a result, eight Big Data maturity models were chosen, evaluated and analyzed. The theoretical foundations and concepts of the research were identified through systematical literature reviews. The benchmarking scores suggest that there is great variance between models when examining the good practices of maturity modeling. The degree of addressing Big Data value creation opportunities is more balanced. However, total scores clearly lean towards a specific group of models, identified as top-performers. These top-performers score relatively high in all examined criteria groups and represent currently the most useful Big Data maturity models for organizational Big Data maturity assessment. They demonstrate high quality of model structure, extensiveness and detail level. Authors of these models use a consistent methodology and good practices for design and development activities, and engage in high quality documentation practices. The Big Data maturity models are easy to use, and provide an intuitive tool for assessment as well as sufficient supporting materials to the end user. Lastly, they address all important Big Data capabilities that contribute to the creation of business value.