[1] The goals of BDMMs are: Key organizational areas refer to "people, process and technology" and the subcomponents include[3] alignment, architecture, data, data governance, delivery, development, measurement, program governance, scope, skills, sponsorship, statistical modelling, technology, value and visualization.
[4][5] An underlying assumption is that a high level of big data maturity correlates with an increase in revenue and reduction in operational expense.
This descriptive model aims to assess the value generated from big data investments towards supporting strategic business initiatives.
[9] Comparative big data maturity models aim to benchmark an organization in relation to its industry peers and normally consist of a survey containing quantitative and qualitative information.
The overall results communicate that the top performer models are extensive, balanced, well-documented, easy to use, and they address a good number of big data capabilities that are utilized in business value creation.
Whilst their content is well aligned with business value creation through big data capabilities, they all lack quality of development, ease of application and extensiveness.
Lowest scores were awarded to IBM and Van Veenstra, since both are providing low level guidance for the respective maturity model's practical use, and they completely lack in documentation, ultimately resulting in poor quality of development and evaluation.