Toward a Maturity Model for Big Data Analytics: A Roadmap for Complex Data Processing

In the current data-driven digital economy, organizations attempt to harness big data power to make their decisions better. The big data analytics assist them not only to identify new opportunities but extract knowledge and obtain better performance. Despite a huge investment in big data analytics initiatives, the majority of organizations have failed to successfully exploit their power. Although big data analytics have received considerable research attention, a little has been done on how organizations implement strategies in order to integrate the different dimensions of big data analytics; hence, a roadmap is required to navigate these technological initiatives. This paper is also an attempt to overcome this challenge by developing a comprehensive big data analytics maturity model to help managers evaluate their existing capabilities and formulate an appropriate strategy for further progress. A mixed-method was applied in this research using a qualitative meta-synthesis approach. For this purpose, first, a systematic literature review was conducted to identify the capabilities and practices of big data analytics maturity. Then the proposed key capabilities and practices were assessed and prioritized based on the opinions of experts using the quantitative survey method. Finally, considering the architecture of the big data analytics maturity model, the capabilities were assigned to maturity levels according to their priority of implementation using a focus group. The proposed model is comprised of four main capabilities, nine key dimensions (KDs) and five maturity levels based on the capability maturity model integration (CMMI) architecture. A questionnaire and a focus group were used to present the big data maturity model. The capabilities and KDs, as well as their implementation order and weight in the proposed maturity model are presented as a roadmap for implementing big data analytics effectively. The proposed model enables organizations to assess their current big data analytics capabilities and navigate them to select appropriate strategies for their improvement. Due to its nature, it allows managers to find their strong and weak points and identify investment priorities. This study provides a comprehensive maturity model using a meta-synthesis which has not been used in this field so far. The proposed model is both descriptive and prescriptive and has a significant theoretical contribution to big data researches. The paper provides a mechanism to benchmark big data analytics projects and develop an appropriate strategy in terms of progress.