Data analytics maturity models: A systematic literature review

Data analytics have become a trending research topic since the last decade given its importance in the organizational decision-making process. Several organizations are trying to employ data in improving their decision making and strategies. Although data scientists and business intelligence consultants are hired to make beneficial use of these data, their activities are not enough for making the organization a data-driven one. The data-driven organizations are not defined by their technical tools, but by their culture, core processes, and capabilities. Organizations and consultants use maturity models as a tool to assess the current situation and optimization of the processes. The first maturity model developed in 1988 was named Capability Maturity Model (CMM) and was used for assessing the ability of government contractors' processes to implement a contracted software project in the USA. Afterward, numerous maturity models were developed in many different domains. Data Maturity models provide a set of methodologies to assess the current state of the organization in analytics, to guide development milestones, to draw organizational dimensions, and to avoid common mistakes in building process. The objective of this paper is to review and study in a systematic way these maturity models and discover the gaps and weaknesses of maturity models in the data domain. This review would pave the road towards finding a new approach for enhancing these reviewed models. The study Will be covering the existing data analytics maturity models in Business intelligence and Big Data. Systematic review practices guidelines in the field of software engineering are being used which was developed by famous researchers. This paper reviews six maturity models and discuss models details with highlighting on trends and gaps. The research paper shows a clear need for formally validated model with fully documented process focus on both organizational and technological aspects with assessment tool measure. This is in addition to the assess of the current situation of analytics effectiveness in the organization. © 2020 Towards the Digital World and Industry X.0 - Proceedings of the 29th International Conference of the International Association for Management of Technology, IAMOT 2020. All rights reserved.