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Developing a Maturity Model for AI-Augmented Data Management

Defize, D.R. (2020) Developing a Maturity Model for AI-Augmented Data Management.

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Abstract:Data management is becoming more complicated due to the increase in data volume, variety, and velocity. In turn, the increase in time-consuming data management work is exponential, which means that it is now impossible to do it all manual. Augmented data management has the potential to overcome organizations’ data management challenges by leveraging artificial intelligence to automate and enhance data management tasks and decisions. Meanwhile, organizations struggle to manage their data successfully. Maturity models are a proven approach to systematically assess and improve organizational capabilities towards achieving an organizational goal. In the context of managing data in organizations, a maturity model may help them navigate through the improvement options available and assess their relevance for the organization’s goals. The objective of this master project is to develop a maturity model for augmented data management. To this end, the research in the present thesis adopted a Design Science grounded research process and, in turn, underwent three phases: The first phase provides the scientific background through a systematic literature review on artificial intelligence, data management, and maturity model development. The results of this phase include: (i) an overview of the subfields of artificial intelligence and their applications, (ii) an overview of all data management and artificial intelligence maturity models available in current literature, and (iii) an overview of methods, methodologies, and guidelines on developing maturity models. The second phase includes the initial design and development of the maturity model. To this end, the design choice is made to leverage the foundation of existing maturity models and build upon those with empirical research to develop a novel model. The development strategy used complementarily research techniques of three types: metamodel analysis, expert interviews, and market research. The metamodel analysis is used to systematically compare and synthesize existing maturity models. Through interviewing experts on artificial intelligence and data management, it is identified which data management processes can be augmented. The market research complemented this view by analyzing tools that provide these functionalities. In the third phase, the initial model is evaluated and refined through a mixed-method validation approach. It includes experts’ perception-based evaluation and case studies. The maturity model is operationalized by creating an Excel assessment tool that can be used to structure the assessment and assess the (sub) capabilities and processes. The model and assessment tool are evaluated with data management consultants, the expected users of the model. The case studies were conducted with the primary functional beneficiary of the model: organizations that want to improve their (augmented) data management practices. Based on the findings of the mixed-method validation, it is concluded that (1) the resulting Augmented Data Management Maturity Model (ADM3) consists of sufficient and accurate maturity levels, (2) the processes and capabilities are relevant, comprehensive, mutually exclusive and accurate and (3) the model itself is understandable, easy to use, useful and practical. It can also be concluded that the recommendations on improving capabilities are understandable, easy to use, and useful. The recommendations on constructing a roadmap are understandable and easy to use.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:50 technical science in general, 58 process technology, 85 business administration, organizational science
Programme:Business Information Technology MSc (60025)
Link to this item:http://purl.utwente.nl/essays/84963
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