Introduction: Data is now recognized as a key asset of organizations and societies. Data linkages in different industries have led to the integration of value chains and the elimination of boundaries between different ecosystems. Economic actors are striving to optimally manage data in order to create sustainable added value for their customers and stakeholders. The objective of this research is to introduce a reference model for agile data management that uses the agile paradigm to achieve business intelligence and to compare its components with other reference models. From
Methodology: the aim of this research is of an applied and developmental type. Reasoning, creativity and analogy have been used to draw conclusions. This research has used a combination of Design Science, Metasynthesis and Fuzzy Delphi methodologies. Thus, based on the design science method, the problem has been explained, the solution has been described, the initial solution has been designed (Metasynthesis), validation (Fuzzy Delphi Screening) and finally the findings have been published.
Main Findings: includes codes organized into five groups: “design perspective, theme, category, core code, and open code,” and presented as a conceptual model (level zero, level one, and level two) as a reference model for an agile data management system for smart businesses. Then, the final artifact is compared with some management systems that are consistent with systems thinking. In conclusion, an Agile Data Management System, like other systems, the artifact in question will have four elements: input (goals), processing (enablers), output (results), and feedback (based on agile and lean thinking).
Discussion: Industries have been slow to adopt new technologies. Many businesses still rely on legacy infrastructure. On the other hand, resistance to change in human resources is a factor that hinders agility on the path to intelligence. The proposed reference model brings intelligence to businesses by using modern technologies at the center of the operational model and embedding agile methods in a data management system.