Evaluation of data management maturity level and how to improve it Use case: Michelin

Data management: The economy of today depends on data more than before. Data is considered as asset and it is organization’s concern to be able to exploit values lying under data. In order to effectively and efficiently use data, it is essential for the organizations to govern and manage their data. From the perspective of business assets, data management brings new approaches to business management. For this purpose, it is vital for organizations to be able to assess their data management level to see whether they are generating expected results or not through measuring data management maturity level. Data management maturity assessment model: Considering the company’s needs, showed that the model we are going to use should meet the following requirements: 1- we should be able to produce quantitative score for overall assessment of Michelin maturity level especially for data governance and data quality maturity level. 2- The method and the model should provide us ability to interpret the results and therefore recommending actionable improvements in the firm. 3- The benefits of the model should be acceptable by the top managers and provide objective results. By reviewing the literature, we concluded that the most suitable model for this purpose is DMM model (Data Management Maturity model) developed by CMMI institute. We chose this model for several reasons. First, it has been widely used by different organizations for data management maturity assessment. Second, it provides the possibility to assess data management maturity by multi-level analysis. Third, it enables the company to measure different domains in the data management concept (namely data quality, data governance and so on). Finally, by utilizing this model, we can focus on a specific domain and try to make improvements. Methodology and steps: According to the DMM model, the following steps are going to be implemented through our methodology. First, we are going to prepare research materials and tools including the domains we will focus on. Second, the methodology and its benefits are going to be explained to top managers of AGB Solution section of Michelin Company. Third, according to top managers’ preferences and CSFs (Critical Success Factors), the interview questions and initial assessment will be designed and implemented. Forth, we will collect the data and analyze them by giving weights to each question depending on the company’s needs and objectives. In the fifth step, we will confirm the interviews and finalize initial assessment. Finally, after discovering deficiencies and lack of management in specific domains, recommendations and potential tools (through data analysis) will be designed for those specific domains in order to make improvements. Key words: Data governance, Data management, Maturity assessment model, Data quality