The increasing data provide new opportunities for data-based maintenance management. However, decision
makers are struggling to harvest optimally value from their data. This paper presents a maturity model to
support analysing maintenance data management processes from the value perspective. Maturity models related
to data management and business intelligence have been presented before, but they have not addressed the
specific features of maintenance, and have not analysed what makes data valuable. The model presented in this
paper categorizes value of data into six dimensions, and defines five maturity levels to assess them. A real-life
industrial case example is presented to demonstrate the use of the model. The model takes into account that the
most advanced data management solutions do not always represent the optimal target level for every situation.
For instance many small and medium enterprises would struggle creating enough additional value from big data
to make the required investment profitable.