In data management, management activities or formal enterprise processes are unorganized

Very few organizations are at Level 0, the level set for definitional purposes in the maturity model.

Generic data management using a limited toolset with little or no governance activity

Data processing is highly dependent on a small number of experts, with roles and responsibilities defined separately across departments

Solutions for managing data are limited

Data quality issues are prevalent but cannot be resolved, infrastructure support is at the business unit level

Have consistent tools and role definitions to support process execution

Organizations are beginning to use centralized tools and provide more monitoring means for data management

Role definitions and processes are not entirely tied to specific experts

The organization becomes aware of data quality issues and concepts and begins to recognize the concepts of master data and reference data

Level 3 will introduce and institutionalize scalable data management processes and view data management as an organizational enabler

Characteristics include controlled data replication within the organization, generally improved overall data quality, and consistent policy definition and management

A more formal process definition can significantly reduce manual intervention, which means that with a centralized design process, the results of the process are more predictable.

Evaluation criteria may include development of data management policies, use of scalable processes, and consistency of data models and system controls

The accumulation of experience gained from Level 1 to Level 3 growth enables organizations to predict outcomes as they approach new projects and tasks and begin to manage data-related risks. Data management includes a number of performance indicators.

Characteristics of Level 4 include standardization of data management tools from desktop to infrastructure, and well-structured centralized planning and governance capabilities

When data management practices are optimized, they are highly predictable due to process automation and technical change management, and organizations at this maturity level are more focused on continuous improvement.

At level 5, tools enable viewing data across processes

Control data proliferation to prevent unnecessary duplication, and use easy-to-understand metrics to manage and measure data quality and processes