CMMI Data v3.0

Introduction to CMMI Data v3.0

CMMI Data v3.0 is an integrated set of best practices specifically designed to assist organizations in building, improving, and measuring their enterprise data management function and staff. It includes two practice areas, the data management practice area and the data quality practice area.

Data management plays a crucial role in ensuring the efficient handling of data, maximizing operational efficiency by prioritizing critical data activities to meet performance needs. To achieve organization-wide cohesion and demonstrate value to the business, significant stakeholder engagement is necessary, regardless of the current level of data management maturity. This involves aligning with shared and approved objectives and priorities through effective scoping, planning, and resourcing of data management activities as a sustained function. Engaging leadership to emphasize the importance of data management and promoting a multiple stakeholder approach are equally vital aspects.

One of the cornerstones of effective data management lies in defining data management roles and responsibilities while being aware of metadata attributes and the purpose of data items. This emphasis on data control is as critical as managing the data values themselves. By strengthening the form and structure of the data management program, these activities build ongoing advocacy and support from stakeholders and provide the organization with the confidence to achieve its strategic objectives comprehensively.

Data management is often intertwined with the development of critical infrastructure, ranging from fixed physical systems to highly distributed cloud computing services. Consequently, the organization’s approach to addressing these infrastructure considerations is significantly influenced by its business strategy, security concerns, supplier partnerships, and more.

On the other hand, data quality focuses on developing, following, and updating an approach for implementing data quality standards. The goal is to maximize the value and accuracy of data for effective business operations and consistent decision-making. The data quality approach considers standards from various sources, including external regulations and internal requirements, and ensures consistency of data across systems throughout the entire data lifecycle.

Through a data quality approach, an organization gains a full understanding of the nature and quality of the data under management, evaluating, preventing, and remediating defects to ensure data aligns with business purposes and strategic objectives. By implementing repeatable processes for data quality cleansing, the organization can reduce effort and lower costs while ensuring its data assets are “fit for purpose” in business operations, decision-making, and planning. A comprehensive data quality program includes data quality profiling and assessment activities to assess data against defined quality objectives, resulting in data assets that meet high standards of accuracy and reliability.