Hello everyone, I am Dugufeng, a former port coal worker, currently working as the person in charge of big data in a state-owned enterprise, and the author of the official account big data flow. In the last two years, because of the needs of the company and the development trend of big data, I began to learn about data governance.
Data governance requires systematic learning to truly master, and professional examination certification is also required to prove one's learning ability and knowledge mastery in data governance. If you have any questions about data governance and data governance certification CDMP, you can refer to my previous article for a detailed introduction.
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This document is based on the collation of learning materials related to data governance, and is collated for the study notes (mind map and knowledge points) of the data governance professional certification CDMP .
The article is long, it is recommended to read after bookmarking.
For subsequent documents, please pay attention to the big data flow of the official account , and will continue to update~
This document is the data management capability maturity assessment part, which is divided into 7 parts.
Due to the display of the page, some levels cannot be fully expanded. The structure is shown in the figure below.
I. Overview
Capability Maturity Assessment (CMA) is a capability improvement program based on the Capability Maturity Model (CMM) framework, which describes the process of developing data management capabilities from the initial state to optimization.
The CMA concept arose out of criteria established by the U.S. Department of Defense to evaluate software contractors. In the mid-1980s, the Software Engineering Institute of Carnegie Mellon University released the Software Capability Maturity Model.
The maturity model defines the level of maturity by describing the capability characteristics of each stage. There is an established order of competency levels and no levels can be skipped. These levels typically include: 1) Level 0. Incompetent level. 2) Level 1. Initial or Interim Level: Success depends on individual ability. 3) Level 2. Repeatable level: the most basic process rules are formulated. 4) Level 3. Defined level: Standards are established and used. 5) Level 4. Managed: Capabilities can be quantified and controlled. 6) Level 5. Optimization level: The goal of capacity improvement is quantifiable.
Organizations can develop roadmaps to achieve the following: 1) High-value improvement opportunities related to processes, methods, resources, and automation. 2) Ability to align with business strategy. 3) Conduct a governance program for regular model-based assessments of organizational capabilities.
The Data Management Maturity Assessment (DMMA) can be used to assess data management holistically, or it can be used to focus on a single knowledge area or even a single process. DMMA helps bridge the conflicting perceptions between business and IT regarding the health and effectiveness of data management practices.
The data management maturity assessment context diagram is as follows:
Business drivers: 1) Regulation. Regulation imposes minimum maturity levels on data management. 2) Data governance. Data governance requires a maturity assessment for planning and compliance purposes. 3) Organizational readiness for process improvement. Organizations recognize that improving their practices begins with assessing their current state. 4) Organizational changes. Organizational changes, such as mergers, create data management challenges. 5) New technology. Organizations want to understand the likelihood of success in adopting new technologies. 6) Data management issues.
Objectives: 1) To introduce data management concepts, principles and practices to stakeholders. 2) Clarify the roles and responsibilities of stakeholders in organizing data. 3) Emphasize the need to manage data as a critical asset. 4) Expand awareness of data management activities across the organization. 5) Help improve the collaboration needed for effective data governance.