1. Introduction to DMM
The Enterprise Data Management Capability Maturity Model ( Data Management Maturity, DMM ) was released by CMMI Research Institute in 2014. It can help organizations build, improve, and measure their enterprise data management capabilities , provide timely, accurate, and easily accessible data throughout the organization , and can be used to evaluate and improve the organization's data management level, helping organizations use data to improve business performance . The DMM model follows some basic principles, structures and proof methods of the Software Capability Maturity Integration Model (CMMI) .
The CMMI Research Institute’s position on DMM is: “DMM will be a good friend of CDO (Chief Data Officer, Chief Data Officer) . To drive strategic insights to maintain competitive advantages, businesses must do their best and use today’s massive data as cleverly as possible .To achieve this goal, organizations need to apply a collaborative approach to optimizing their data assets ." It is precisely for this purpose that DMM provides such a method and tool for the industry.
DMM is a comprehensive reference model for state-of-the-art practice process improvement . The DMM defines the fundamental business processes of data management and the key capabilities that constitute the progressive path to maturity . It is a comprehensive framework for data management practices that not only helps organizations benchmark their capabilities, identify strengths and gaps, but also facilitates organizations to establish their own data management maturity roadmap , helping organizations manage their critical data more proficiently Assets that drive proactive tactical and strategic support, provide a consistent and comparable baseline to measure progress over time, and leverage their data assets to improve business performance.
Since CMMI has achieved great success in the software process maturity (SW-CMM) assessment process, the DMM model has attracted the attention of all parties as soon as it was released. At present, a group of assessors has been trained internationally, including China, Brazil , The United States, etc., and model verification has been carried out in Freddie Mac (Federal Home Loan Mortgage Corporation), Microsoft and other companies.
2. The structure of DMM
The DMM model includes 20 data management process areas and 5 supporting process areas . Divided into 6 categories. Each category contains several process areas. These process areas are the primary means of communicating the model's themes, goals, practices, and examples of work products. Organizations can build data management capabilities by completing process domain practices, and can improve their data management maturity by combining infrastructure support practices .
The process areas of the DMM:
Data Management Strategy
Data Management Strategy
communicate
Data Management Responsibilities
business case
Funding
data governance
governance management
Glossary of business terms
metadata management
data quality
Data Quality Strategy
Data profile
Data Quality Assessment
data cleaning
data manipulation
Data Requirements Definition
Data Lifecycle Management
Data provision management
Platform and Architecture
architectural approach
Architecture Standards
Data Management Platform
data integration
Historical data archiving and retention
Support process
Measurement and Analysis
process management
Process Quality Assurance
Risk Management
configuration management
3. The 5 levels of DMM
Similar to CMMI, DMM also proposes five levels according to the enterprise's data management capabilities. Different levels of process areas mean that the results of process improvement of best practices will also increase accordingly.
Level 1 Execution
Data management is only at the level of project implementation needs . The execution of the process is ad hoc, mainly at the project level . Processes often fail to apply across business areas. The process principle is primarily reactive. For example, data quality processes focus on remediation rather than prevention. There may be fundamental improvements, but such improvements are not scaled across the organization and often cannot be sustained.
Level 2 Management
Organizations realize the importance of managing data as a critical infrastructure asset . The organization plans and executes processes according to the management strategy; employs skilled staff and sufficient resources to ensure controlled outputs; engages relevant stakeholders; and monitors, controls, and evaluates processes to comply with relevant process definitions .
Level 3 Definition
View data at an organizational level as a key element in achieving targeted performance . Adopt and consistently follow a set of standard procedures . Adaptation of a set of standard processes, according to the organization's guidelines, to obtain processes applicable to the organization's particular needs.
Level 4 Metrics
Consider data as one of the sources of competitive advantage for your organization . Process metrics are defined and used for data management. This includes managing variance, forecasting and analysis using statistical and other quantitative techniques. Process performance management runs through the entire process life cycle.
Level 5 Optimization
Think of data as a key element for organizations to survive in a dynamic and competitive marketplace . Target identification of improvement opportunities to optimize process performance by applying level 4 analysis. Share best practices with your peers and within your industry.
4. Objects of DMM
This model is aimed at every organization that wants to efficiently manage its data assets . Companies that have used the DMM model cover a wide range of industries, including IT, aviation, finance, and government.
The DMM can be tailored to fit the needs of any organization, and it can be applied to an entire organization, a line of business, or a major multi-stakeholder project.
5. Benefits of implementing DMM
1. Help and guide enterprises to obtain the current status of data management, identify gaps with industry best practices, pinpoint key issues, and propose data management improvement suggestions and directions.
2. Carry out personnel training, improve the skills of enterprise data management personnel, and improve the maturity of enterprise data management capabilities.
3. It can help enterprises establish continuous improvement in data management and improve the quality of data.
4. It can improve the data management level of enterprises and help organizations bridge the gap between business and IT.
5. Help companies establish best practices related to data management.
Reposted from: Baidu Security Verification