Read dama knowledge system 39 data quality concepts (up)
Read dama knowledge system 39 data quality concept (up). Png
1. Data quality
1. 1. The term “data quality” refers both to relevant features of high-quality data and to processes used to measure or improve data quality
Effective data management involves a complex and interrelated set of processes that enable organizations to use their data to achieve their strategic goals
1. 3. Data value is achieved on the assumption that the data are reliable and credible per se, in other words, that the data should be of high quality
1. 4. Factors contributing to low-quality data
1. 5. All data management principles should contribute to improved data quality and support organizations in using quality data should be the goal of all data management principles
1. 6. Generating quality data requires commitment and coordination across functions
1. 7. Advance preparation of high-quality data to respond to unexpected or unacceptable data-related risks through implementation processes and project management
1. 8. No organization has a perfect business process, a perfect technical process or a perfect data management practice, and all organizations encounter problems related to data quality
1. 9. Formal data quality management is similar to ongoing quality management in other product areas, including standard-setting throughout the life cycle, quality improvement in data creation, conversion and storage, and data management based on standard metrics
1. 10. Data quality programme team
1. 11. As with data governance and corporate data management, data quality management is not a project but an ongoing exercise
1. 12. One of the challenges of data quality management is that quality-related expectations are not always known
2. Operational drivers
2. 1. Improving organizational data value and access
2. 2. Reducing risks and costs associated with low-quality data
2. 3. Improving organizational efficiency and productivity
2. 4. Protecting and enhancing the reputation of the organization
2. 5. Quality data are more valuable than low quality data
2. 6. Many direct costs are related to low-quality data
2. 7. Quality data is not an end in itself; it is only a means of organizational success
Objectives
3. 1. Develop a managed approach to tailoring data to the needs of data consumers
3. 2. Define standards and norms for data quality control as part of the entire data life cycle
3. 3. Defining and implementing processes for measuring, monitoring and reporting data quality levels
Principles
4. 1. Importance
4. 2. Life cycle management
4. 3. Prevention
4. 4. Root amendments
4. 5 governance
4. 6. Standard drivers
4. 7. Objective measurement and transparency
4. 8. Embedding business processes
4. 9. System enforcement
4. 10. Linkages with service levels
Key data
5. 1. Most organizations have significant data but not all are equally important
5. 2. A principle of data quality management is to focus improvements on the most important data for the organization and its clients, which can clarify the scope of projects and enable them to have a direct and measurable impact on business needs
5. 3. Assessing key data
5. 4. Main data are critical
6. Data quality dimensions
6. 1. Data quality dimensions are a measurable feature of the data
6. 2. Strong-wang framework
6. 2. 2. Site data quality
6. 2. 3. Express data quality
6. 2. 4. Access to data quality
6. 3. Thomas redman
6. 3. 2. Data models
6. 3. 3. Precision of attribute domains degrees
6. 4. Larry english
6. 4. 2. Inherent qualitative characteristics
6. 4. 3. Practical quality characteristics
6. 5. Dama uk
6. 5. 2. Exclusiveity
6. 5. 3. Timeliness
6. 5. 4. Effectiveness
6. 5. 5 accuracy
6. 5. 6. Coherence
6. 5. 7. Usability
6. 5. 8. Timing issues (exceeding prescription per se)
6. 5. 9. Flexibility
6. 5. 10. Confidence
6. 5. 11. Value
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