Data Quality Management

Data Quality Management entails the establishment and deployment of roles, responsibilities, policies, and procedures concerning the acquisition, maintenance, dissemination, and disposition of data.

Data Quality Dimensions
The dimensions are distinct components that encompass the broader definition of data quality. The quality dimensions are defined as follows: 

1. Completeness - Level of Data missing or unusable.
2. Accuracy - The accuracy dimension refers to how well information in or derived from the data holding reflects the reality it was designed to measure. 
3. Timeliness - Timeliness refers primarily to how current or up to date the data is at the time of release, by measuring the gap between the end of the reference period to which the data pertains and the date on which the data becomes available to users. 
4. Integrity - Degree of referenced data.
5. Uniqueness - Level of non-duplicates.
6. Conformity - Degree of data stored in a nonstandard format.
7. Usability - Usability reflects the ease with which a data holding’s data may be understood and accessed. 
8. Relevance - Relevance reflects the degree to which a data holding meets the current and potential future needs of users. 

Information Life Cycle Phases

The Virtuous Cycle of Data Quality
Data quality management incorporates a “virtuous cycle” in which continuous analysis, observation, and improvement lead to overall improvement in the quality of organizational information across the board.

Five Fundamental Data Quality Management Practices

1.	Data Quality Assessment - To understand the impact of poor data quality affects the business and to develop a business case for data quality management.
	Business Impact Analysis Data Profiling Anomaly Review Define Measure of Data Quality Prepare Data Quality Assessment Report
2.	Data Quality Measurement and Metrics - To analyse the assessment result and focus on the data elements that are deemed critical based on the selected business users’ needs.
	Select Dimensions of Data Quality  Define Data Quality Metrics Define Data Validation Rules Set Acceptability Thresholds Devise Data Quality Scorecard
3.	Integrating Data Quality into the Application  - To integrate data requirements analysis across the organisation and to engineer data quality into the System Development Life Cycle (SDLC).
	Data Quality Requirements Analysis Enhance the SDLC for Data Quality Integrate Data Quality Improvement Methods
4.	Operational Data Quality Improvement - Data Stewardship procedures are used to manage identified data quality rules and conformance to the acceptability thresholds.
	Data Standards Management Active Metadata Management Data Quality Inspection and Monitoring Define Data Quality Service Level Agreement
5.	Data Quality Incident Management - To review the levels of acceptability, report, log, and track issues, and document the process for remediation and improvement.
	Data Quality Issue Reporting and Tracking Root Cause Analysis Data Cleansing Process Remediation

Summary of Data Quality Management

Plan

1. Strategy & Approach
2. People 
3. Process
4. Technology
5. Priorities

Improve

1. Analyze
2. Measure
3. Solution
4. Execute
5. Communicate

Control

1. Focus
2. Monitor
3. Maintain
4. Manage
5. Communicate

The following points should be considered when launching a data quality management initiative: 

1. Identify & Measure Data Quality: This is a key first step, as the ability to understand the up-front level of data quality will form the foundation of the business rules and processes that will be put in place. Without performing an upfront assessment, the ability to effectively implement a data quality strategy will be negatively impacted. From an ongoing perspective, the data quality assessment will allow an organization to see how the data quality procedures put in place have caused the quality of the data to improve.
2. Define Data Quality Rules & Targets: Once the initial data quality assessment is complete, the second part of the analysis phase involves score-carding the results in order to put success criteria and metrics in place for the data quality management initiative. From an ongoing perspective, this phase will involve performing trend analyses on the data and the rules in place to ensure the data continues to conform to the rules put in place through the data quality management initiative.
3. Design Quality Improvement Processes: This phase involves the manipulation of the data to align with the established business rules. Examples of potential improvements include: standardization, removing noise, alignment of product attributes, measures or classifications.
4. Implement Quality Improvement Processes: Once the data has been standardized, the second phase of the enhancement process involves the identification of duplicate data and taking action based upon the business rules that have been identified. Since data quality is an iterative process, the rules to identify and address duplicate data will continue to evolve with an organization.
5. Monitor Data Quality Versus Targets: The ability to monitor the data quality processes is critical, as it provides the organization with a quick snapshot of the health of the data within the organization. Through analysis of the data quality scorecard results, a data governance committee will have the information needed to confidently make additional modifications to the data quality strategies in place if needed. Conversely, the scorecards and trend analysis results can also provide the peace of mind that data quality is being effectively addressed within the organization.

Data Quality Management Challenges
Deploying a data quality management program is not easy; there are significant challenges that must be overcome.Some of the most significant reasons companies do not pursue a formal data quality management initiative include: 

• No business unit or department feels it is responsible for the problem.
• It requires cross-functional cooperation.  
• It requires the organization to recognize that it has significant problems.
• It requires discipline. 
• It requires an investment of financial and human resources. 
• It is perceived to be extremely manpower-intensive. 
• The return on investment is often difficult to quantify.