Why Data Criticality Matters for a Successful Data Quality Program
Criticality: Data Quality programs should focus on the data most critical to the enterprise and its customers. Priorities for improvement should be based on the criticality of the data and on the level of risk if the data is not correct.
Managing Data Quality Across the Entire Data Lifecycle
Lifecycle Management: The quality of data should be managed across the data lifecycle, from creation or procurement through disposal. This includes managing data as it moves within and between systems (i.e., each link in the data chain should ensure data output is of high quality).
Why Prevention Is Better Than Correction in Data Quality
Prevention: The focus of the Data Quality programs should be on preventing data errors and conditions that reduce the usability of data; it should not be focused on simply correcting records.
Root Cause Analysis: The Key to Sustainable Data Quality
Root Cause Remediation: Improving the quality of data goes beyond correcting errors. Problems with the quality of data should be understood and addressed at their root causes, rather than just their symptoms. Because these causes are often related to process or system design, improving data quality often requires changes to processes and the systems that support them.
The Role of Governance in Building High-Quality Data Environments
Governance: Data Governance activities must support the development of high-quality data, and Data Quality program activities must support and sustain a governed data environment.
Using Standards to Drive Consistent D.Q
Standards-Driven: All stakeholders in the data lifecycle have data quality requirements. To the degree possible, these requirements should be defined in the form of measurable standards and expectations against which the quality of data can be measured.
The Importance of Objective Data Quality Measurement and Transparency
Objective Measurement and Transparency: Data quality levels need to be measured objectively and consistently. Measurements and measurement methodology should be shared with stakeholders since they are the arbiters of quality.
Embedding Data Quality Standards Into Business Processes
Embedded in Business Processes: Business process owners are responsible for the quality of data produced through their processes. They must enforce data quality standards in their processes.
How System Owners Should Systematically Enforce D.Q
Systematically Enforced: System owners must systematically enforce data quality requirements.
Connecting D.Q to Service Level Agreements (SLAs)
Connected to Service Levels: Data quality reporting and issues management should be incorporated into Service Level Agreements (SLA).
