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Data Quality Objectives

Need to manage data quality objectives and want practical standards and best practices for information management governance and accountability?

The objective of data qualityData Quality Objectives management is to ensure customer requirements for quality are met in order to:
  • Base decisions on fact;
  • Assist in prioritizing corrective action;
  • Assist in determining the source of quality problems;
  • Affirm or deny that solutions achieve or exceed intended goals; and
  • Provide clarity
Once measurements have been taken, all metrics should be defined in the context of an appropriately approved method for assessing stability of the environment.

All metrics should be reported periodically as established by legal regulations, business rules, or exceptionally when urgent or special causes exist.
What is metric management?

All metrics used for information quality measurement and reporting should meet the following criteria:
  • All metrics that are routinely reported within an organization should be adequately controlled;
  • Recommended methods for controlling metrics may include documenting metrics in a repository or lexicon, which includes; a set of agreed upon metric attributes, establishing appropriate segregation of duties, and approval; and
  • Each metric should have the following metric attributes documented
Metric Attributes
Metric Name Identifier (abbreviation) or full English name of a given data element used for providing performance information or performance measurement;
Metric Definition
  • the concise and complete description of the business purpose or function of the agreed-upon standard measure (metric) that is used for governance and oversight;
  • The description may contain multiple specific contexts. For information quality metrics, these contexts may include either business processes or technical environments
Calculation Formula(s) used to derive values and/or fields within a spreadsheet or database.
Data Elements A context for the information presented (e.g., $ Amt Delinquent, % Outstanding).
Data Source The most immediate system/database/spreadsheet from which the data is obtained
Metric Owner Accountable for the definition of the metric, ensuring the metric is current and consistent with regulatory guidance, and all changes made to the metric.

What are data quality objectives?

The measurement of information quality assumes and depends on the measurement of data quality.

Data quality objectives should show whether the information is meeting customer needs as follows:
  • The metric value falls explicitly between the upper and lower control limits specified by the business logic and business rules requirements, design documentation and application architecture or business logic for which the information is being used;
  • The information has supported decision-making over time.  Here the measure may be an indirect indicator, i.e. number of repeat users of a given report; and
  • Data profiling activities have confirmed suitability of the data for future intended uses.
The default metric for the quality of data being measured is defects per million (dpm).  The upper and lower control limits for defect ranges should be set by the specific requirements of the data’s purpose, in accord with the data quality attributes as identified below.

Data Quality Objectives
Accessibility retrieved as needed by appropriate individual
Accuracy exact and precise
Completeness fulfilling formal requirements and expectations with no gaps
Timeliness current and not outdated or obsolete
Integrity what was requested and expected
Validity obtained via an approval process
Consistency commonly defined and used across the enterprise
Relevance Fits intended use

How should we select samples for measurement?

Data selected for data quality measurement reporting should be prioritized based on both of the following criteria:
  • The cost and/or risk to the enterprise of the particular data’s current quality; and
  • Availability of statistically significant samples.
Measurements should be taken using tested and rigorous sampling methodologies in order to accomplish specific objectives set by the business customers of the data or information.

What are some requirements for data quality management?
  • The organization should plan, acquire, implement, and control data and information for the sake of enabling information value and cost chains to produce the highest quality data at optimal speed and cost;
  • These measurements will be considered in-line measurements;
  • Each information value and cost chain begins with end-customer, shareholder, and regulatory satisfaction; 
  • Such satisfaction metrics taken outside the data quality management domain should be treated as primary indicators of data and information quality value;
  • Where possible, data and information quality metrics should be aligned with a corresponding set of defined process outputs; 
  • Measuring quality strictly within one organization or business area should be treated as one step in a process of managing data and information quality so that end-customers, shareholders, and regulators report satisfaction across organizational boundaries. 
Quality of data or information may degrade as it is handed from one process to another. Under these circumstances, data quality metrics should enable any information customer to specify quality requirements.

Requirements for the data quality scope and measures should be methodically captured from IT sponsors and information users.

What are some general guidelines?
  • Appropriate documentation should be developed and maintained by the business areas for all processes and procedures;
  • This documentation should be subject to the appropriate senior management review and approval; and
  • Accountabilities for each key role involved in data quality management should be defined and communicated to all stakeholders.
Data quality objectives checklist

Data quality standards and best practices should also consider the following:
Data quality business intelligence
Data warehouse quality
Quality assurance plan
Data quality strategy
Data quality assurance
Data quality tools
Standards for quality management information
Ensure that accountabilities for for test scripts are defined in project test plans.
Standards for data quality should be established.
Ensure that quality assurance testing includes security testing.
Define roles for quality management
Define requirements for user acceptance in the requirements analysis phase.
Ensure that production tests are conducted prior to moving new application code to the production environment
Best practices and procedures for identifying and correcting quality issues should be established.


Data management is a sub-set of information management that governs organization and control of the structure and design, storage, movement, security and quality of information.

Standards and best practices are required to ensure data quality objectives, rapid project delivery and optimal return on information management investment

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