Data Quality Objectives
manage data quality
objectives and want practical standards and best practices for
information management governance and accountability?
objective of data
management is to ensure customer
requirements for quality are
met in order
Once measurements have been taken, all metrics
should be defined in the context of an appropriately approved method
for assessing stability of the environment.
decisions on fact;
- Assist in prioritizing corrective action;
- Assist in determining the source of quality
- Affirm or deny that solutions achieve or
exceed intended goals; and
- Provide clarity
All metrics should be reported periodically as
established by legal regulations, business rules, or exceptionally when
urgent or special causes exist.
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
- Recommended methods for controlling
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
||Identifier (abbreviation) or full English
name of a given data element used for providing performance information
or performance measurement;
- the concise and complete description of
business purpose or function of the agreed-upon standard measure
(metric) that is used for governance and oversight;
description may contain multiple specific contexts. For information
quality metrics, these contexts may include either business processes
or technical environments
||Formula(s) used to derive values and/or
fields within a spreadsheet or database.
||A context for the information presented
(e.g., $ Amt Delinquent, % Outstanding).
||The most immediate
system/database/spreadsheet from which the data is obtained
||Accountable for the definition of the
metric, ensuring the metric is current and consistent with regulatory
guidance, and all changes made to the metric.
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
- 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
- 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
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 profiling activities have confirmed
suitability of the data for future intended uses.
||retrieved as needed by appropriate
||exact and precise
||fulfilling formal requirements and
expectations with no gaps
||current and not outdated or obsolete
||what was requested and
||obtained via an approval process
||commonly defined and used across the
||Fits intended use
should we select samples for measurement?
Data selected for data quality
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
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
- Availability of statistically significant
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
- Each information value and cost chain
begins with end-customer, shareholder, and regulatory
- 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
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.
- 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
Requirements for the data quality scope and measures should be
methodically captured from IT sponsors and information users.
are some general guidelines?
- Appropriate documentation should be
developed and maintained by the business areas for all processes and
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
for quality management
that accountabilities for for
test scripts are defined in project test plans.
for data quality should be
that quality assurance testing
includes security testing.
roles for quality
requirements for user
acceptance in the requirements
tests are conducted prior
to moving new application code to the production environment
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