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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
quality
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.
Summary...
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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