|
What is Data Quality Management?
Need to
manage data quality
management 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.
Data quality
management checklist
Be sure to address most of the following items while completing this
portion of the information management strategy study. This will help
formulate requirements for change.
 Are
metrics that are routinely
reported within a organization adequately
controlled?
Do
methods for controlling
metrics include documenting metrics in a repository
or lexicon, which includes, a set of agreed upon metric attributes, establishing appropriate
segregation of duties, and approval? Are metric
attributes documented?
Do data
and information quality metrics 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.
Are data quality attributes
documented?
How are samples for measurement
selected e.g. 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.
Are measurements taken using
tested and rigorous sampling methodologies in order to accomplish
specific objectives set by the business customers of the data or
information.
Does the organization 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?
Quality
of data or information may
degrade as it is handed from one
process to another. Under these circumstances, do data quality metrics
enable any information customer to specify quality requirements?
Are requirements for data quality
scope and measures methodically captured from IT sponsors and
information users.
Is appropriate
documentation developed and maintained by the business areas for all
processes and
procedures?
Is this
documentation subject
to the appropriate senior management review and approval?
Are accountabilities
for each key role
involved in data quality management defined and communicated to
all stakeholders?
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.
Data
quality management standards and best practices are required to ensure
rapid project delivery and optimal return on information management
investment
|
