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Unformatted text preview: ected like dirty laundry that is
thrown into a machine and left unattended to come out
spotless a few hours later. Data quality isn’t autopiloted
by a machine tool, and nor is it just a software issue. It’s a
business issue about managing a corporate asset.
Like any other management discipline, data quality is
inextricably linked to a process driven by operational
(transactional) or strategic (analytic) business needs.
This is where the role of policies and practices becomes
a determining factor in implementing data quality
improvements. Typically part of a broader corporate data
governance or risk management initiative, or set up to
ensure that increasingly stringent regulatory compliance
mandates are being followed, it is these policies that govern
how automated data quality works. Involve business and IT equally in data quality
Data quality is not only an IT problem, it’s also a business
problem. Both functions have different perspectives,
and the successful improvement of data quality requires
close cooperation and collaboration between the two.
While improving data quality is deemed a necessary
evil for curing the ills of poor information management
discipline, it is often perceived and implemented as just
another reactive IT project. The sticking point appears to
be lack of communication and understanding. Somewhere
between IT implementations and the business applications
and processes they are intended to support, data quality
has become lost in translation. When data management
professionals talk of metadata models and profiling metrics,
business users can tune out. On the other hand, when
business users go off on tangents about lead metrics and
sales initiatives, the IT staff’s attention can wander.
Getting data quality teams to look at the project from
a business perspective rather than a narrow technical
perspective can be a tricky exercise in relationship building.
Lines of data quality control and collaboration must be
drawn and acknowledged by both sides: the business owns
the data, while IT is merely its custodian. Embed data quality into t...
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- Fall '13