Corporate data cannot simply be collected like dirty

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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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