The hallmark of data quality is how well data supports the context in which it's consumed. Your legal department, for example, may use "Informatica Company" while your finance department uses "INFA" and both records are of equal quality.
Quality is a relative and never-ending judgment, one that needs to be defined by the business (or business unit) that's consuming the data. An essential element of holistic data governance, trustworthy data serves critical business needs across the enterprise-from legal to finance to marketing and beyond.
Driving data quality requires a repeatable process that includes:
- Defining the specific requirements for "good data," wherever it's used.
- Establishing rules for certifying the quality of that data.
- Integrating those rules into an existing workflow to both test and allow for exception handling.
- Continuing to monitor and measure data quality during its lifecycle (usually done by data stewards).
And because rules and needs change and new systems can be added to the mix, truly successful data quality initiatives need to be scalable to address those new requirements.
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