Enhancing data quality takes a balanced mix of processes and technology as well as good top-level management involvement.
FREMONT, CA: As data is becoming a vital part of business operation, the quality of the data that is gathered, stored, and used during business processes will decide the success of the business today and tomorrow. In the present era of digital transformation, the support for focusing on data quality is even better than it was before.
In data quality management, the aim is to use a balanced set of remedies to prevent future data quality issues and to purge data that does not meet the data quality key performance indicators. The data quality key performance indicators will usually be measured on the business data within the data quality dimensions as data uniqueness, data completeness, data consistency, data conformity, data timeliness, data precision, data accuracy, data relevance, data validity, and data integrity.
A data governance framework must be there to tailor data policies and data standards that set the bar for what data quality key performance indicators are needed and which data elements should be addressed. This comprises what business rules must be adhered to and underpinned by data quality measures. A business glossary is another valuable outcome from data governance used in data quality management. The business glossary is the primary tool to establish the metadata used to achieve ordinary data definitions within a firm and eventually in the business ecosystem where the business operates.
Data quality management is a necessity of business, and with discipline, attention to detail, and the right safety net, an organization’s data quality management strategy can save it from the repercussions of false, lost, and deleted data.
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