No matter where organizations store their data—whether in data lakes, warehouses or cloud platforms—managing that data is always a challenge. Organizations recognize the power of enterprise-wide data governance to better manage data, mitigate risk and maximize data value, but the importance of integrated data quality is often overlooked.
As businesses continue to create and ingest data at exponential rates, data quality is increasingly at risk. By the time companies recognize declining data quality, reputational, regulatory and operational damage has already been done. Consequently, business users no longer trust either data or analytic results. Poor data leads to inaccurate decisions, errors and wasted time and money.
The integrity of data is paramount to successful data-driven initiatives. Accurate, consistent and trustworthy data is used to produce valuable business insights, mitigate regulatory risk, achieve operational efficiency and increase organizational value. Bad data systemwide and across the data supply chain can only derail business initiatives and poses significant operational, financial and reputational liabilities.
To effectively confront data integrity challenges, companies need to not only establish a comprehensive data governance framework, but also prioritize data quality.
There are obvious synergies between data governance and data quality. Data governance is an effort to officially align people, processes and technology to empower businesses to utilize and take advantage of data. On the other hand, data quality is a calculation of data’s accuracy, completeness, consistency, trustworthiness and usability. Combined, they ensure that all data users can quickly find, understand, use and trust data to enhance decision-making, achieve business objectives and drive ROI.
An enterprise data governance program with an integrated data integrity strategy provides end-to-end data quality from data creation through consumption. Data integrity builds user trust among IT and business users who are using data as a basis for critical decisions. Comprehensive quality rules between sources and systems assure that data integrity is maintained throughout the data supply chain.
As the business and technology landscape rapidly change due to increasingly burdensome regulatory requirements and explosive growth in transactional data, companies require a streamlined and scalable approach to data quality.
Success with data requires a well-designed and sustainable data governance program that features integrated, comprehensive data integrity initiatives. There are five critical steps that organizations must follow to incorporate data quality into their data governance initiative, including:
Data quality improvement requires a continuous operation involving people, processes and technology that works in collaboration to turn data into meaningful actionable business intelligence. Data quality-powered data governance is key to making the most of an enterprise data quality program and cultivating success with data.
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