Data quality issues have existed for as long as data. Incomplete, incorrect, duplicate or outdated data all degrade data integrity. While data management continues to evolve and enable companies to leverage data more effectively and efficiently than ever before, the evolution of data has also increased the quality risks to data. The exponential growth of data, the rising popularity of streaming data and event-driven architectures, and the increasingly complex regulatory landscape all contribute to the epidemic of bad data. Poor quality data can proliferate within data systems and environments across the data supply chain, result in unreliable analytics outcomes and derail data-driven initiatives throughout the entire enterprise. As the value of data as a strategic asset continues to grow along with the potential operational, financial and reputational risks of bad data, the C-suite is finally prioritizing data quality.
Data analytics has become a vital asset in most organizations’ competitive arsenal, delivering powerful business intelligence to achieve business objectives, enhance operational efficiency and drive innovation. But the massive scale and accelerated speed of data present significant data management and data quality challenges that can hinder analytics efforts. Organizations must manage large amounts of information stored in data lakes, warehouses and sometimes swamps. They must manage data access and assure data understanding so business users leverage the right data at the right time. And they must ensure data is accurate, consistent, complete and reliable to produce meaningful business insights and build user trust and data utilization.
The good news is, establishing a business-friendly enterprise data governance program that features integrated, comprehensive data quality mechanisms can optimize data insights and help solve some of the most persistent challenges in data management.
Just as siloed data can derail data management efforts, so too can siloed data technologies or tactics. Organizations can no longer afford to approach data governance as a project of limited scope; it must be an enterprise framework that mitigates risk, fosters a data-driven culture and promotes data usage and understanding. Data quality supports all of these efforts, ensuring regulatory compliance, encouraging data usage and enhancing data value. This is why the most successful data governance programs include data quality as an integral component.
An effective quality-powered data governance program requires organizations to first identify and document essential information flows, data provisioning systems, external source systems and data lineage to create a common understanding of the key data elements between the source and target system. Source and target system owners work together to establish data quality criteria, metrics and rules to monitor, measure and improve data quality at key points in this data supply chain.
Next, companies must assess integrity risk by locating and assessing critical data quality issues, pain points and threats. Once liabilities are evaluated, businesses must design quality rules and exception management processes to address potential issues and prioritize the most severe risks. Automated rules established as part of an end-to-end solution help companies avoid sampling errors and gain efficiency.
Data quality-powered data governance requires a collaborative effort of both technical and business users to implement policies and processes to establish data ownership, workflows, access and usage. But it also requires a technical solution that combines the power of data governance, data quality and data analytics to increase enterprise data value.
Data quality is the key to reliable reporting, actionable business insights and better results. Accurate, consistent and reliable data is crucial to building data trust and encourages users to find new and innovative ways to turn data into value. With increased data utilization among business users, data governance is even more critical to assure that information is easily understood, appropriately accessed and effectively leveraged. Business users are increasingly using analytics to explore operational data, generate insights and drive business decisions. Ultimately, data owners, stewards and consumers can efficiently and effectively manage, share and use data to drive growth and boost cash flow.
These shared goals and synergies between data quality, data governance and data analytics underscore the advantages of an integrated platform that features all three capabilities. Data governance is grounded in data understanding, which helps users trust their data. Data quality ensures data is accurate and complete, further building user confidence in data. And analytics can be used to not only turn reliable data into trustworthy business insights, but also apply machine learning algorithms within data governance to monitor and continuously improve data integrity. This iterative process of data trust, understanding and utilization is best supported by an integrated data platform built on quality-powered data governance.
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