The evolution of data continues at lightning speed, with new tools and strategies emerging all the time. Big data is no longer just the concern of big business, and analytics are being leveraged by organizations of every size and industry for competitive advantage. In the midst of all of this rapid change, however, there’s one constant challenge that every organization faces: ensuring data quality. Data is continuously being created, moved, used and transformed throughout its lifecycle and across organizations’ data supply chains. And no matter what environment, system or process it is being stored in or used for, there is a need for that data to remain consistent, complete and accurate if businesses want to leverage it for reliable and meaningful business intelligence.
Data is a business’s most valuable asset. However, its value is always at risk. As data moves across a business, its integrity is in danger. If business users don’t trust their data, they won’t use it, nor should they. Leveraging poor quality data generates meaningless or even harmful “insights,” resulting in bad business decisions. Organizations need quality data to generate high-value business intelligence.
Every day, we create 2.5 quintillion bytes of data, so it’s no surprise that organizations produce data at a rapid pace. They also frequently ingest third-party data for a variety of operational functions. As data is created and absorbed, it is exposed to new systems, procedures, changes and uses, putting data quality in jeopardy. As information environments expand and grow increasingly complex, disparate applications, databases, systems, messages and files are difficult to track, making it hard to identify and solve ongoing data integrity challenges.
Organizations must measure and score the quality of data assets so business users can quickly decide what data is best for developing insights. By scoring and monitoring data quality as an integrated part of an enterprise data governance strategy, businesses prevent data integrity issues from ever occurring in the first place.
Data governance provides a framework to prevent data quality issues from developing within the data supply chain. It is about coordinating people, processes and technology to ensure data is appropriately accessible and understood by business users. That way, it remains a valuable asset. Essential to this goal is data quality. Organizations require a wide variety of data quality checks and controls throughout the data supply chain, including:
When these checks or controls are applied within a comprehensive data governance framework, businesses can easily score and monitor data integrity enterprise-wide. This prevents the proliferation of data issues and builds trust among business users. However, data governance efforts are about far more than ensuring data quality. They’re also about building data understanding across the organization. By ensuring that data is both accessible and understood among business users through the use of business glossaries, data dictionaries, data lineage and uniform policies and procedures, businesses reinforce data trust among all users.
There are obvious harmonies between data governance and data quality efforts. Data governance provides business users with understandable, well-curated data, while data quality controls ensure the integrity of data before consumption. By combining data governance and quality efforts, business users can quickly find, trust and develop meaningful insights from data.
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