With the ever-increasing amounts of data, its structures, relationships and sources, the need to address risk related to evolving privacy laws, plus, needs from multiple users across lines of business, it’s no surprise that bad data prevails. Bad data that proliferates within data systems and across the data supply chain will derail data-driven initiatives throughout the entire enterprise.
As the most strategic asset to an organization, measuring its suitability and availability is paramount to successful business outcomes. Accurate, consistent, complete and reliable information is used to produce valuable analytical insights, achieve operational excellence and increase data valuation. But managing such large amounts of information stored in various lakes, warehouses and sometimes swamps is often a demanding and overwhelming task.
Here’s the good news. Establishing a comprehensive and sustainable data governance program that features an integrated, automated and comprehensive data quality solution can solve many integrity-related data challenges.
Effectively confronting today’s data quality threats means implementing an all-inclusive data governance framework that integrates and prioritizes information value as a critical enterprise objective. A key component of information value is data quality. Automating data quality checks across the data supply chain, prioritizing possible inaccuracies and reconciling data are just the tip of the iceberg when considering an integrated approach to data governance.
To start, organizations must understand their landscape, the cause and effects and causality of data. They need to identify and document essential information flows, data provisioning systems, external source systems and data lineage to create a common understanding of the key relationships, and cause and effects, between the source and target system. Source and target system owners work together to establish control points, data quality criteria and data integrity metrics for the key data elements.
Next, companies must assess integrity risk by locating, identifying and assessing critical data quality issues, pain points and risks. Once gaps and liabilities are evaluated, businesses must design information rules and exception management processes to address quality problems, and prioritize the most severe risks. Automated rules and evaluating the likelihood of possible inaccuracies based on historical data characteristics can help companies gain efficiency by looking in the right places.
Businesses can establish successful enterprise data quality with a multitude of capabilities that help conduct high-volume data integrity checks. As the amount of data and validations grow, it’s critical to verify the quality of data at scale and ensure trust among users.
Data governance capabilities also ensure all information is easily understood, appropriately accessed and entirely trusted by all users, allowing different departments to leverage this data to generate insights and drive business decisions. Ultimately, data owners, stewards and consumers can efficiently and effectively manage, share and use data to drive collaboration, growth and revenue.
Finally, a modern data governance solution should be augmented with integrated analytics capabilities and machine learning algorithms for self-learning to continuously improve data integrity. Business owners can utilize enterprise asset visualization to proactively monitor data quality indicators. They can then identify opportunities for improvements by analyzing micro-trends in data quality indicators. Automated analytics capabilities that span end-to-end business processes provide a cost-effective approach for monitoring and provide the best results for building enterprise-wide trust in data.
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