Data environments come in all shapes and sizes, but the common denominator among organizations is their reliance on data to help them make strategic business decisions for efficiency, profitability and growth. No matter the architecture or environment—be it an enterprise data warehouse, data lake, data hub, enterprise service bus, data transformation layer or a data mart—one thing is certain: if data is not properly managed and maintained, then it is essentially useless.
Data environments are complex. Data is continually in motion, flowing along the data supply chain, and being created, ingested and leveraged for use. As data moves across systems, processes and environments, its quality is consistently at risk. Threats to data integrity can discourage data usage among data consumers and result in inaccurate and unreliable decision-making.
Businesses that have poor quality data are in danger of making business decisions based on inaccurate, incomplete and inconsistent data, which can result in a diminished bottom line, negative customer experiences, tarnished reputation and falling behind the competition. However, a comprehensive data governance program can ensure the ongoing accuracy and integrity of data assets that are easily accessible to business users for reference and analysis.
Data governance and data quality are often approached by organizations as two independent data management tasks, but they’re not. Data governance can provide a comprehensive framework for enabling data access, understanding and accountability across an organization. Yet it also incorporates data quality assurance across the data supply chain.
Standalone data quality tools typically include parsing and standardization, cleansing, profiling and monitoring capabilities to improve data integrity. However, when data quality capabilities are integrated into a comprehensive data governance solution, organizations can increase visibility and automation while also improving the usability and reliability of data. Once integrated, organizations can continuously monitor data integrity for improvement while also providing data quality scores to business users, so they know the value of specific data assets.
Enacting continuous data quality monitoring and improvement within a data governance program requires advanced analytics and machine learning. Advanced analytics can be used to identify outliers that traditional data quality rules might not be able to detect. As data is monitored and data outliers are identified and resolved, machine learning algorithms can enhance quality levels through self-learning capabilities to build increased data trust and usage among business users. When data integrity improvement is automated through a complete data governance program, organizations can ensure that no matter how their data is transformed or redeployed across the enterprise, it will continue to be subject to quality standards to ensure its ongoing accuracy, consistency and reliability.
A comprehensive governance program with data controls, quality scores and ongoing monitoring can prevent many downstream data-related problems and help reduce data misunderstanding and misuse. But without the right enabling technology, none of this is possible.
A successful data governance program requires a comprehensive solution suite to maximize both the organization’s data quality and the insights it provides. The solution suite should include data governance capabilities to engage all relevant parties within the organization and clearly define varying roles and responsibilities among data owners, stewards and business users. It should provide business users with a complete 360-degree view of their data environment, allowing users to easily define data and associated business terms, track data lineage and manage all aspects of their data assets.
The solution suite should also include data quality capabilities to conduct high-volume data quality checks such as data profiling, consistency, conformity, completeness, timeliness, reconciliations, and visual data prep to verify the quality of data and ensure continued trust among business users. In addition, the solution suite should combine advanced analytics capabilities and apply machine learning algorithms for self-learning to continuously improve data quality by finding outliers that traditional rules may not catch.
With capabilities for data governance, data quality and analytics, the platform should facilitate a full understanding of an organization’s data landscape, enabling all data stakeholders to effectively manage, share, and utilize data to drive growth and increase profits. Ultimately, business users will have confidence in both the integrity of their data assets and their data knowledge, unlocking a plethora of reliable information to help improve strategic decision-making.
For more information on a comprehensive data governance solution, download the data sheet below.
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