Organizations across every industry have complex data supply chains, and from the moment data enters them, whether it’s ingested from external sources or created internally, its quality is at risk. Data storage environments, systems, and processes can all threaten data’s integrity, which poses a major challenge for every enterprise, as trustworthy data is critical to supporting accurate and reliable decision-making. When important business decisions are based on inaccurate, poor quality data, it can lead to flawed strategies and erode customer satisfaction, leading to customer churn, a damaged reputation, and lower adoption rates.
Making matters worse, business users no longer have faith in their data, conversely leading to lower data utilization, a lack of compelling analytical insights, or questionable analytic outcomes. Without data trust, business users may make important decisions based on gut feelings instead of hard, analytical evidence.
Organizations, large and small, need to expeditiously find, acquire, and protect high-quality data to inform complex business decisions. Businesses that don’t have trustworthy data are at risk of affecting their bottom line and significantly falling behind the competition. Strong data governance can help ensure the ongoing accuracy and quality of data, and that it is fully accessible to business users to be used quickly and efficiently.
Ensuring Data Quality with Data Governance Standards
Data governance is more than just a tool organizations deploy to overcome an isolated obstacle. It should provide a comprehensive framework for enabling data access, understanding, and accountability across an enterprise. And in doing so, it should incorporate and integrate data quality assurance across the data supply chain. Unlike standalone, traditional data quality tools that typically include parsing and standardization, cleansing, profiling, and monitoring capabilities to improve data quality, data quality within a structure of data governance is all about increasing visibility and automation while improving the usability and reliability of data. Beyond the standard tactics for ensuring data quality, data quality within the context of data governance can help with data accuracy and integrity in two fundamental ways – not only provide ongoing quality monitoring and improvement, but also provide data quality scores so business users know the exact quality dimensions of available data assets.
To achieve data quality monitoring and improvement, data governance can be enabled with machine learning algorithms. As data is monitored and data quality issues are identified and resolved, these algorithms can improve quality levels through self-learning to build trust and utilization among business users. Automated data quality improvement as part of a robust data governance platform ensures that no matter how data may be transformed or redeployed across an organization, it will continue to be subject to quality standards to ensure its ongoing accuracy, consistency and reliability.
Building user trust in data assets is important to encourage utilization, and data quality is key to generating meaningful insights. But quantifiable quality scoring is really critical if an organization wishes to conduct risk assessments, quantify security risks, or monetize their data assets. Data quality in data governance allows for the calculation of data quality scores to accomplish these goals, and also buttress appropriate usage of and confidence in data assets.
A comprehensive governance framework with data controls, quality scores, and ongoing monitoring can prevent many downstream data-related problems and help reduce data misunderstanding and misuse.
Successfully Executing Data Governance
Successful data governance requires a business-centric, enterprise-wide strategy and 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 provide business users with an all-inclusive view of their data landscape, allowing users to easily define data and associated business terms, track data lineage, and manage all aspects of their data assets—enabling collaboration, knowledge-sharing, and user empowerment through transparency across the enterprise.
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, visual data prep and machine learning to verify the quality of data and ensure continued trust among business users. In addition, the solution suite should combine analytics capabilities and apply machine learning algorithms for self-learning to continuously improve data quality.
Organizations must engage all relevant parties within the organization and clearly define varying roles and responsibilities among data owners, stewards, and business users. Data governance can’t fall on just IT or one department. It requires a collaborative approach across the organization from both business users and IT to align data governance with business objectives and ensure a full understanding across the entire enterprise. When business users have confidence in both their data quality and data knowledge, they know the insights generated from data are reliable and will improve strategic decision making.
With capabilities for data governance, data quality and analytics, the solution suite should also facilitate a full understanding of an organization’s data landscape, enabling data owners, stewards and consumers to effectively manage, share, and utilize data to ultimately drive growth and increase profits.
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