In today’s connected world, organizations are generating astronomical volumes of data at accelerated rates. But whatever the size of the data environment that houses all this data—be it a traditional data warehouse or a big data environment like a data lake—the common denominator is raw data, ingested from multiple internal and external sources, and typically of unknown quality. As organizations prioritize data management tasks and deliverables, many struggle with which step to take first when it comes to their data environments: data quality or data governance.
In the realm of data management, there are numerous terms that may be used interchangeably among employees, typically due to similarities in function and usage within the ecosystem. Data quality and data governance certainly aren’t synonymous, but can sometimes be confused or conflated for good reason, as the level of data quality is often reliant upon the strength of data governance. But even when the distinction between good quality and good governance is clear, without governance, the quality of data is often misunderstood and therefore mistrusted. Consequently, organizations should ensure solid data governance within their environments to take on the challenge of data quality.
When data quality is improved within the framework of a solid data governance program, it not only assures data’s accuracy, completeness and relevance, but also tackles data credibility. Data quality tools may include parsing and standardization, cleansing, profiling, and monitoring to improve data quality, but they need to occur within a solid data governance framework. You must first establish clear understanding and ownership of data assets, including where that data came from and how it is used across all facets of the enterprise. Sensitive data must be identified and handled in accordance with regulatory and compliance rules, and responsibility and accountability must be established. Data assets are valuable, and that value is only rivaled by the expertise within an organization’s human capital. It is critical to leverage these together, and identify data stewards, owners, and stakeholders who can define usage and act as resources for business users. All of this builds a culture of collaboration and improves data understanding and utilization.
Data governance serves many functions, including establishing a business glossary and data dictionaries, tracking data lineage, ensuring compliance, preventing data breaches and protecting data assets, and creating uniform policies and procedures. But fundamentally, it is about laying the foundation for data understanding. Data understanding ensures that data is used appropriately to maximize value and mitigate risk, yet it also encourages business users to increasingly leverage those data assets for analytics that can lead to critical business insights. Business users will only increase utilization when they not only understand those assets, but trust their quality as well—and that’s where data quality comes in. Data quality can be tracked, scored and monitored as part of a comprehensive data governance solution.
Data governance provides data organization and understanding in a clear-cut framework of responsibilities, policies, and standards. But data assets need to be continuously managed throughout the data supply chain, as data can be transformed and new uses for data will evolve over time. Data lineage and definitions are not static, and as data moves from sources through systems, data quality can often be impacted.
“Protecting” data assets refers not just to data security, but to a commitment to preserve and improve data integrity. A comprehensive governance program with data controls and quality monitoring can prevent many downstream data-related problems and help reduce data misunderstanding and misuse. Clearly defined workflows and user-friendly visualizations will also increase accountability and provide for smooth issue escalation and resolution.
The right approach to data governance requires a business-centric, enterprise wide strategy to maximize both the quality of an organization’s data and the insights it provides. The solution should facilitate a comprehensive understanding of an organization’s data landscape, enabling data owners, data stewards and data consumers to effectively manage, share, and utilize data to extract maximum business value. In addition, the solution should incorporate analytics and apply machine learning algorithms to identify data quality gaps across an enterprise.
Ultimately, improved data quality is a key benefit from the implementation of a comprehensive data governance framework. But data governance comprises the broader, strategic, enterprise vision of recognizing and managing data as a valued enterprise asset.
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