Ensuring Data Quality Within a Comprehensive Data Governance Framework

How Data Governance Manages Data Quality Along the Data Supply Chain

Chris ReedDecember 5, 2018

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Perhaps you’re familiar with the term, “data supply chain.” Here at Infogix, we’re actually quite fond of it, because it provides a handy frame of reference for many people. You don’t have to be involved in manufacturing to have a basic understanding of what a supply chain is and how it functions. It often provides an apt illustration of how data moves through organizations as well. Data, just like widgets, could come from an outside source or be created internally, is sometimes altered along the way, and is often subject to many processes along its journey. And just like any widget, it may get dinged and damaged along the way.

From the second data enters an organization’s data landscape, its quality is at risk. As data flows through various processes, systems and environments, its integrity may be threatened, posing a variety of operational hazards. Without high-quality data, organizations can’t make informed business decisions or ensure regulatory compliance. The result? Flawed conclusions, lost revenue and frustrated customers.

When it comes to real supply chain management, quality control is essential. All along the way, quality is repeatedly checked to ensure the continued integrity of the product. In data supply chain management, many organizations also use a data quality tool to monitor data integrity, but they fail to leverage the power of integrated data management capabilities for maximum returns. The best strategy is to ensure data quality across the data supply chain through a comprehensive data governance framework.

An All-Inclusive Data Governance Framework

In the realm of data management, historically data tools were often deployed at a project level. Because budgets were frequently handled at the departmental or line of business level, data management efforts were scattered and siloed. But with today’s advanced tools, organizations can gain more value and help build a data-driven culture by having data governance and data quality work together. For example, when data quality is improved within a solid data governance framework, it not only assures data’s accuracy, completeness and relevance, but also addresses the challenge of data credibility.

 It is critical for organizations to build a culture of collaboration and improve data understanding and utilization amongst everyone in the organization. To do so requires data quality capabilities for parsing and standardization, cleansing, profiling and monitoring to improve data quality. However, these requirements must also occur within a comprehensive data governance framework.

Data governance serves many functions, 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. By establishing a clear understanding of data assets, including where that data came from and how it is used, as well as tracking and monitoring data quality scores, business users are empowered to increase utilization of data assets.

The problem with disparate tools is that they don’t work together at an enterprise level to encourage unified understanding, collaboration or communication. If they aren’t working in lock-step, they can’t help foster a data-driven culture or work in concert as a cohesive data management strategy to drive innovation or competitive advantage. To do that, you need an integrated, enterprise approach that delivers a multitude of capabilities.

Ditching Disparate Tools

To ensure data quality throughout the data supply chain on an enterprise level starts with an integrated solution built on a solid data governance framework. From when data is first ingested or created, you want that data to be easily understood, accessed, and trusted by business users every step of the way so that it may be leveraged to generate insights and drive business decisions.  To understand what your data means and its quality level, you must understand its attributes, lineage, metadata, and quality. Enterprise data governance delivers these key capabilities, plus the integrated data quality monitoring and improvement your users demand.

Data quality capabilities within an integrated solution should enable high-volume data quality checks such as data profiling, consistency, conformity, completeness, timeliness, and reconciliations 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 integrity.

With a multitude of capabilities, the solution suite should also facilitate a full understanding of an organization’s data landscape. This enables data owners, stewards and consumers to effectively manage, share and utilize data to drive growth and increase revenue.

If you would like to learn more about ensuring data quality within a comprehensive data governance framework, download the data sheet below.

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