Technically Speaking, What Is Data Governance?
Technically Speaking, What Is Data Governance?
Data governance is a wide reaching topic, so it can be hard to know exactly what it means. We try to tackle that problem in this post.
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The term data governance has been around for decades, but only in the last few years have we begun to redefine our understanding of what the term means outside the world of regulatory compliance, and to establish data standards. This rapid evolution of data governance can be attributed to businesses looking to leverage massive amounts of data for analytics across the enterprise, while attempting to navigate the increasingly rugged terrain of worldwide regulatory requirements.
Data governance is a critical data management mechanism. Most businesses today have a data governance program in place. However, according to a recent Gartner survey, “more than 87 percent of organizations are classified as having low business intelligence (BI) and analytics maturity,” highlighting how organizations struggle to develop governance strategies that do more than ensure regulatory compliance.
Instead, businesses require a sustainable, scalable, and enterprise-wide data governance approach to overcome immediate data compliance obstacles and advance their long-term analytical goals.
Data Governance Is About More Than Compliance Mandates
Data governance has always held different definitions depending on the industry, organization, executive leadership, and operational focus. For many years, data governance was relegated solely to the IT department, and dedicated to compliance and data security efforts. It had little or no impact on driving business intelligence or enhancing operational processes. But as the volume, value, and role of data has expanded in business, so too has the purpose and definition of data governance.
Today, it is critical that business users across the enterprise understand their organization’s data assets, including lineage, quality, function, and ownership. Each asset is a potential driver of business insights and competitive advantage. Just as important is a unified definition of data governance so that every stakeholder across the organization understands their data management roles and responsibilities.
A clear and concise explanation of data governance today is the formal orchestration of people, processes, and technology to enable an organization to leverage data as an enterprise asset.
Breaking Down Data Governance
To create an environment of data understanding and assure that data remains an asset, data governance begins by ensuring data knowledge across an enterprise, increasing communication, encouraging collaboration, and reinforcing accountability. In addition, as the sheer volume of data grows, so do the opportunities to leverage data as an asset, but only if users understand what assets are available, where to find them, and who to ask when questions arise. If users don’t understand data’s origin and lineage, they won’t trust it. Therefore, they won’t leverage it for analytics. A business glossary, data dictionary, and data lineage create a foundation of data governance by providing:
Business Glossary: Defines data, terms and business attributes.
Data Dictionary: Provides data sources, usage, relationships and dependencies.
Data Lineage: Tracks data origin, usage, and flow within systems and processes.
Data governance increases the effective usage of data, but more importantly, provides a clear understanding of valuable data assets. Business users are far more likely to use trusted and understood data, and if resources for fielding questions and clarifying confusion are easily accessible and readily available. Enterprise collaboration and transparency are also key to building that trust. Data consumers can rate the quality and usability of data assets they’ve interacted with, engendering trust and encouraging utilization among other users. In addition, data assets are leveraged more appropriately because greater user understanding assures the right data will be chosen for the right analysis.
Data governance also solves a long-standing challenge shared by many businesses, the preservation of institutional knowledge. Often, policies, processes, procedures, and other information are stored in human memory rather than enterprise repositories. As a result, organizations may lose critical information due to employee attrition. But with data governance, businesses catalog and collaborate with information, thus ensuring that institutional knowledge is captured and curated.
As with many business initiatives, data governance is often driven by an organizational obstacle or challenge. A regulatory fine is imposed, a new process fails, or a new regulation is passed and suddenly a new data governance initiative is on the table. However, data governance is not a single project. It is an ongoing program.
Data Governance Never Ends
Data governance requires a proactive approach. It shouldn’t be a reactionary measure because a data quality challenge emerges or a regulatory fine is given. Instead, implementing data governance requires building out a centralized, enterprise strategy with distributed accountability throughout the organization to avoid data challenges before they arise.
Understanding is the foundation of data governance, and knowledge begins with enterprise-wide transparency. Clarity into an organization’s data landscape depends on a centralized catalog of information, ideally bolstered by automated workflows, to streamline ongoing communication.
With information cataloged, data is defined across the entire organization, data owners and stewards can focus on operationalizing governance, and business users have a single interface in which they can quickly and easily access the information they need. All stakeholders will benefit from an ongoing, centralized approach to data governance to keep data up-to-date, accurate, and reliable.
Data is of no value if it isn’t understood and isn’t continuously refined. Centralized data governance accomplishes both, promoting accountability and collaboration to build a business that can rely on high-quality data assets, as well as data stewards and owners who support business users. The results are valuable business insights and smarter operational and analytical decisions.
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