Developing a Mature Data Governance Model

Growing Data Governance to Yield Maximum Returns

Emily WashingtonJuly 25, 2018

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Keeping enterprise data working effectively for your organization isn’t just about the data itself—it’s about ensuring the health of the entire data ecosystem as information flows and functions across the data supply chain continuum. Fundamental to those data management efforts is data governance, which, at its core, delivers visibility into, strengthens accountability for, and enables utilization of an organization’s data assets. Data governance often starts out as a manual documentation exercise on spreadsheets or in SharePoint, but often is quickly abandoned or outdated as the volume of data and continued attention required make these efforts neither scalable nor sustainable.

To remain competitive, organizations must continuously mature their data governance model, or the health of enterprise-wide data will quickly deteriorate. Evolving data governance means learning from past mistakes and building upon previous steps to progress toward an automated data governance program focused on business process automation. Ultimately, data governance should adapt to a business audience who is eager to understand data governance from a business standpoint.

Data Governance Fundamentals

At the initial stages, data governance should provide data definitions and define ownership/stewardship responsibilities for an organization’s data assets. Oftentimes, organizations begin documenting this information using basic tools like desktop spreadsheets, wikis or other collaboration tools. Often, these efforts are conducted by business stakeholders and confined to a specific department or project. Within IT, governance is often concerned with risk mitigation from a compliance perspective and managing and understanding technical data lineage, so they know how data has moved and transformed over time to ensure regulatory compliance. However, this “strategy” silos business units and IT and fails to leverage data as a critical business asset.

As a result, organizations seek to converge both business and technical data worlds with a singular view of enterprise data, basic lineage and workflows to manage and provide transparency into all data assets. Instead of using simple tools to document information about data, they seek out data management tools from vendors to tackle specific tasks. At first, this works great, but as the volume and depth of data increases, due to the introduction of new technologies like big data stacks, streaming data and data lakes, these tools quickly become overwhelmed.

With GDPR now in effect and other regulatory mandates such as BCBS 239, CCAR, Solvency II and MiFID all emphasizing data, organizations must quickly mature their data governance model to ensure compliance. Business users must also find new and creative ways to use data, which means it is critical they have the ability to quickly and easily search, request and access enterprise data to keep pace in an increasingly competitive marketplace.

Data Governance Maturity

Today, a mature data governance model should simplify the complexity users face when searching for data with an intuitive and easy-to-use interface tailored to how business users consume data. By layering in machine learning and recommendation engines into the collection, as well as validation, reconciliation, and statistical controls of data sets, organizations can facilitate fast and easy access to data that is appropriate, timely, and accurate.

Achieving a mature data governance model requires organizations to eliminate previously siloed technologies so data management tools provide an integrated solution that empowers business users and enables the most efficient and effective use of data assets. From leveraging machine learning and analytics to increase automation, with data quality monitoring to improve and maintain data accuracy and reliability, to the workflows, dashboards, and secure, self-service data access that empower data consumers to perform functions which previously required IT experience.

With combined capabilities, the solution suite should provide a complete 360-degree view of an organization’s data landscape, from the data available, its owner/steward, lineage and usage, to its associated definitions, synonyms and business attributes. It should allow business users to easily define, track, and manage all aspects of their data assets, enabling collaboration, knowledge-sharing, and user empowerment through clarity across the enterprise. Bottom line – business users are empowered to quickly consume any data asset where security allows with useful metrics essential to data-driven decision making.

The right solution suite must bridge the business to IT divide and bring people and data together. It should clearly define ownership and accountability for every data asset, so everyone knows the resource when he or she has pressing questions about data.

Data governance is not a project; it’s an ongoing process that has no end date. But for those who maintain and mature their model, it will help to increase efficiencies and profitability across the enterprise and help gain a competitive advantage.

Now the only question is, what’s the next step in data governance maturity?

If you would like to learn more about developing a mature data governance model, download the white paper below.

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