Using Data Governance to Drive a Competitive Advantage
Recent research estimates that 65% of today’s leading organizations have formalized data operations in place focused on driving business and financial outcomes through better use of corporate data. Many of these organizations are using data governance (DG) as a fundamental pillar to support that goal while others have DG programs that have lost direction, been put on the shelf, or simply haven’t been introduced as a formalized capability to support the vision. Wherever an organization may fall on their data governance journey, there are a few fundamentals concepts that should always be the cornerstone of a successful DG program.
Adopt a Fit-for-Purpose Data Governance Definition
A quick online search for ‘data governance’ will turn up multiple definitions of the capability – some of which are accurate, and some flat out incorrect. No matter how an organization defines data governance, the effort must be aligned to corporate needs with a consensus amongst stakeholders across the data value chain (e.g., data creation, use, disposition). Further, the chosen definition should align with some form of industry standard (DMBOK or CMMI, Gartner, etc.). A clear, concise, and agreed upon DG definition that resonates with key stakeholders is a simple, yet often overlooked, first step. Too frequently, we see organizations that have a program in place but don’t have the definition formalized, have multiple definitions, or in some cases, never really took the time to define it at all. A data governance definition that we typically like to start with is: a repeatable process that enables the delivery of standardized, high-quality information and insights to end users in a timely, compliant, auditable, and secure manner.
Develop the Case for Change
A stated certainty behind any successful data governance program is the importance of a clear and resolute business case (defining our ‘why’ and articulating the justification for change from the status quo). Many DG professionals would argue that this is the single most important component of any data governance program; for without it, the program will never take wings and will continuously be at risk of becoming an ambiguous and misunderstood capability. Data governance, at its core, allows organizations to treat and manage their data as a strategic asset to drive a competitive advantage. In short, it reduces risk across the organization and prevents data from being the excuse for ineffective decision making (e.g., if we only had the right data when we needed it, we could have completed X more in sales/revenue). Organizations with fruitful data governance programs understand that the business case is an essential ingredient to success that requires continuous reviews, updates, and modifications over time.
Treat Data as a Corporate Asset through Formalized Data Domains
Organizations that are highly successful at driving value through their data governance programs apply the same rigor and discipline to their data assets as they do with other corporate assets, such as people, facilities, and products. The ability for organizations to better organize around their corporate data starts with data domain identification and formalization. Data domains are simply ‘subject areas of data’ that are most vital to the organization’s overall success (e.g., customer, product, supplier, people, finance, etc.). Not all data carries the same value, and there are certain data domains that are inherently more important than others – take time to identify and prioritize them. No two organizations have the exact same data domain structure, and data domains should be reviewed periodically to ensure investments and priorities are aligned to corporate vision (e.g., a new product offering).
A critical aspect of data domains is that the data within a given domain crosses functional and business applications (e.g., customer data is shared throughout various applications and business units). If an organization is still referring to their data by ‘application’ or ‘system’ (e.g., Snowflake, Salesforce, SAP, etc.), then opportunity exists to better manage data as a corporate asset (e.g., “Supplier Name” exists across multiple applications throughout an organization). Once data domains have been identified, an organization can start to build tactical plans around the Critical Data Elements (CDEs) that reside within each domain.
Drive Visibility via a Data Governance Operating Model
The data governance operating model brings visibility and formalization to primary roles and responsibilities that are required for an organization to fulfill their stated definition of DG. The operating model highlights the mechanisms by which executive (strategic) decision making for data flows across data domains as well as the systems and applications that support those domains (tactical). It provides clarity around who has the authority to make data changes and who has the accountability/capability to implement the changes. The data governance operating model also describes the role of an enterprise data governance program manager and how they will act as a change agent to support the goals and objectives of the data governance program.
Build & Maintain the Project Portfolio
Underpinning data governance programs with strong portfolio and project management capabilities is another leading practice within today’s successful data governance initiatives. This includes prioritizing efforts with a focus on early wins, measuring success, and communicating value. Most recently, organizations are folding agile ops into the DG equation, providing projects with the necessary strategic alignment, accountability, visibility, and quality to enhance the effectiveness of DG investments.
A Methodical Approach to Technology Enablement
Data governance programs that are driving the most value for their organizations have established clear strategic intent and have a formalized DG operating model in place with a clear understanding of their data domain structure prior to moving towards technology enablement. If these steps have been taken, only then should an organization start to roll out technology adoption. Where we see most of our clients maximizing the power of technology is through the use of data cataloging tools, which provide users the ability to ‘shop’ for pertinent datasets to support business outcomes. This could be in the way of a reports catalog or access to the raw data itself. Recently, many platform providers have rolled out additional features to include AI/ML cataloging and sophisticated data lineage and data provenance capabilities that support better access to trusted information.
No matter where an organization may be on their data governance journey, it’s important to stay grounded in the above concepts and make sure DG activities are business-led and technology-enabled through descriptive and intentional project plans that are tied to business outcomes and value generation at every turn. Eliminating ambiguity through clear definitions, emphasizing the importance of strong communication, and frequently measuring results will help organizations position themselves for success with the ability to make better use of their data.
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