Starting data governance initiatives can seem a bit daunting. You’re establishing strategies and policies for data assets. And, you’re committing the organization to treat data as a corporate asset, on par with its buildings, its supply chain, its employees or its intellectual property.
However, as Jill Dyché and Evan Levy have noted, data governance is a combination of strategy and execution. It’s an approach that requires one to be both holistic and pragmatic:
• Holistic. All aspects of data usage and maintenance are taken into account in establishing the vision.
• Pragmatic. Political challenges and cross-departmental struggles are part of the equation. So, the tactical deployment must be delivered in phases to provide quick “wins” and avert organizational fatigue from a larger, more monolithic exercise.
To accomplish this, data governance must touch all internal and external IT systems and establish decision-making mechanisms that transcend organizational silos. And, it must provide accountability for data quality at the enterprise level. The SAS Data Governance framework illustrates a comprehensive framework for data governance that includes all the components needed to achieve a holistic, pragmatic data governance approach.
The framework presented here is a way to avoid data dysfunction via a coordinated and well-planned governance initiative. These initiatives require two elements related to the creation and management of data:
• The business inputs to data strategy decisions via a policy development process.
• The technology levers needed to monitor production data based on the policies.
Collectively, data governance artifacts (policies, guiding principles and operating procedures) give notice to all stakeholders and let them know, “We value our data as an asset in this organization, and this is how we manage it.”