Companies have short attention spans when it comes to data governance. Even for organizations with sustained programs, the continuous push and pull of new regulations, projects, or data and analytics investments create constant disruption. To address these expansions, data owners either search for the simple approach or reeducate on data governance 101.
Here is the truth: There is nothing simple or basic about data governance. Effective data governance grows out of data management maturity. It is why, to make progress, organizations are hiring chief data officers and activating strategic and unified data, analytics, and data governance competency centers. Data governance policies and procedures designed to herd your organization's "data cats" require experience and expertise.
To be effective, you need a data governance framework and a plan tightly aligned to the purpose, culture, and actions that live within your business practices, rather than outside them.
Purpose
Throw out the notion that low-level data governance begins with IT. Data governance is neither an IT project nor a box on the enterprise data strategy and architecture model. The right place to assess the need for a data governance initiative and program is linked to the business needle that you need to move. Even for objectives like regulatory compliance, obtaining "better" data via a data governance program should translate to revenue- and growth-generating outcomes. For example, identity management and preference management need to align with privacy regulations, but they also improve customer understanding and yield better results from loyalty programs and targeted sales initiatives. Thus, business risk is addressed while also serving business upside.
Culture
Culture is not an organizational model or assignment of data ownership. Centralized or federated models are best determined by the centralized and federated nature of the enterprise model. The purpose of data governance is to catalyze interested parties to