Data governance for 2020 and beyond
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.
Special feature
Turning Big Data into Business Insights
Businesses are good at collecting data, and the Internet of Things is taking it to the next level. But, the most advanced organizations are using it to power digital transformation.
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 build a culture of participation, enablement, and sustainability for data compliance and value. Culture addresses the alignment between experience, expertise, and ability to support data governance efforts independent of and across data ops, model ops, development ops, and business ops. Leadership, communication, training, and reinforcement of good data behaviors mature as they become embedded and ambient in the enterprise culture.
Action
Avoid checkbox assessments of technologies deployed and processes executed to determine maturity and effectiveness. Action is about practices, services, and enablement that match data governance objectives and business culture. Technology deployments to enable and automate policies and procedures must address near-term, long-term, and innovation horizons. Procedures must conform to culture and become embedded into everyday best practices to eventually become ambient in business processes.
This post was written by VP, Principal Analyst Michele Goetz, and it originally appeared here.
READ MORE HERE