Data and analytics leaders know that without good governance, their investments in data and analytics will fail to meet key organizational demands such as revenue growth, cost optimization and better customer experience.
What D&A leaders urgently need are data governance best practices and practical steps to create an effective foundation for data and analytics.
Download Roadmap: Data and Analytics Governance
“Data and analytics leaders are finding it difficult to identify which aspects of governance need to improve, because they don’t have a clear benchmark for best practices in key governance areas,” says Saul Judah, VP Analyst, Gartner.
7 data governance key foundations
No. 1: Align data and analytics governance with business outcomes
Governance efforts should be directly connected to business strategy and priorities. However, organizations often orient their D&A governance practices around data rather than business, making it challenging for D&A leaders to have meaningful discussions with business leaders.
To better support business outcomes, align governance policies and standards with business priorities, business processes metrics and D&A metrics.
Put business value and prioritized outcomes at the center of your governance charter, with clear business metrics for success. Make sure these metrics are attributed to named stakeholders and connected with D&A metrics. Finally, organize workshops with key decision makers and contemplate strategies to improve their business results.
No. 2: Maintain a model of accountability and decision rights
A model of accountability and decision rights is critical for any successful D&A effort. This provides the oversight needed to ensure that the right people are accountable for the decisions they make, and that stakeholders have confidence in the governance decision-making process.
No. 3: Implement trust-based governance
Data and analytics assets exist everywhere across an enterprise and vary in nature, so making business decisions based on the assumption that “all information is equal” is no longer a good approach. Instead, establish a trust-based governance model that:
Supports a distributed D&A ecosystem
Acknowledges the different lineage and curation of assets
Assists business leaders in making contextually relevant decisions with greater confidence
Evaluate how technologies such as a data catalog can help you discover, evaluate and govern data and analytics assets across the enterprise ecosystem.
No. 4: Value digital ethics and transparency
For successful digitalization, D&A governance must operate based on the principles of transparency and digital ethics. Data and analytics governance decisions should be clear, defensible and documented. As a data and analytics leader, you should establish a framework of digital ethics that can be implemented across the enterprise.
Ensure that your data and analytics governance charter aligns with your organization’s business values, as well as digital ethics principles. Make sure it designates relevant authorities and accountabilities and explains the basis on which decisions are being made.
The data and analytics governance operating procedures should demonstrate a clear audit trail highlighting the decisions made, actions taken, related investments and expenditures, and compliance to digital ethics.
No. 5: Consider risk management and information security
Top-performing organizations are risk-aware, rather than being risk-averse. This means they address opportunities created by data and analytics alongside the risks. Often, organizations govern business opportunity and risk separately, and they also don’t consider information security as a key component when evaluating business outcomes.
D&A governance bodies should have multidisciplinary teams that can take balanced decisions, giving the necessary weight to opportunity, risk and security, and keeping the long-term interests of organizations in mind.
The metrics for evaluating governance decisions should indicate business value, future risks and opportunities, and the gaps in information security. To address D&A risks in real time, establish a control environment and integrate the enterprise information security framework with it.
No. 6: Deploy governance training and education
D&A governance initiatives require people to behave differently, by following expectations set by policies and standards. But it’s not always clear exactly what these new behaviors should be. Collaborate with HR and plan a learning and development regime to support data governance best practices. Analyze the governance-related roles to get an idea of the required skill set and develop training modules consisting of webinars, blogs or guidelines to provide relevant and updated learning material. Assess their impact in helping people make better governance decisions and make the necessary enhancements.
Set well-defined and measurable goals for the data and analytics role. For example, completing specific training modules on data governance best practices can become a part of annual employee objectives.
No. 7: Encourage cultural change and collaboration
Because D&A governance decisions are made across the enterprise, focus on collaboration rather than centralization. D&A governance cannot be seen as a bureaucratic activity; rather, it should focus on people-to-people interactions, storytelling, knowledge sharing and innovation.
Start by getting a sense for how data is currently perceived in the organization by attending executive meetings, all-hands meetings and other sessions. Figure out what needs to change culturally and develop a story-based narrative for explaining how data and analytics governance can address real challenges that lead to digital fatigue.