These best practices for data and analytics governance can help data and analytics leaders make the most out of their business opportunities.
Leaders in data and analytics know that without good governance, investments in data or analytics will not meet key organizational requirements such as revenue growth, cost optimization, and improved customer experience.
Data governance best practices and concrete steps to build a solid foundation for data and analysis are what D&A leaders need urgently.
“Data and analytics leaders find it difficult to identify which areas of governance need improvement because they don’t have a clear benchmark to best practice in key governance areas,” states Saul JudahVP Analyst, Gartner.
7 data governance key foundations
1. Align data and analytics governance with business outcomes
Governance should be linked to business strategy and priority. Organizations often focus their D&A governance practices on data, rather than a business. This makes it difficult for D&A leaders and business leaders to have meaningful conversations with them.
Align governance policies and standards with business priorities, business process metrics, and D&A metrics to improve business support.
Your governance charter should place business value and prioritized outcomes in the center of it. Include clear business metrics to ensure success. These metrics should be assigned to named stakeholders and linked with D&A metrics. Next, hold workshops with key decision-makers to discuss strategies for improving their business results.
2. Maintain a model of accountability and decision rights
For any D&A project to be successful, it is crucial that there is a model of accountability and decision rights. This gives stakeholders the ability to trust the government’s decision-making process and ensures that everyone is accountable.
3. Implement trust-based governance
Analytics and data assets are everywhere in an enterprise. They also vary in their nature. Therefore, it is not a good idea to make business decisions based upon the assumption that all information is equal. Instead, create a trust-based governance system that:
- Supports a distributed D&A network
- Recognizes the differences in lineage and curation
- Assists business leaders to make contextually relevant decisions with more confidence
Analyze how technologies, such as a data catalog, can help you find, evaluate and manage data and analytics assets throughout the enterprise ecosystem.
Also read: Data Analytics In Digital Transformation: Driving The Change For Organizations
4. Value digital ethics and transparency
D&A governance must be based on transparency and digital ethics in order to achieve digitalization. Data and analytics governance decisions must be transparent, logical, and well documented. You should be a leader in data and analytics.
Your data and analytics governance charter should align with your business values as well as the principles of your digital ethics. It should identify the relevant authorities and accountability and explain the basis for decisions.
Data and analytics governance operating procedures must provide clear audit trails that highlight the decisions taken, investments made, and expenditures related to digital ethics.
5. Consider risk management and information security
High-performing companies are more risk-aware than risk-averse. This means that they consider the opportunities presented by data and analytics as well as the risks. Organizations often manage business risk and business opportunity separately. Information security is not considered a critical component of evaluating business outcomes.
Multidisciplinary teams should be formed to oversee D&A governance. They will have the ability to make balanced decisions that give weight to security, opportunity, and risk, while also considering the long-term interest of the organization.
For evaluating governance decisions, the metrics should include business value, future risk, and opportunities, as well as gaps in information security. Establish a controlled environment to address D&A risk in real-time and integrate the enterprise information safety framework with it.
6. Deploy governance training and education
D&A governance initiatives demand that people behave differently in order to meet the expectations of policies and standards. It’s not always easy to know what these new behaviors should look like. Work with HR to plan a learning and developmental program that supports data governance best practices. To determine the roles in governance, analyze them, and create training modules that include blogs, webinars, or guidelines. This will provide current and relevant learning material. They can be used to help people make better governance decisions.
You should set clear and quantifiable goals for your data and analytics role. You can make it a part of your annual employee goals to complete training modules on data governance best practices.
7. Encourage cultural change and collaboration
D&A governance decisions can be made throughout the enterprise so it is important to focus on collaboration and not centralization. D&A governance should not be seen as a bureaucratic activity. Instead, it should be based on people-to-people interactions, storytelling, and knowledge sharing.
Begin by taking part in executive meetings, all-hands meetings, and other sessions to get a feel for the current data perception within your organization. You will need to identify the cultural changes that are needed and create a narrative-based narrative to explain how data and analytics governance can address the real problems that lead to digital fatigue.
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