What do you do if your data governance strategy lacks innovation?

Assess who you have in the seat driving the Data Governance bus, and stop using the measuring stick of technical competency and degree of institutional and tribal knowledge to pick the right leader. Success in Data Governance operationalization and doing it in an innovative way requires you to #doDifferent and #thinklikeaCxO. If that person really is the right person for the job, then make sure to support them with thought leadership through fractional CDO & Data Delivery Partners from organizations who focus 100% on data. Tip: You might also want to pick up the great read #GeeksWithEmpathy by Jeremy Adamson. I just finished it and will be doing a formal review next week. Follow me and the #dataninja tag to find out more.

It’s crucial to align your strategy with the overarching business objectives.This involves emphasizing the importance of data quality and integrity, and encouraging stakeholders to leverage data analytics to drive innovation and improve business outcomes.Cross-functional collaboration is essential for innovation in data governance. By facilitating collaboration between different departments and business units, you can promote the sharing of data, insights, and best practices. This interdisciplinary approach can lead to innovative projects that leverage data governance principles to drive business value.Embracing experimentation and iteration is another key aspect. Encourage teams to explore new ideas, technologies, & explore market trends.

Conduct a thorough assessment of your current data governance strategy to identify areas where innovation is lacking. Look for opportunities to introduce new ideas, approaches, and technologies that can drive improvement. Seek insights from stakeholders, team members, and industry experts to understand their perspectives on the effectiveness of your data governance strategy and where innovation is needed. Consider conducting surveys, interviews, or focus groups to gather feedback.

To infuse innovation into your data governance strategy, you should first assess your current and future needs. Evaluate how well existing processes/tools support business objectives and identify gaps. Determine which areas would benefit most from new approaches - data quality, metadata management, compliance, etc. You could survey key stakeholders across business units to understand their pain points and desired improvements. Additionally, you should analyze market trends and emerging technologies that could transform data governance. AI/ML, blockchain, and cloud migration are examples to explore for modernizing your strategy. This needs assessment lays the foundation for prioritizing innovations aligned with your unique requirements.

Verify how the innovation impacts the relationship between teams on issues such as responsibilities, ways of working and data management. Give presentation a new changes to the leader stakeholders to how these changes impact in topics relevant to the organization like security or performance: their feedbacks help you to evaluate if these change are relevant or not to them. They will engage your proposes if they find it relevants and valuable.

Foster collaboration and knowledge sharing across different departments and disciplines within your organization. Engage with colleagues from areas such as data science, IT, marketing, and operations to gain diverse perspectives and insights. Organize brainstorming sessions or workshops with your team to generate new ideas and innovative approaches to data governance. Encourage creativity and open-mindedness, and explore unconventional solutions to existing challenges.

Updating policies can be a strategic step if your data governance strategy lacks innovation. The importance of updating correct policies leads to Adaptation to New Technologies, Enhanced Data Security, Data Quality Assurance, Data Privacy Compliance, Data Lifecycle Management and many more. When updating policies, involve key stakeholders from across the organization, including legal, compliance, IT, security, and business units, to ensure alignment with business objectives and regulatory requirements. Communicate policy changes effectively to all employees and provide training and resources to support implementation and compliance.

Investing in tools can be a strategic move if your data governance strategy lacks innovation. This would impact in Automation and Efficiency, Advanced Analytics, Enhanced Security, Scalability, Integration Capabilities, Compliance and Regulation, and User experience. When evaluating potential tools to enhance your data governance strategy, consider factors such as functionality, scalability, integration capabilities, security features, and vendor support. Choose tools that align with your organization's specific needs, objectives, and budgetary constraints.

Talent can be with modern DG skills such as Informatica and Atlan which has some really cool low code such as Auto classification, and Auto lineage. You can also use Unity catalog for similar results if you are using Databricks.

Investing in data literacy training is crucial to ensure that employees have the necessary skills to understand, interpret, and use data effectively in their roles. Providing training and resources can help improve data literacy across the organization, empowering employees to make informed decisions based on data.Utilizing advanced data governance tools and technologies can streamline processes, enforce data quality standards, and enable self-service access to trusted data. Investing in innovative solutions that facilitate data discovery, lineage, and governance at scale can drive efficiency and innovation within the organization.

Encourage continuous learning and growth among your data governance teams, to seek fresh insights and refine skills. This can be achieved by inviting subject matter experts, attending industry conferences or engaging in technology watch to stay ahead of emerging trends and technologies.

?? Innovation in governance strategy? We need to embroider by reevaluating our processes and tools. Implementing cloud-based solutions allows us to collaborate more fluidly and securely, thus optimizing the quality and access to data. Some tools: Collibra, Alation and Informatica, Talend, Erwin, Dataplex, Lose Prevention, Puerview. Additionally, introduce AI and ML techniques to improve anomaly detection and data-driven decision making. This helps in the evolution and strengthens our capabilities to maintain data integrity, it also drives innovation in all areas of the company.

One of the game changes for DG is to associate the importance of DG to AI projects such as LLMs. This will not only allow you to think OOB, but also creates the much needed spark that DG deserves.