What are the best ways to align your data governance policies with BI tools?

*Assess your data maturity. *Define your data governance roles and responsibilities. *Choose your BI tools based on your data needs and capabilities. *Implement your data governance policies and procedures in your BI tools. *Monitor and evaluate your data governance and BI performance.

In my experience, getting a semantic layer with governed data providing a single source of truth and making BI tools leverage the same for their dashboards will help. Having said that proper assessment should be done on what should come in to the semantic layer by involving tech and business stakeholders. There also has to be a proper alignment between the data governance and the BI teams on terms of reference which will contribute to the success.

Start by assessing your organization's data maturity by evaluating the existing data infrastructure, data quality, management practices, and how data supports business objectives. Identify areas of strength and improvement to determine the governance features most needed in BI tools, such as access controls, data quality monitoring, and compliance tracking. Choose BI tools that can adapt to your data governance framework, offering customization options to enforce policies directly within the tools. Ensure these tools support the scalability and complexity of your data ecosystem.

Apart from defining roles and responsibilities, i would also consider defining bare minimum workflows/processes to follow. In case of any change management, such as an employee being promoted or resigning, there is a change in the database entry. It is important to determine from whom communication should start, how the change should happen, and how it should be reflected in the dashboards. If possible, create automations/monitoring alerts for this kind of change management happening in one system to another. Once the change is done, it is important to ensure data integrity and consistency across all visualizations. Although the nature of work may be complex, having a bare minimum workflow initially defined can help simplify the process.

Establish a governance structure that includes stakeholders from various departments such as IT, business units, and legal. Define specific roles like Data Stewards, Data Owners, and Data Custodians, ensuring each has a clear understanding of their responsibilities regarding data quality, security, and compliance. These roles should also be involved in the selection and implementation of BI tools to ensure these solutions meet governance requirements. By embedding data governance roles within the BI process, you ensure that data handling, reporting, and analytics are consistent with organizational policies and standards, facilitating better data accuracy, security, and regulatory compliance.

Start by conducting a thorough analysis of your current data landscape and future requirements. Consider the types of data you manage, the volume, and the complexity, as well as your team's technical expertise. Look for BI tools that not only offer the analytical capabilities you need but also integrate well with your data governance framework, such as compliance with data privacy laws, data quality management, and user access controls. Engaging with stakeholders across the organization to understand their data needs and challenges can also guide your selection, ensuring the chosen BI tools enhance decision-making while adhering to governance standards.

IMO, I would consider these 3 points mainly that will aid in defining data governance within in the BI tools: * Security and compliance certifications like GDPR, SOC2, HIPAA out of the box. * Row level security with enough features to support users, groups / roles and their permission management. * Easy integration support with the existing user management system / SSO support. Ex: Your organization may use Azure AD then tool should support the same for easy integration. Other features I would consider important are * Self-service analytics solutions * ML and AI capabilities at least in tool roadmap. * Extensible / customizable visualizations where we integrate our own visualization with relative ease. * Embedded support

Develop a solid relationship with both your chief privacy officer and your chief security officer. Document restrictions. Implement in layers of security on a need to know basis. If there is sensitive data that is superfluous to the BI team do not import it into the database. Otherwise, obfuscate it using a one way hash. Typically, end-users only need aggregated metrics for operational analysis, KPI monitoring and predictive metrics. Load aggregated metrics to the visualization tool. Access to the data at each layer should be based upon job function as documented in a privacy and security impact analysis document.

Very simple: User Experience! 1. Do actual users have access to, and know how to user BI tools? 2. Do BI tools get complete business meaning of tables (entities), attributes and it's associations with other entities? 3. All the policies and "User Entitlements" should be "minimum required access model". This should be integrated with data governance tool. 4. Whether data governance and BI tool are separate, data governance should be enforced smoothly across all the tools, not just BI tools!

Siempre se debe iniciar respondiendo qué y por qué. Detrás de cada por qué, siempre hay un cómo. Estar claro del actual nivel de gestión de los datos (diagnóstico) en una empresa es el inicio para definir e implementar una estrategia adecuada de Gobierno y gestión de datos.