Looker BI and Data Governance: the bedrock of a scalable BI Platform

As your organization grows, so does the complexity and volume of your data. This necessitates a robust BI platform that can handle the increasing demands while maintaining data integrity and trust. Looker, with its powerful capabilities, provides a solid foundation for scaling up your BI efforts. However, without effective BI and Data Governance, even the most sophisticated platform can become unwieldy and unreliable. In this article, we will explore how to implement BI and Data Governance best practices in Looker, ensuring that your BI platform remains scalable, secure, and trustworthy.

Establishing a solid foundation

BI and Data Governance involves defining and enforcing policies, processes, and standards that ensure data accuracy, consistency, and security. In Looker, this starts with:

• Clear roles and responsibilities: define who owns which data asset, who maintains it, and who can access it. Looker's robust permission system allows you to implement granular access controls, and apply row-level security, ensuring that data is only accessible to authorized users.
• Data lineage and documentation: maintain a clear record of where data originates, how it is modeled and transformed, and where it is used. Looker's data modeling layer makes it easy to track a centralized data lineage, providing visibility across your BI model.
• Data quality management: establish processes for data validation, cleansing, and enrichment to ensure that your data is accurate and reliable. Leverage Looker’s data tests and dashboard alerts to flag inconsistencies and user-defined issues.

Scaling up with confidence

As your BI platform grows, it's important to maintain control and ensure that your data remains governed. Looker provides several tools to help you achieve this:

• Version control and change management: keep track of changes to your Looker models and dashboards, ensuring that everyone is working with the latest version. Looker's Git integration allows for seamless version control and collaboration.
• Data catalog and discovery: create a central repository for your data assets, making it easy for users to find and understand the data they need. Looker's data dictionary provides detailed descriptions of fields and tables, facilitating data discovery.
• Automated testing and monitoring: implement automated data tests to validate data accuracy and monitor performance. Looker's data testing framework allows you to create and run tests on a regular basis, ensuring that your data remains consistent.

Benefits of effective BI and Data Governance

Implementing BI and Data Governance in Looker offers several advantages:

• Improved data quality: ensures that your data is accurate, reliable, and trustworthy, leading to better decision-making.
• Enhanced security: protects your sensitive data from unauthorized access and misuse.
• Increased efficiency: streamlines data management processes, saving time and resources.
• Regulatory compliance: helps you adhere to data privacy and security regulations.

Conclusion

BI and Data Governance is not just a buzzword; it’s a critical aspect of any successful BI implementation. By implementing best practices in Looker, an approach we at Datwave apply daily across complex enterprise environments, you can ensure that your BI platform remains scalable, secure, and trustworthy, even as your organization grows. 
Effective Business Intelligence and Data Governance are continuous disciplines that require ongoing oversight, refinement, and alignment with organizational evolution. We have seen this firsthand through our work supporting clients as their data platforms, structures, and needs mature.

With Looker’s powerful capabilities and a strong framework of governance practices—enhanced by Datwave’s proven expertise in designing, optimizing, and operationalizing efficient Looker BI environments—you can establish a scalable, well-governed analytics platform that delivers meaningful, sustained business value.

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Author

Beatrice Riccardi | BI Expert @Datwave

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