Essential Metrics for Looker Product Owners: Maximizing Efficiency and User Experience

As a Looker product owner, it is crucial to track key metrics to ensure the optimal performance, efficiency, and user experience of your Looker instance. By monitoring these metrics, you can identify areas that require attention, make data-driven decisions, and continuously improve the usage and impact of Looker within your organization. In this article, we will explore the essential metrics that every Looker product owner should track based on the previously mentioned categories.

Usage and Adoption Metrics:

• Unused Looks, Dashboards, Explores, Views, Models, Fields, Filters: Tracking these metrics allows you to identify unused or outdated components within Looker. By regularly cleaning up unused resources, you can improve organization, reduce clutter, and ensure that users have access to relevant and up-to-date information.

Data Model Optimization:

• Unused Views, Fields: Monitoring these metrics helps you identify views and fields not utilized in Looker's data modeling. By removing unused elements, you can streamline your data model, improve query performance, and enhance overall system efficiency.

User Management:

• Unused User Attributes: Tracking unused user attributes allows you to identify custom user attributes that are not being utilized in user permissions or access control. By cleaning up unused attributes, you can maintain an efficient user management system and ensure that access control remains streamlined.

Dashboard and Visualization Optimization:

• Dashboard & Looks Auto Refreshes: Monitoring the auto-refresh settings for dashboards and looks enables you to ensure that data is updated at appropriate intervals. By aligning the refresh frequency with user needs, you can provide real-time insights and enhance user experience.
• Dashboards with more than 20 Tiles: Tracking dashboards with excessive tiles helps you identify crowded or complex dashboards. By optimizing the dashboard design and reducing the number of tiles, you can improve readability, performance, and overall user satisfaction.

Error Monitoring and Performance Optimization:

• "errors_and_broken_content" (Performance Optimization): Tracking errors and broken content allows you to identify and address any issues or inconsistencies within Looker. By monitoring error logs and addressing broken content, you can ensure accurate data visualizations and optimize system performance.
• Performance Recommendations: Monitoring performance recommendations help you optimize Looker's performance. By implementing recommended optimizations, such as query tuning or dashboard optimization, you can enhance query execution, reduce loading times, and provide a seamless user experience.

Scheduled Jobs and Alerts:

• Scheduled Jobs, E-mails: Tracking scheduled jobs and e-mails ensures the smooth execution of automated data deliveries and notifications. By monitoring the scheduling and delivery of important reports, alerts, and data updates, you can ensure timely access to critical information.
• Alerts: Tracking alerts allows you to stay informed about significant changes or events in your data. By setting up and monitoring alerts, you can proactively address anomalies, monitor key performance indicators (KPIs), and receive timely notifications about important data conditions.

Conclusion:

As a Looker product owner, tracking the essential metrics outlined above is vital for maximizing your Looker instance's efficiency and user experience. By regularly monitoring these metrics, you can optimize resource utilization, improve data model performance, enhance dashboard design, address errors, and proactively address important events. This data-driven approach will enable you to make informed decisions, drive continuous improvement, and ensure that Looker remains a valuable and impactful tool within your organization.

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