The hottest Analytics Tools Substack posts right now

Google's BigLake allows users to access and manage data across different storage solutions like BigQuery and object storage. This makes it easier to work with big data without needing to move it around.
The Storage API enhances BigQuery by letting external tools like Apache Spark and Trino directly access its stored data, speeding up the data processing and analysis.
BigLake tables offer strong security features and better performance for querying open-source data formats, making it a more robust option for businesses that need efficient data management.

Self-service analytics hasn't delivered as promised. Companies still struggle to find basic answers and often just switch tools instead of addressing the real issues.
Dashboard fatigue is a real problem. Many dashboards go unused because they are complicated and not user-friendly, making executives reluctant to engage with them.
AI is not a cure-all for self-service problems. Data needs careful preparation and clear questions from users to be effective, and many still rely heavily on traditional methods like spreadsheets.

Event-based analytics is crucial for understanding user behavior and product performance.
Session-based analytics focus on website traffic while event-based analytics track user interactions like clicks and actions.
Implementing and maintaining event-based analytics can be challenging due to issues with data integration and interpretation.

The Fermi problem approach helps estimate the impact of new product features by breaking it into smaller questions. This method allows for better understanding and clearer predictions.
Using rough estimates based on educated guesses provides range estimates instead of precise answers, which can help account for uncertainty in projections.
Continuous refinement of estimates with new data allows for adjustments, creating more credible and strategic insights for product management decisions.

Self-serve analytics lets non-analysts access and analyze data without always needing help from an analytics team. This can help speed up decision-making and reduce bottlenecks.
The goal is to create tools and provide education for everyday users so they can do their own analytics easily. Training and tutorials will be essential to help users become comfortable with these tools.
The focus is on keeping users engaged and motivated to use self-serve analytics. Understanding what stops people from doing analytics themselves is key to improving the program.

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