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Analytics & Metrics

Product analytics, KPIs, dashboards, data-driven

233 bites

Analytics & Metrics30 sec read

Ad Hoc Reporting: Answering One-Off Business Questions

Ad hoc reporting is your data "quick dive" for one-off questions, unlike static dashboards. A sales team might use it to see how a holiday affected regional sales.

Analytics & Metrics30 sec read

Data Dictionary: The 'About' Page for Your Data

A data dictionary is the instruction manual for your database, defining what each piece of data means and how it's formatted. It's used by engineers to understand a schema or by analytics tools to interpret columns. The biggest footgun is letting it go stale.

Analytics & Metrics30 sec read

Reverse ETL: From Warehouse Insights to Operational Action

Reverse ETL pushes clean data from your central warehouse back into the operational tools business teams use daily. This powers sales with customer scores in their CRM or marketing with personalized segments, all from a single source of truth.

Analytics & Metrics31 sec read

Snowflake Schema: Trading Query Speed for Storage

A snowflake schema saves storage by normalizing a star schema's dimensions into smaller, related tables. It's used in data warehouses to reduce redundancy, but the extra joins required can slow down queries, making it a trade-off against a simpler star schema.

Analytics & Metrics30 sec read

Star Schema: The Blueprint for Analytics Data

A star schema organizes analytics data with a central fact table (e.g., sales) surrounded by dimension tables (e.g., customers). It's built for fast queries in data warehouses. The footgun is normalizing dimensions, which negates its speed advantage.

Analytics & Metrics30 sec read

Data Marts: Your Department's Slice of the Data Warehouse

Think of a data mart as a department's personal slice of the main data warehouse, containing only relevant data. This allows teams like Sales or Marketing to run faster, focused queries. The footgun is letting each team define shared terms differently.

Analytics & Metrics30 sec read

Data Warehouse: The Single Source of Truth for Analytics

A data warehouse is a central database optimized for analytics, not transactions. It integrates historical data from disparate sources like sales and marketing to create a single source of truth for business intelligence.

Analytics & Metrics30 sec read

Customer Data Platform (CDP): Your Customer's Single Source of Truth

A Customer Data Platform (CDP) creates a single, persistent profile for each customer by unifying data from siloed sources. It's used for real-time personalization and AI-driven marketing. The footgun is confusing it with a CRM, which manages relationships.

Analytics & Metrics31 sec read

Client-Side vs. Server-Side Tracking

The difference is who sends your data: the user's browser (client-side) or your server (server-side). Client-side is simple but fragile against ad blockers. Server-side offers more control but adds cost. The footgun is thinking server-side is a magic fix.

Analytics & Metrics32 sec read

User ID: The Key to Cross-Device Analytics

A User ID stitches together a person's journey across devices and sessions, moving beyond anonymous tracking. It's key for apps with logins to see the full customer lifecycle. The footgun: never use the User ID for a custom dimension; it will break reporting.

Analytics & Metrics30 sec read

The Data Layer: Your Web Page's Central Message Bus

The data layer is a central message bus for your website, decoupling your app from analytics tags. It passes dynamic data like purchase totals or button clicks to tools like Google Tag Manager, which listens for these events to fire tags.

Analytics & Metrics30 sec read

Event Tracking: Measuring What Users Do

Event tracking turns user actions like clicks and purchases into analyzable data. Analytics platforms use this data to report on engagement and conversions. The biggest footgun is inconsistent naming, which pollutes your data and breaks reports.

Analytics & Metrics30 sec read

Analytics Measurement Plan: From Why to What

An analytics measurement plan forces you to define success before you look at data. It connects high-level business objectives to specific user actions and sets clear targets.