Data Pipelines and Reporting

Most businesses have their data scattered across 5-10 tools: Stripe for revenue, Mixpanel for product analytics, HubSpot for CRM activity, Intercom for support volume, Google Analytics for marketing. Getting a coherent view of the business requires either an expensive BI tool or manual copying — unless you build a data pipeline. A data pipeline is an automation that regularly fetches data from multiple sources, transforms it into a consistent format, and loads it into a central location. The central location can be a Google Sheet (simple, accessible), Airtable (better structure), Postgres on Supabase (scalable, queryable), or a proper data warehouse like BigQuery (enterprise). For most early-stage startups, a Google Sheet or Airtable-based pipeline is sufficient. Here's a concrete example: every day at 7am, fetch yesterday's revenue from Stripe, new signups from your database, support tickets opened from Intercom, and ad spend from Google Ads. Write each metric to a 'Daily Metrics' sheet. Build a simple Google Sheet dashboard on top of this data. The key transformation step is normalization — making sure all your data is in the same timezone, the same date format, and the same currency. This sounds trivial but is genuinely where most data pipelines fail. Stripe reports in UTC, your analytics tool might report in your local timezone, and your CRM might store dates in a completely different format. Normalize everything to one standard in your pipeline.

A data pipeline that stores data is table stakes. The leverage comes from automated reporting — summaries distributed to the right people at the right time, without anyone having to pull them. Weekly business review report: every Monday at 8am, your pipeline aggregates the week's key metrics, compares them to the prior week (calculate the delta, flag significant changes), and posts a structured report to a Slack channel. The report includes: revenue (MRR, new, churned, net new), product (DAU/WAU, feature adoption), and support (ticket volume, average response time, CSAT). Using Claude to write the narrative: instead of just posting numbers, pass the metrics to Claude with a prompt: 'Here are this week's business metrics and last week's for comparison. Write a 3-bullet executive summary highlighting the most notable changes and flagging anything that needs attention.' This turns a table of numbers into a readable narrative that the team actually reads. Alert-based reporting is equally valuable: don't just report on schedule — alert on thresholds. When MRR churn exceeds 2% in a week, alert immediately. When support ticket volume spikes 50% above the 7-day average, alert immediately. When any key metric goes to zero (which often means a tracking bug), alert immediately. These alerts catch problems hours earlier than they'd otherwise be discovered.