Dashboards and automated reporting you can trust

Building a single source of truth for reporting

Most reporting problems start before the dashboard. Sales numbers live in the CRM, invoices in accounting, orders in Shopify, ad spend in three ad platforms and operational data in spreadsheets. Each team exports what it needs and calculates slightly differently, so meetings turn into arguments about whose number is right. A single source of truth fixes this by pulling every system into one central store, such as BigQuery, Postgres or a well-structured Google Sheet for smaller volumes.

Just as important is agreeing what each metric means. Is revenue booked when the deal closes, when the invoice goes out or when the payment lands? Does a refunded order count as a sale? We write these definitions down with you and build them into the pipeline once, so every dashboard and report uses the same logic. When a definition changes, it changes everywhere at the same time.

Power BI vs Looker Studio for small business dashboards

Power BI suits companies already on Microsoft 365. It handles larger data models, offers strong calculations and row-level security, and shares well through Teams, though viewers generally need a paid license. Looker Studio is free, runs in the browser and connects naturally to Google Analytics, Google Ads, BigQuery and Google Sheets, which makes it a good fit for Google Workspace teams and marketing-heavy reporting. Its limits show with very large or complex data models.

We recommend one based on your existing tools, the number of people who need access and how complex the data is. Both can sit on the same pipeline, so the choice is less permanent than it looks. For client-facing reporting, such as monthly results shared with your own customers, we sometimes build a simple custom web view instead, so you can brand it and control access without per-viewer license costs.

Automated data pipelines, scheduled reports and alerts

The pipeline is the part people do not see, and the part that decides whether the dashboard is trusted. We build connectors with n8n, Python or managed tools that pull data on a schedule, handle API limits and pagination, and load it into the central store. Every run checks row counts against the source, looks for duplicates and missing days, and stops with an alert rather than quietly showing incomplete numbers.

On top of the pipeline, reports go out on their own. A weekly PDF can reach managers every Monday, a month-end pack can go to your accountant, and a daily summary can post to a Slack channel. Alerts watch the numbers that matter most, such as cash below a floor or refunds above normal, and message the right person the same day. Nobody has to remember to run the report, so it arrives every time.