Foundation · build guide
Data Access & Dashboards
Google Ads, Meta, GA4, and your CRM in live dashboards your channel owners actually use
What it does
Data Access & Dashboards connects Google Ads, Meta, GA4, and your CRM into live, self-serve dashboards your channel owners can actually use. You agree on one metric dictionary, land each platform's data in a shared layer on a schedule, and put a dashboard on top — so weekly performance questions get answered by opening a page, not by filing a request with an analyst.
It compounds because everything else sits on it. Budget shifts, bid changes, creative tests — every automation you add later reads from this same governed layer. That is why the Readiness Score weights data access most heavily: fix it once and every automation above it stops creating a bottleneck; skip it and each new workflow inherits the same stale, disconnected numbers.
You run this with scheduled exports and a spreadsheet — no APIs, no engineering. Each platform delivers a report on a schedule, you land it in Google Sheets in a consistent shape, and Looker Studio reads the Sheets. It suits a team where one person is willing to own a short weekly hygiene pass in exchange for everyone else getting self-serve answers.
You connect the platforms over read-scoped APIs or MCP servers and let an agent run the refresh loop: pull, reconcile, update the data layer, flag anomalies. You approve definition changes and anomaly callouts; the agent never touches the source platforms. It suits a team ready to retire manual exports and maintain one small governed pipeline instead.
Before you start
Prerequisites.
Path A
Build it with manual data extracts.
You run this with scheduled exports and a spreadsheet — no APIs, no engineering. Each platform delivers a report on a schedule, you land it in Google Sheets in a consistent shape, and Looker Studio reads the Sheets. It suits a team where one person is willing to own a short weekly hygiene pass in exchange for everyone else getting self-serve answers.
Write the metric dictionary first
Before touching any export, agree what spend, conversions, and revenue mean — and which source wins when platforms disagree. Write it on one page with a named owner. Every dashboard number should trace back to this sheet, and it is what keeps this week's numbers comparable to last week's.
Schedule exports from each platform
Set up Google Ads scheduled reports, Meta Ads Manager exports, GA4 report exports, and a saved CRM report. Put them all on the same date grain — daily rows — and the same delivery day, so every source covers the same complete week when you sit down to update.
Land everything in one Google Sheets file
Create one tab per source with identical columns: date, account, campaign, spend, clicks, conversions, revenue. Paste or import each export into its tab and stop there — source tabs are never edited by hand beyond the import. Add a changes tab where you log anything you had to fix.
Blend and publish in Looker Studio
Connect Looker Studio to the Sheet and build one page per channel plus a cross-channel summary, using the dictionary's definitions for every calculated field. Share view access with each channel owner so the answer to a performance question is a link, not a request.
Run the weekly hygiene pass
Check that every tab has rows through the latest full week, that campaign naming hasn't drifted, and that CRM revenue roughly reconciles with platform-reported conversions. Note anything you corrected in the changes tab so the next person can see what moved and why.
Cadence: Weekly. Expect roughly an hour per cycle once the exports and templates exist; the initial build takes two to three weeks.
Path B
Integrate it with MCP connections.
You connect the platforms over read-scoped APIs or MCP servers and let an agent run the refresh loop: pull, reconcile, update the data layer, flag anomalies. You approve definition changes and anomaly callouts; the agent never touches the source platforms. It suits a team ready to retire manual exports and maintain one small governed pipeline instead.
Provision read-only access per platform
Create read-scoped credentials for Google Ads, Meta, GA4, and your CRM. No edit scopes anywhere — this workflow only reads. Keep a list of exactly what was granted per platform so access can be reviewed and revoked cleanly.
Stand up the data layer the agent owns
Create BigQuery datasets — or a structured Google Sheets file at small scale — with one table per source plus a run log. This is the only place the agent has write access. Dashboards read from here, and the source platforms stay untouched.
Wire the scheduled pull loop
Use Claude with an MCP server per platform, or n8n, Make, or Zapier for the scheduled fetch. Each run pulls the latest daily rows, appends them to the source tables, and writes a run-log entry: what was pulled, row counts, timestamps. A failed pull should be visible in the log, not silent.
Add reconciliation and anomaly checks
Have the agent compare platform-reported conversions against CRM outcomes on every run, and flag gaps or day-over-day swings beyond thresholds you set. Flags land in a review queue for a human — not on the team dashboard — so a tracking break gets caught before anyone plans against bad numbers.
Publish dashboards and a gated weekly digest
Point Looker Studio at the data layer and have the agent draft a weekly summary including any anomalies it found. A human reviews and confirms flags before they go to channel owners, and any metric definition change goes through the dictionary's named approver first.
Tools & guardrails
Run it safely.
- Read-only everywhere: no credential in this build carries edit scope on an ad platform or the CRM
- The agent writes only to its own data layer, and every run appends to an audit log with row counts and timestamps
- Metric definition changes require the named approver and a logged before/after, so numbers stay comparable week to week
- Anomaly flags queue for human confirmation before they reach channel owners — nothing auto-publishes
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We stand this automation up as a governed sprint on the stack you already run — scoped, built, and handed over with the guardrails configured. No new platform, no migration.