Audit and build a BigQuery reporting foundation your business can trust
Map source systems, KPI risks, reporting workflows, and the smallest practical build sequence before committing to a larger platform.
Illustrative audit output
See the decisions an audit should leave behind
Synthetic planning example only. These are sample inputs and decisions, not a client result or a promised project scope.
Step 1: Discovery
Confirm the reporting problem, likely sources, decision makers, and whether a bounded audit is useful. No architecture or build is agreed yet.
Step 2: Audit
Inspect sample exports and report logic with the source and KPI owners. Deliver an inventory, ranked risks, GCP options, phase-one boundary, and acceptance plan.
Step 3: Implementation
Only after scope and access are agreed, build the selected batch loads, BigQuery models, checks, and reporting output.
Sample source and report inventory
- Billing export: invoice lines, credit notes, status, posting date
Report use: Monthly net revenue in the finance pack
Proposed owner: Billing / finance source owner
- CRM export: account ID and sales stage
Report use: Customer grouping alongside revenue; CRM is not the revenue ledger
Proposed owner: Sales operations source owner
- Monthly spreadsheet pack: joins and manual adjustments
Report use: Existing leadership output to compare against
Proposed owner: Finance report owner
Prioritized reporting risks
- Priority 1: Credit notes or posting cutoffs change net revenue across periods.
Decision and owner: Finance approves the period rule and a source control total before modeling.
- Priority 2: CRM and billing account IDs do not always match.
Decision and owner: Sales operations owns an exception list; unmatched accounts remain visible.
- Priority 3: Spreadsheet adjustments have no traceable source.
Decision and owner: Report owner identifies approved adjustments and their evidence.
Native Google Cloud choices to decide
Cloud Storage landing
For recurring file exports, retain dated source files in Cloud Storage before BigQuery batch loads so a failed load can be inspected or replayed. A stable direct source-to-BigQuery load may be simpler if that source already supports it; avoid a storage hop without a reason.
BigQuery models
Keep source-shaped tables separate from curated revenue tables. Choose a posting-date partition only after finance settles the period rule; add clustering only if actual query patterns justify it. Batch refresh is the starting option, not real-time streaming.
Reporting output
Publish one governed BigQuery table for the monthly pack. Connect the existing reporting tool if it can use that table and meet access needs; consider a new dashboard only if the current workflow cannot.
Sample phase-one boundary
In scope: One monthly net-revenue pack, the billing and CRM exports needed for it, an agreed sample period, one curated BigQuery output, checks, and handoff notes.
Excluded: No full CRM analytics rebuild, real-time ingestion, every historical report, or reporting-tool migration in this sample phase.
Owners and acceptance checks
- Audit exit: finance, sales operations, and the report owner confirm the inventory, open risks, KPI rule, proposed GCP design, exclusions, and named build owners.
- Build exit: reconcile billing control totals to the curated output for the agreed sample period within a finance-approved tolerance; review unmatched accounts and approved adjustments.
- Handoff: verify the agreed batch refresh and report output, record failed-load and exception handling, and name the team that will operate each step.
Share your source systems, one recurring report, and its biggest mismatch to discuss a bounded audit. Do not send credentials or confidential records.
Specialist BigQuery delivery
Audit, implementation, reporting automation, and maintenance for growing teams on Google Cloud.
Start with the reporting bottleneck
Email info@agiledwh.com or use the project form after the page loads.