I check the layer behind the dashboard.
Most reporting problems start before the dashboard — in how data moves between tools, how metrics are defined, or how tracking was set up.
Start with a lightweight reporting check before committing to implementation.
Mini audits start at $400.
For teams with reporting in place, but trust gaps still showing up.
Revenue, CRM, and product data do not match — and the team spends more time checking numbers than using them.
See related work ↓Ad platform, GA4, store, and CRM numbers are close, but not reliable enough for channel decisions.
See related work ↓Client reporting takes too much manual cleanup before it can be shared or explained.
See related work ↓Reports depend on spreadsheets, PDFs, or manual cleanup before the team can trust the numbers.
See related work ↓Check GA4, GTM, conversions, and key tracking logic for obvious gaps or errors.
Review dashboard metrics, dimensions, filters, and data source connections.
Check how data moves between GA4, ads, CRM, Shopify, Stripe, BigQuery, or Sheets.
Identify what looks unreliable, duplicated, missing, unclear, or worth fixing.
Provide a clear fix plan with scope options if implementation is needed.
Summarize what was checked, what changed, and what still needs attention.
You can start with a small check before deciding whether you need implementation help.
For teams that have reporting in place, but do not fully trust the numbers.
Includes: issue list, first checks, and recommended next steps.
For teams that want the issues found in the audit fixed.
For teams that need a cleaner reporting workflow.
For ongoing reporting checks and small fixes.
Starting ranges, not fixed packages. Not sure what fits? I'll suggest the smallest useful next step.
Projects related to tracking, reporting reliability, automation, and dashboard workflows.
Projects shown with anonymized details and representative screenshots.
Built a BigQuery + Apps Script + Looker Studio reporting system. Reduced manual reporting work and helped the team monitor churn, members, classes, and operational KPIs consistently.
Outcome The team stopped rebuilding the same reports every week — churn, retention, and class data could be reviewed on a recurring schedule.
Automated extraction and standardization of data from multiple PDF formats using Python, regex, and Excel/VBA workflows. Reduced manual processing and improved data consistency for operational reporting.
Outcome Manual PDF and spreadsheet work became a repeatable process, so reports could be prepared faster and more consistently.
Set up a new GTM structure, configured GA4 event tracking, validated live event collection, and built Looker Studio reporting views so interaction data could be reviewed more reliably.
Outcome The team had one cleaner place to review conversion data, instead of manually checking GA4, CRM, and Looker Studio separately each week.
A dashboard is only useful if the data behind it is reliable. I do not just build charts. I look at the tracking logic, data flow, metric definitions, and reporting workflow behind them.
I work best with teams that need someone technical enough to handle GA4, GTM, SQL, BigQuery, and automation, but practical enough to explain the issues clearly to non-technical stakeholders.
Send the tools you use, what numbers do not match, or what feels unreliable. I'll reply with where I would check first.