Governance and forecast quality

Infoblox CRM governance and forecasting

I standardised CRM governance and KPI definitions across sales, finance, and supply, so planning meetings stopped fighting about whose numbers were right and started feeding real go-to-market calls.

Employer / client: Infoblox
Duration: Jan 2020 - Apr 2022
Project type: Data and analytics

Architecture and GTM map

From governed data to go-to-market

Architecture diagram: Salesforce, ERP, and Excel sources flow through SQL quality gates into a governed layer with locked KPI definitions and a data-quality health score, then into Power BI and Tableau, with defects looping back to the checks. — Raw records pass through SQL quality gates into a governed layer with locked KPI definitions and a health score, then into reporting. Defects loop back into the checks.
CRM governance architecture

GTM enablement map: governed CRM data and a trusted forecast fan out into four go-to-market motions: segmentation and targeting, territory and quota planning, renewals and retention, and demand and replenishment planning. — The same governed data and trusted forecast fan out into four go-to-market motions, so sales, finance, and supply plan from one agreed number.
GTM enablement map

Process flow

How I work the steps

01
before Source pull
Salesforce, Excel, and ERP-style inputs gathered for the recurring review.
BA / Data
02
control Quality checks
Duplicates, stale ownership, segmentation, and mismatch checks run.
BA
03
handoff Field UAT / requests
Missing fields tested and requested through IT/Product Manager where needed.
BA / IT
04
after Dashboard drilldown
KPI view ties back to source-record confidence.
BA / BI

How I worked the problem

Ran KPI-definition workshops to surface where teams were using the same metric name to mean different things, then locked the shared definitions down before anyone touched a dashboard.
Built SQL audit checks for duplicates, missing segmentation, stale ownership, and SKU-account mismatches across CRM, ERP, and the curated Excel inputs.
Worked with data engineering on Python/SQL ETL so the recurring cleanup moved from manual edits into a repeatable reporting flow.
Designed Power BI navigation that went from the executive KPI down to the transaction record, so any challenged number could be traced back to source.
Tied the clean data and the forecast straight into the go-to-market calls it fed: segmentation, territory and quota planning, renewals, and demand or replenishment planning.

Measured results

What I delivered

60K+ volume

Accounts/SKUs governed

Audit scope across account and SKU records.

~60% cycle-time

Reporting cycle reduction

Python/SQL ETL killed the repeated manual fixes and waiting time.

~20% accuracy

Forecast accuracy improvement

Cleaner lifecycle, usage, and sales signals lifted forecast confidence.

Outcomes

Duplicate accounts, stale owners, and SKU mismatches got caught at the source instead of derailing the operating review.
Reporting stopped being a reconciliation exercise. Python and SQL handled the repeat cleanup so the same records weren't fixed by hand every week.
Replenishment and renewal conversations started from a number the room actually agreed on, because the dashboard told you how confident it was.
The go-to-market motions that ran on this data, segmentation, territory and quota planning, renewals, and replenishment, all started from the same trusted base.