Layer 03 · Connected AI
674 hours saved. Leadership stopped cleaning data and started using it.
Network Security Leader · Product Operations & Customer Intelligence
The Client
One customer, three systems, no single answer
A network security leader's picture of each customer lived in four streams: Salesforce held the accounts and opportunities, Zendesk held the support history, and product usage arrived from two separate sources. As the company scaled, answering a simple question — how is this customer actually doing? — meant stitching them all together by hand.
The Challenge
Strategic leaders were doing data janitor work
Product Ops spent roughly 13 hours per week manually reconciling data across those systems. Data mismatches and orphaned rows delayed critical insights by days. Leadership couldn't trust the data they had, so every strategic decision required another round of manual verification. Talented people were being used as a data pipeline.
Our Approach
Build a Customer 360 on explicit, auditable matching
We built a Workato pipeline to ingest, normalize, deduplicate, and update a single-view Customer 360 from the core sources, with matching logic and governance to ensure reliable reporting. The key insight: trust comes from auditability. Every record in the 360 traces back to an explicit account mapping — exact Salesforce IDs where they exist, maintained mapping tables where product code names and system aliases don't line up. No black-box guessing, because a match nobody can explain is a number nobody will trust. It is the same governance-first standard we bring to every Workato build.
The Architecture
Four sources in. One trusted record out.
Salesforce, Zendesk, and two product-usage sources flow through a Workato pipeline into a single trusted Customer 360 — feeding unified leadership dashboards on a 12-hour refresh and Slack alerts triggered on specific data conditions.
Design Principles
Explicit, auditable matching
Exact IDs where they exist; maintained mapping tables where product code names and aliases don't line up. Every match is traceable.
Governance and data quality checks
Not just ingestion, but validation at every step, with clear flagging when records don't meet quality thresholds.
Access controls for trust
If leadership can't trust that only the right people are writing to the Customer 360, the data integrity problem just moves upstream.
Insight latency is a cost
When decisions require waiting days for data reconciliation, that delay has a dollar value. We treated time-to-insight as a metric from day one.
The Results
Leadership stopped verifying and started deciding
674+ hours a year back. The roughly 13 hours a week Product Ops spent on manual reconciliation went to zero — the pipeline does it on every 12-hour refresh.
Insights arrive 14x faster. Time-to-insight dropped from days of stitching and verification to a standing 12-hour cadence. When a question comes up, the answer is already assembled — and condition-based Slack alerts reach the right people without anyone watching a dashboard.
The verification loop is gone. Because every record traces to an explicit, auditable mapping, leadership stopped re-checking the data before using it. That trust — not the hours — is the real return, and it is what our data and automation services are built to deliver.
Technology
Is your team a human data pipeline?
Bring us the reports nobody trusts and the reconciliation that eats every week. We'll show you what a governed Customer 360 looks like in a 30-minute working session.