by Phil Swainson | 15 December, 2025
In Part 1, we explored how targeted automation reduced ticket noise and eliminated unnecessary manual effort. In Part 2, we turn our focus to a deeper — and often more challenging — problem: data consistency across operational systems.
Automation can only be as effective as the data it relies on. When systems disagree, workflows fracture, confidence erodes, and teams quietly revert to manual checks “just to be safe”. This engagement set out to change that.
The Real Challenge: Fragmented Operational Data
Our customer had invested heavily in best-of-breed platforms:
- Network monitoring
- IT service management
- Asset and inventory systems
- Order and provisioning platforms
Each system worked well in isolation — but collectively, they told different versions of the truth.
Common issues included:
- Devices existing in one system but not another
- Conflicting naming conventions
- Missing or stale attributes
- Manual reconciliation during incidents
None of this was visible on dashboards — but it was painfully visible to engineers during outages.
Why “Just Integrate It” Isn’t Enough
Many organisations attempt to solve this with point-to-point integrations. Over time, this creates:
- Tight coupling between systems
- Unclear data ownership
- Silent failures that go unnoticed
- Automation logic embedded in scripts instead of models
The result? Integrations that technically work, but operationally cannot be trusted.
We took a different approach.
Establishing a Clear Source of Truth
The first step was defining data authority.
Rather than allowing every system to update everything, we worked with the customer to establish:
- Which system is authoritative for which data
- Which attributes are mastered vs derived
- Where enrichment should occur
- What “good” data actually looks like
This governance layer is often skipped — but it’s foundational. Automation without clarity simply moves inconsistencies faster.
Automation Through Controlled Data Propagation
With ownership defined, we introduced controlled automation flows:
- Changes were pushed from authoritative sources only
- Unique identifiers ensured accurate matching
- Enrichment happened once, then propagated
- Updates were validated before distribution
Crucially, automation logic was data-driven, not hard-coded — allowing it to scale as systems and requirements evolve.
From Manual Reconciliation to Trust by Design
Previously, engineers routinely cross-checked multiple tools before acting. After implementation:
- Inventory, monitoring, and ticketing aligned
- Engineers trusted what they saw
- Onboarding workflows accelerated
- Incident response became more decisive
This wasn’t just about speed — it was about confidence.
The Bigger Picture: Automation Maturity
This project highlighted an important truth:
Automation maturity isn’t measured by how many scripts you run —
it’s measured by how much trust engineers place in the outcome.
By treating data as an asset rather than a by-product, the customer moved from reactive automation to intentional, governed automation.
Key Takeaways
- Automation fails without clear data ownership
- Integration is not the same as alignment
- Trust in data directly impacts operational behaviour
- Scalable automation is model-driven, not script-driven
What’s Next
This engagement laid the groundwork for more advanced capabilities:
- Smarter incident correlation
- Context-rich ticketing
- Predictive workflows
- Intelligence layers built on reliable data
When your data is consistent, automation becomes a force multiplier — not a risk.
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Thinking About Your Own Automation Journey?
If your teams still double-check tools before acting, that’s a signal — not a failure. It’s often the sign that data foundations need attention before automation can truly deliver.
Talk to us about building automation you can trust.
