IT/OT convergence has been a buzzword for over a decade, yet most organizations still struggle to make it work. Despite billions invested in digital transformation initiatives, the divide between Information Technology and Operational Technology teams remains one of the most persistent challenges in industrial organizations.
In this guide, we'll explore why convergence is so difficult, what successful organizations do differently, and how modern approaches are finally making true collaboration possible.
Understanding the Divide
Before we can bridge the gap, we need to understand why it exists.
Different Priorities
IT Teams Focus On:
- Data security and compliance
- System standardization
- Cost optimization
- Rapid innovation cycles
- Enterprise-wide visibility
OT Teams Focus On:
- Safety and reliability
- Process continuity
- Equipment longevity (20+ year lifecycles)
- Real-time determinism
- Production output
These aren't just different priorities—they can actively conflict. IT's desire to standardize on modern platforms clashes with OT's 15-year-old-but-perfectly-functioning PLCs. IT's security policies can break real-time control loops. OT's air-gapped networks prevent the visibility IT needs.
Different Languages
The terminology barrier is real:
| IT Term | OT Equivalent | |---------|---------------| | Server | Controller/PLC | | Database | Historian | | API | OPC-UA/MQTT | | Downtime | Unplanned outage | | Patch Tuesday | Production stoppage |
When teams can't communicate effectively, collaboration suffers.
Different Risk Tolerances
This is the fundamental issue. In IT, if a system goes down, you lose productivity. In OT, if a system goes down incorrectly, you might lose a facility—or worse, lives.
OT engineers have seen what happens when IT principles are blindly applied to operational systems:
- Automatic updates that brick controllers
- Security scans that crash SCADA systems
- Network changes that introduce latency into control loops
This history creates justified skepticism about any IT-driven initiative.
Why Previous Approaches Failed
The Data Lake Dream
The most common convergence approach: "Let's centralize all OT data in a cloud data lake where IT can analyze it."
Why it fails:
- OT teams resist sending production data off-premises
- Data movement creates latency, breaking real-time use cases
- Governance becomes a nightmare with duplicated data
- The project takes years and costs millions
- By the time it's done, requirements have changed
The "Rip and Replace" Fantasy
"Let's standardize on a single platform across IT and OT."
Why it fails:
- OT equipment has 20+ year lifecycles
- Replacing working systems introduces risk
- The cost is prohibitive
- There's no single platform that does everything well
The Middleware Maze
"Let's build integration layers between every system."
Why it fails:
- Point-to-point integrations don't scale
- Maintenance burden grows exponentially
- Each integration is a potential failure point
- Context gets lost in translation
What Works: The Modern Approach
Successful convergence doesn't require choosing between IT and OT priorities. It requires approaches that respect both.
Principle 1: Data Sovereignty
The most successful convergence initiatives start with a simple premise: OT data stays in OT.
This doesn't mean IT can't access it—it means the data doesn't move. Query it in place. Analyze it in place. Build dashboards that pull from source systems in real-time.
This addresses OT's core concerns:
- ✅ No data leaving secure OT networks
- ✅ No impact on production systems
- ✅ OT maintains control over their systems
- ✅ No new infrastructure in OT environments
Principle 2: Read-Only Access
The second principle: IT gets read-only access to OT data.
IT doesn't need to write to historians or modify SCADA configurations. They need visibility. By making the access explicitly read-only:
- Security concerns are minimized
- There's no risk of IT changes affecting production
- Audit trails are simple
- OT approval is easier to obtain
Principle 3: Semantic Translation
Instead of forcing OT to adopt IT naming conventions (or vice versa), create a semantic layer that translates between them.
IT View: /enterprise/chicago/production/line-1/temperature
↕
[Semantic Layer]
↕
OT Reality: PI:CHIC_PROD_L1_T101.PV
Both teams use their familiar conventions. The translation happens transparently.
Principle 4: Incremental Value
Don't try to boil the ocean. Start with one high-value use case:
- Cross-site production comparison
- Quality investigation acceleration
- Maintenance correlation analysis
Deliver value in weeks, not years. Build trust through results. Expand scope based on success.
A Practical Convergence Framework
Here's a framework that works:
Phase 1: Discovery (Weeks 1-4)
Goals:
- Identify 2-3 high-value use cases where IT needs OT data
- Map the source systems involved
- Document current pain points and workarounds
- Get OT stakeholder buy-in on read-only access
Deliverable: Use case documentation with defined success metrics
Phase 2: Connection (Weeks 5-8)
Goals:
- Deploy read-only connectors to source systems
- Establish secure access paths (jump hosts, DMZs as needed)
- Verify data accessibility without production impact
- Document the semantic mapping requirements
Deliverable: Connected systems with verified data access
Phase 3: Contextualization (Weeks 9-12)
Goals:
- Build the semantic model mapping OT tags to business context
- Validate mappings with OT subject matter experts
- Create the unified view IT teams will consume
- Test query performance and accuracy
Deliverable: Working semantic layer with validated mappings
Phase 4: Enablement (Weeks 13-16)
Goals:
- Deploy user interfaces (dashboards, query tools)
- Train IT analysts on accessing OT data
- Establish governance processes
- Measure against success metrics
Deliverable: Production system with measured business value
Phase 5: Expansion (Ongoing)
Goals:
- Add additional data sources
- Expand to new use cases
- Refine semantic model based on usage
- Scale to additional sites
Deliverable: Continuous improvement roadmap
Measuring Success
How do you know convergence is working? Track these metrics:
Efficiency Metrics
- Time to answer cross-system questions
- Number of manual data requests eliminated
- Engineering hours recovered
Collaboration Metrics
- Joint IT/OT project success rate
- Cross-team meeting frequency
- Shared dashboard usage
Business Metrics
- Downtime reduction from faster root cause analysis
- Quality improvement from better visibility
- Cost avoidance from infrastructure consolidation
Common Pitfalls to Avoid
Pitfall 1: Starting with Technology
Don't lead with "we're implementing [tool X]." Lead with the business problem you're solving. Technology is a means, not an end.
Pitfall 2: Ignoring Change Management
Technical integration is the easy part. Getting IT and OT teams to collaborate effectively requires deliberate change management. Invest in joint workshops, shared objectives, and collaborative problem-solving.
Pitfall 3: Underestimating OT Complexity
IT teams often underestimate how complex OT environments are. There's a reason those systems have 20-year lifecycles—they're deeply embedded in operational processes. Respect that complexity.
Pitfall 4: Over-Engineering the Solution
Start simple. A working solution that covers 80% of use cases is infinitely more valuable than a perfect solution that's still in development.
The Path Forward
IT/OT convergence doesn't have to be a multi-year, multi-million-dollar initiative. With the right approach—data sovereignty, read-only access, semantic translation, and incremental delivery—organizations can achieve meaningful convergence in months.
The key insight is that convergence isn't about merging IT and OT into a single team or platform. It's about enabling collaboration while respecting the different priorities and constraints of each domain.
Ready to bridge the IT/OT divide in your organization? Request a demo to see how Conduit enables convergence without compromise.