Why Your IT and OT Data Lines Aren't Connected (And How to Fix It.)
You're capturing more data than ever from the factory floor, but are you any wiser for it? For many manufacturers, the answer is no. Critical insights into efficiency, quality, and profitability are getting lost in translation, stuck in the gap between your operational and business systems.
This isn't a data storage problem; it's a data communication problem.
Would you accept a physical assembly line with missing parts, broken conveyors, and no quality control? Of course not. Yet, this is exactly how many data workflows operate, creating a disconnect between your Information Technology (IT) and Operational Technology (OT) data.
This blog aims to break down why this costly gap exists and provide a clear blueprint for fixing it with the help of cloud technology, helping you turn data noise into profitable action.
The Great Divide: What Are IT and OT Systems (and Why Don't They Talk)?
To solve the problem, we first need to understand the two sides of the data divide.
IT Data: The Language of Business
Information Technology (IT) systems are the backbone of your business operations. They include your Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and supply chain logistics software. IT data is the language of the head office; it's typically structured and transactional, focused on answering the what and why of the business , from finance to order fulfilment.
OT Data: The Voice of the Machine
Operational Technology (OT) systems on the other hand, are the nervous system of your factory floor. They manage and monitor physical processes through tools like SCADA systems, PLCs, and an ever-growing number of sensors. OT data is the real-time voice of your machinery, speaking in a high-volume stream of temperatures, pressures, cycle times, and vibration readings.
The Reasons for the Rift
These two worlds rarely communicate effectively for a few key reasons:
Historical Separation: Traditionally, IT and OT were managed by different teams with different goals. IT focused on data security and management, while OT prioritised plant uptime and production continuity.
Technical Barriers: The systems were never designed to speak the same language. They use different network protocols, data formats, and often rely on legacy technology that makes integration a complex challenge.
Security Concerns: For many, the fear of creating vulnerabilities by connecting sensitive operational systems to broader corporate networks has kept them siloed.
The Real-World Cost of a Broken Data Assembly Line
When IT and OT can't communicate, it creates tangible blind spots and inefficiencies that directly impact your bottom line.
Inefficiency and Blind Spots: Without a unified view, you can't see how a machine fault is impacting order fulfilment schedules. This leads to reactive problem-solving, manual data chasing, and delayed decisions.
Quality Control Gaps: You can't correlate raw material batch data from your ERP system with real-time production parameters to predict and prevent quality issues before they result in waste.
Increased Operational Costs: The inability to perform predictive maintenance is a classic example. You can't use OT sensor data to predict a potential failure and proactively schedule maintenance in your IT system, leading to more unplanned downtime and expensive emergency repairs.
Missed Opportunities: You are sitting on a goldmine of data. When connected, this information can unlock powerful process optimisations, drive innovation, and build a more resilient, data-driven supply chain.
How to Fix It: Building a Unified Data Core with Google Cloud
The solution isn't to add another piece of disconnected software; it's to build a new foundation. To fix the "two-speed factory," you need to establish a Unified Data Core—a central nervous system that can finally get your IT and OT systems speaking the same language. This is the heart of your 'Assembly Line of Analytics,' and it's built on the scalable and secure infrastructure of Google Cloud.
The journey to unification doesn't require a risky, rip-and-replace overhaul. Instead, it's a practical, scalable process.
Your Unification Journey: A 3-Step Methodology
We advocate a "start small and scale" approach that delivers value quickly and builds momentum for digital transformation.
Step 1: Connect a High-Value System
Don't try to boil the ocean. Begin by identifying a single, high-impact area where the IT/OT disconnect causes significant pain. This could be connecting production line output sensors (OT) with your order management system (IT) to get a real-time view of production efficiency against sales forecasts.
Using a solution like Google Cloud's Manufacturing Data Engine, you can rapidly ingest data from both sources, creating the first connection point into your Unified Data Core. This allows you to prove the value of data unification with a concrete, measurable win.
Step 2: Unify IT & OT for a Single Workflow
With data from your first high-value system flowing into a centralised platform like Google BigQuery, you can create your first automated data workflow. This is the first "production line" in your new Assembly Line of Analytics.
For example, when OT sensor data shows a machine is operating outside its normal parameters, an automated workflow can be triggered. Instead of a red light flashing on the factory floor, an alert is sent to the right people, and a maintenance ticket with all the relevant operational data is automatically created in your IT service management system. This transforms a reactive process into a proactive, data-driven action.
Step 3: Scale to a Full Factory View Across IT & OT
Once you've demonstrated success with a single workflow, you've created the blueprint to scale. The beauty of a cloud-native platform is its ability to expand. You can begin connecting more and more data sources your MES, SCM, and even building management systems into the Unified Data Core.
As your unified dataset grows in BigQuery, you can leverage Google Cloud's advanced AI and machine learning tools like Vertex AI. This is where true transformation happens. You can move beyond simple alerts to:
Predictive Maintenance: Forecast asset failure before it occurs.
Quality Enhancement: Identify the root causes of production flaws.
Cost Optimisation: Leverage predictive insights to reduce energy consumption and material waste.
This three-step journey, powered by Google Cloud, de-risks the digital transformation process and delivers a rapid return on investment. It's about building a scalable foundation that turns siloed information into a seamless flow of finished intelligence.
Don't Just Collect Data. Put It to Work.
A broken assembly line costs you money every minute it's down. A broken data line is no different. The gap between your IT and OT systems is a fundamental inefficiency that prevents you from unlocking the true potential of your manufacturing operations.
It's time to stop guessing and start knowing. By building a strategic 'Assembly Line of Analytics,' you can finally unify your data from the shop floor to the top floor and empower your teams to make faster, smarter decisions that drive profitability. Don’t worry, you don’t have to go it alone. Our team of technical Google Cloud experts are here to help. We’ll start by establishing a strategy before navigating the right path for you going forward.
Time to fix your data? Book a data discovery call with our experts to get your blueprint.
