Where Do I Start? A Three-Step Framework for Your Industrial Data Journey
You've seen the capability maps, explored the vendor landscape, and sat through the demos. Now comes the question everyone eventually asks: where do I actually start?
We hear it all the time. After reading our articles on the Data Platform Capability Map, after browsing our DataOps Vendor Database, after watching the conference talks and nodding along — the same question comes back:
“OK, great. But where do I start?”
It’s a fair question. The vendor landscape is overwhelming. The technology options are endless. And let’s be honest: most of the advice out there jumps straight to solutions without helping you figure out what problem you’re actually solving.
So here’s our answer. It’s not complicated, but it does require honesty, discipline, and the willingness to involve the right people.
The way we approach this question is by organizing a combined maturity assessment and workshop. In that workshop, we both identify the current state as well as the future goals.
It’s extremely important to involve people from IT, OT and Operations in these types of activities, otherwise, you are again back to making technology choices nobody is waiting for.
And the good news is that we have written down our entire approach! 🙂
(and we are also teaching about it in our ITOT.Academy)
Step 1: Define Your Goal
Before you evaluate a single vendor, before you spin up a single broker, before you write a single line of code — you need to answer one question: what are you trying to achieve?
This sounds obvious. It’s not. We’ve seen plenty of organisations jump straight to technology (”We need a UNS!” or “Let’s build a data lake!”) without ever aligning on why. The result? Expensive pilots that impress in boardrooms but die on the shopfloor.
The vendors in our DataOps Vendor Database are more than happy to help you with the technical side. They have the tools, the expertise, and the reference architectures. But here’s what they will NOT do for you: they won’t define your goals.
That’s your job.
And the best way we’ve found to define those goals? Run a Data Maturity Assessment Workshop.
What Happens in the Workshop?
We use our five-stage Data Journey Model — from the “Dark Ages of Data” (Stage 0, where data is trapped in SCADA systems) all the way to guided optimisation (Stage 4, where models actively recommend decisions). If you’ve followed our Data Platform series, you’ll recognise these stages.
The workshop itself follows four phases:
Current State — Every participant places a sticky note where they think the organisation sits today. No discussion yet, just collect perceptions. What usually happens: IT and Operations end up on opposite sides of the board. That visual gap? It’s worth more than any consultant’s slide deck.
What’s Working — Identify the systems and tools already delivering value. This is where IT learns things from Operations they’ve never heard of, and vice versa. You’ll be surprised how much good work is happening in pockets nobody knows about.
Blockers — What’s holding you back from the next stage? Missing technology? Lack of skills? Organisational silos? Budget? Data quality? Get them visible. Group them. You’ll notice the same themes emerging across departments.
Future State — Where do you want to be in 2–3 years, and what would that enable? Not “Stage 3 because it sounds impressive” — but what you’d actually be doing at that stage. What decisions would be faster? What problems would disappear? Good future state statements describe outcomes, not shopping lists.
From there, you prioritise. We use four criteria: High Enablement (will this unlock more use cases?), High Value (does it solve a real pain point?), High Impact (will people notice?), and High Visibility (never underestimate the power of a visible win for securing your next budget).
The sweet spot sits at the intersection.
Get the Moderation Guide
We’ve packaged everything you need to run this workshop into a free Moderation Guide. It includes step-by-step facilitation instructions, the Data Journey Model explained, and ready-to-use whiteboard templates — available as PDF and as a Miro board so you can run the session physically or remotely.
Whether you’re doing this with five people in a meeting room or twenty across multiple sites, the guide gives you a structured way to build consensus on what actually matters.
One honest note: the exact method you use to run this workshop is less important than ensuring the right people are in the room and willing to engage honestly. You can’t workshop your way out of IT and OT stuck in silos. But if the group genuinely wants to collaborate and improve, a grassroots initiative can gain traction quickly.
Step 2: Start Small, Build to Scale
With your goals defined and your first use case(s) identified, it’s time to move. But “start small” is only half the mantra. The full version is: start small, think big — and thinking big is not optional.
Starting small without a vision means you’ll solve one problem and create three new ones. Thinking big without starting small means you’ll be stuck in analysis paralysis for another year. You need both.
What “starting small” actually looks like depends on several factors:
Your current capability. This is exactly what you assessed in Step 1 using the Data Journey Model. If you’re at Stage 0, your first step is getting data out of those SCADA systems. If you’re at Stage 1, it’s adding context. Don’t try to do predictive maintenance when you can’t even reliably access your historian data.
Your OT complexity. Not every manufacturing environment is the same, some have heavily invested in DCS systems, optimizers and the like since decades, others are perfectly fine with a couple of standalone HMI’s. The distinction matters. An MQTT broker is perfect for OT-light and IIoT use cases, but it’s not necessarily the right starting point if you already have a mature SCADA/MES landscape.
Your willingness to build versus buy. Some organisations want full control and are ready to invest in internal platform teams. Others prefer managed solutions. Neither is wrong — but be honest about what your team can actually sustain.
Bonus: your production process type. Asset-heavy environments (pumps, compressors, turbines) benefit from asset-first modelling. Process-driven environments (batch, continuous) often need process-first modelling. This shapes your data architecture more than most people realise.
Whatever you choose, one principle applies: design like you’ll be solving five problems tomorrow, even if you’re only solving one today. Use structured data models. Document your integration points. Choose tools that allow for modular upgrades. It’s not about slowing down — it’s about not painting yourself into a corner.
Step 3: Evolve
Here’s what nobody talks about at the beginning: the plateau always comes.
You’ve started. You’re building momentum. Use cases are multiplying. Early wins have bought you credibility and budget. And then, gradually, things start to slow down.
It’s not dramatic. It’s subtle. But the warning signs are predictable:
It’s becoming more and more difficult to add new data sources.
Users are starting to complain about speed. Dashboards load slowly. Queries time out.
Your team spends more time maintaining than innovating. Bug fixes dominate the backlog.
Key person dependencies are increasing, not decreasing. When someone goes on holiday, projects stall.
Workarounds are multiplying. Instead of using the platform as designed, teams are building custom solutions around it.
These signs don’t appear overnight. They accumulate gradually. That’s what makes them dangerous — you can rationalise each one individually while missing the pattern collectively.
The plateau isn’t failure. It’s a signal. Technology evolves, requirements change, and what worked brilliantly at 10 sites might not work at 50. Recognising it early and evolving before you stall is what separates sustained transformation from one-hit wonders.
Starting again doesn’t mean throwing everything away. Reuse what works. Keep the organisational structures that enabled your success. Preserve the trust you’ve built on the shopfloor. But be honest about what needs to change.
We covered this in depth in our article on Fighting Entropy: How to Scale Without Spaghetti. If you’re already past the starting phase, that one’s essential reading.
So, Where Do You Start?
Right here:
Define your goal — Run a Data Maturity Assessment Workshop with IT, OT, and Operations in the room. Surface the perception gaps. Build consensus on what matters.
Start small, build to scale — Pick your first use case based on value, impact, and visibility. Choose technology that fits your OT complexity and current maturity. Design for tomorrow.
Evolve — Watch for the plateau. Recognise the warning signs early. Adapt before friction becomes crisis.
The Moderation Guide gives you everything you need for Step 1. It’s the foundation everything else builds on.
Download the free Data Maturity Assessment Workshop Guide →
Includes: step-by-step facilitation instructions, the Data Journey Model, and whiteboard templates (PDF + Miro board).
Want to go deeper? Check out our ITOT.Academy where we focus on concepts, not tools — and on frameworks, not features.