Digital transformation remains a boiling hot topic around any production facility. Read why next-generation data historians are the most powerful engines for driving a digital culture change within your organisation.
In a Deloitte survey of 361 Industry 4.0 executives across 11 countries, 94% of respondents report digital transformation as their organisation’s top strategic initiative. A PwC survey of 200 European executives indicates that more than 9 of 10 of respondents are investing in digital factories, yet only 6% consider their factories to be fully digitised. Does this sound familiar?
Most manufacturers collecting production data still can't use most of it. That's not a knowledge gap or a tooling gap, it's a structural one: the data exists, but it's scattered, siloed, and locked to whoever happens to know which system it came from.
McKinsey's Global Lighthouse Network research puts a number on how often this stalls transformation entirely: at least 70% of manufacturers get stuck in what McKinsey calls "pilot purgatory," unable to scale a working pilot across the wider production network. The pilot works. It just never leaves the one line it was tested on.
Collection was never the hard part
SCADA and DCS systems have been capturing readings for decades, temperature, pressure, flow, all logged in real time. The problem is where that data lives afterward: scattered across siloed systems, retained for days or weeks instead of years, and locked to whoever knows the specific system it came from. You can't run root-cause analysis on data you can no longer see, and you can't benchmark this quarter against last year if last year was never kept anywhere queryable.
More data without structure is a swamp, not a lake.
Piling years of raw readings into a database doesn't create insight on its own. Without structure, it creates an unsearchable archive that requires a specialist to touch every time someone needs something from it. This is where a lot of digital transformation initiatives quietly stall, not in the pilot, but right after it: the data got collected, but nobody who actually needed it could get to it without submitting a request and waiting.
The distinction that actually matters: an open historian fixes access
This is the part most "digitize your data" advice skips entirely, and it's worth being specific about, because it's the actual mechanism, not just a rephrasing of the problem. The difference isn't that an open historian collects more data than a closed one. It's that the data comes out the other side already structured, in formats a BI tool or MES can query directly, over MQTT, Parquet, or a REST API, without a translation layer or a specialist sitting in between. That's what turns "we have the data somewhere" into "the engineer building a dashboard, the quality manager doing a batch comparison, and the plant director looking at group-level trends are all working from the same source, on their own."
What that looks like in practice
When food producer Roger & Roger implemented an open historian in 2019, the shift wasn't about collecting more data, they already had ERP, WMS, and MES systems running. It was about what happened once the data those systems generated became structured and reachable. "It has shifted us away from relying on gut feelings and makeshift Excel sheets to having real, actionable visibility," says Jan-Laurens Vandermeersch, Business Analyst Manufacturing. Operators now see an overview of the entire line and its KPIs directly on a dashboard, and adjust as needed, without waiting on a report from someone else.
The access problem shows up even more clearly once a company tries to scale. When Roger & Roger brought a new, greenfield site online in Hungary, IT Director Koen Van Ceulebroeck didn't have to start from zero: "Armed with insights from our initial rollout, we could embed an effective data model and reporting system from the start." That's only possible if the data model from the first site was actually structured enough to reuse, a swamp doesn't transfer to a new site, a lake does.
The actual transformation is who gets to ask questions
Digital transformation isn't a platform migration, and it isn't a pilot that ran successfully on one line. It's the moment someone outside the automation team can open a dashboard and answer their own question in minutes instead of filing a request and waiting a week. That shift, more people making decisions off real data, without a specialist as the bottleneck, is what actually breaks pilot purgatory. Everything else, the sensors, the platform, the migration, is infrastructure in service of that one shift.
Not sure if your current setup is collecting data or actually making it usable?
See how Factry Historian structures data for direct BI and MES access, or book a demo to walk through what that looks like for your plant specifically.
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