What challenges come with corporate data?

Manufacturing companies generate more data today than ever before: machine and sensor data from production, order and customer data from ERP and CRM, quality and maintenance data, plus growing volumes of communication and documentation data. The real challenge is rarely the sheer volume of data itself.

It becomes difficult because this data is created in two entirely separate worlds — IT on one side and OT (operational technology: production equipment and machine controls) on the other. Without a shared picture, data volume grows, but insight does not.

Every new machine, every new system, and every additional sensor generates more data. Over years this adds up to a huge volume of historical and ongoing data, spread across the widest range of formats, protocols, and systems — from classic PLC controls to modern cloud ERP systems.

This growth is usually organic and unplanned: systems get introduced one after another, without a shared data infrastructure being considered from the start. The result is a landscape that has grown historically, one that nobody has a complete overview of anymore.

IT and OT have historically had different priorities: IT stands for security, integration, and standardization; OT stands for availability, real-time capability, and robust control of equipment. These different cultures have led both areas, over decades, to build largely isolated system landscapes of their own.

The result is data silos: production data sits in historians and control systems that IT rarely has visibility into, while ERP and planning data is often not available to production in real time. Each side only sees a slice of the full picture.

Without a connection between IT and OT, a lot of potential goes unused: maintenance decisions get made without visibility into actual machine data, production planning happens without current order or material data, and quality problems are often caught late because the relevant data sits scattered across different systems.

In practice, this means more manual coordination between departments, longer response times during disruptions, and decisions based on outdated or incomplete information instead of a current, overall picture of production.

As data volume grows, so does the question of who is responsible for data quality. Without clear ownership, data errors, duplicate records, and outdated information often go unnoticed for a long time — with direct consequences for decisions built on that data.

Compliance and transparency requirements (such as GDPR or industry-specific regulations) make this challenge even sharper: if you don't know where which data lives and how current it is, meeting reporting obligations becomes difficult.

The challenge is not primarily a lack of technology — most companies already have capable IT and OT systems in place. The real problem is the missing connection between these systems, not the absence of individual tools.

It's also not a problem that more isolated point solutions can fix. One more dashboard or one more report per department tends to add complexity rather than create a reliable, shared data foundation.

The biggest obstacle is usually organizational: IT and OT teams speak different languages, work with different priorities, and are rarely jointly responsible for a shared data infrastructure. Without a clear mandate, bringing IT and OT together stays a side issue.

On the technical side, proprietary protocols, older machine controls without modern interfaces, and historically grown, undocumented system landscapes make integration harder still. Successful projects address both levels — organizational and technical — together, from the start.

pronubes brings IT and OT data together through a central backbone, without replacing existing systems. Machine and sensor data from production, ERP and planning data from IT — all of it flows into one shared, structured data base.

The result is a reliable data base that every department can rely on: transparent, consistent, and compliant. Instead of growing data volumes with no shared picture, a common view emerges that everyone involved can work from.