Industrial Data Management

From Data Chaos to Software-Defined Factory

A Flood of Data, Zero Impact: Why Companies Lose Billions

Unplanned downtime costs the world’s 500 largest industrial corporations roughly USD 1.4 trillion per year—about 11 % of their annual revenue. In the automotive sector alone, outages add up to USD 2.3 million per hour.
At the same time, the global volume of machine data doubles roughly every 18 months. Yet most of that information remains locked away in proprietary silos. Topics such as downtime prevention, energy efficiency, or regulatory compliance usually make it into pilot projects, but rarely reach full production or enterprise-wide scale. What’s missing is a reliable, scalable approach to data: Industrial Data Management (IDM).

Industrial Data Management—The New Baseline Between IT & OT

IDM creates the technical and organizational foundation for capturing, standardizing, and managing industrial data in real time, across every site. With IDM, digital use cases like predictive maintenance or AI-driven workflows can truly scale.

Core principles of successful IDM

core principle	why it matters
Edge before Cloud	<50 ms reaction times and latency, high availability on constrained bandwidth, plus local buffering for shop floor resilience
Unified Namespace (UNS)	A single “source of truth” for maximum data consistency
Infrastructure as Code & DevOps	Versioning, audit trails, traceability, and rapid rollback
Zero‑Trust‑Security	mTLS, role-based access control, segmented networks

What Is Industrial Data Management?

In the discussion about digital factories, networked value creation and AI-supported applications, people often talk about applications – but rarely about the essential prerequisite for this: the continuous availability of relevant data in real time. This is precisely where Industrial Data Management (IDM) comes in. It is the “hidden infrastructure” that determines whether digital strategies scale – or remain stuck in pilot phases.

Industrial data management refers to all technical and organizational processes that are necessary to collect, standardize and contextualize production data from a variety of sources – machines, sensors, control systems, energy management systems, building technology. The aim is to turn isolated raw data into a consistently usable database that serves both operational and strategic purposes.

Five must-have capabilities:

1. Data Freshness – Near real-time data for automation, condition monitoring, and predictive maintenance.
2. Data Quality – Consistent structures, uniform semantics and complete metadata are crucial for reliability and further processing.
3. Cross-Site Availability – Data must no longer be tied to individual machines or plants – a central data model is required.
4. Interoperability – Different systems (e.g. MES, ERP, cloud analytics) must be able to access the same data via standardized interfaces.
5. Governance & Compliance – The origin, integrity and authorization of the data must be traceable at all times – especially in the context of CSRD, ISO 27070 or TISAX.

IDM is therefore not just a conceptual IT tool, but the decisive basic technological and organizational technology for every modern factory. Those who fail to lay this foundation consistently will be able to set up digital use cases, but will not be able to scale them – an expensive mistake that is already causing disillusionment in many organizations today.

Market pressure and regulatory reality: why industrial data management is now mandatory

The digital transformation of the manufacturing industry is no longer a voluntary optimization strategy; it is a business and regulatory obligation. With the mandatory CSRD reporting obligation (Corporate Sustainability Reporting Directive), European industrial companies in particular will be under pressure from 2025 to disclose their ESG performance based on reliable production data.
Companies that do not systematically record energy, material or waste consumption in an audit-proof manner run the risk of missing targets, incurring legal risks or losing funding.
Industrial data management is therefore not only the key to increasing efficiency, but also the bridge between production reality and reporting obligations. It becomes an integral part of modern, strategic corporate management.

Architecture with vision: How modern industrial data solutions become scalable

Anyone designing industrial data management on a greenfield site would prioritize three things: maximum connectivity, minimum latency and complete governance. This is exactly what a systematic architectural approach that clearly separates the three levels of industrial data processing does:

(1) Data collection at the edge:
Machine data is collected in local agents – where it is generated. These edge agents speak over 30 common OT protocols (e.g. OPC UA, Modbus, S7), buffer in the event of network failures and can inject initial metadata, such as location, time or machine type.

(2) Data structuring & orchestration in the Unified Namespace (UNS):
The integrated MQTT broker is used to transfer structured data streams into a hierarchical namespace – semantically consistent, easily searchable and API-accessible. This creates a central, real-time-capable database for all downstream systems.

(3) Data distribution via standardized interfaces:
REST, MQTT, Kafka, OPC UA – whether cloud analytics, MES or reporting tool: Access to all data is API-first, with role-based access and zero-trust security.

This architecture is more than just IT design. It becomes a strategic enabler for every form of data-driven innovation, from condition monitoring to GenAI agents on the shop floor.

Cybus Connectware: Why this IDM solution is an industrial blueprint

Cybus Connectware is one such industrial data management platform that translates these principles into industrial practice, building bridges not only technologically but also organizationally. It is used by leading industrial companies as a Factory Data Hub. In other words: As the backbone of their data-driven production control. The strengths of the platform lie in five key performance dimensions:

• Scalability: thanks to horizontal agent architecture and self-clustering brokers, Connectware can process millions of messages per minute with stable performance even as complexity grows.
• Vendor neutrality: No ties to specific machine manufacturers or cloud providers. It protects against lock-in and creates digital sovereignty, especially for European manufacturers.
• Real-time capability: Data can be processed with latencies significantly lower than 50 ms and enable automatic feedback loops for process sequences.
• Governance & compliance: With TLS/mTLS, role-based access control, network segmentation, IT security is natively integrated.
• Standardization & reusability: Configurations are declarative via YAML. Once created, they can be replicated quickly and consistently to any number of plants or machines.

The platform is used by companies such as Porsche, Handtmann, Krone, Grimme and Liebherr and serves both operational excellence and strategic transformation programs. It thus exemplifies the new category of IDM solutions that do not replace production-critical systems, but rather intelligently connect them with one another.

KPI	without IDM	with IDM via Connectware
Integration time per asset	5 days	10 minutes
MTTR* *Mean Time to Repair	6 h	1,5 h
ROI Greenfield	–	< 12 months
ROI Brownfield	12 months	6 weeks

Measurable business impact: what benefits does industrial data management really bring?

Any technology is only as valuable as its measurable contribution to a company’s success. With Industrial Data Management, this contribution and the return on investment (ROI) can now be quantified in concrete terms. Evaluations by our customers show

• Integration time per machine: reduction from an average of 5 days to 30 minutes – a time gain of almost 100%.
• Return on investment (ROI): In greenfield plants, the investment is typically amortized in under 12 months, in brownfield plants in as little as 6-8 weeks.
• Business interruptions: Real-time monitoring and predictive maintenance can reduce mean time to repair (MTTR) by up to 50%.
• Sustainability & ESG: Energy savings of up to 15% are possible through the data transparency gained alone.

Such effects show: Industrial Data Management is no longer just an IT project – but an operational lever for speed, scalability and sustainability. Short- and long-term goals such as reducing production costs, shortening innovation cycles and future-proof fulfillment of customer requirements can be achieved.

Strategic roadmap: Five steps to industrial data management

A successful start to industrial data management follows a clear 5-step model:

1. Prioritize goals and use cases: Clarify which business challenges (e.g. downtime, energy efficiency, traceability) should be solved with data.
2. Carry out a system and data inventory: Get a complete overview of existing machines, protocols, latency requirements and security zones.
3. Set up a pilot factory: Select a plant or line, make your data available through Connectware, define the UNS and bring the first use case live within a few weeks.
4. Establish governance and operations: Establish scalable configurations, role models and operational processes for the platform.
5. Scale and reuse: Roll out the solution to other plants using templates and self-service – and expand data-based services based on this.

This turns a pilot project into a resilient, scalable foundation for your software-defined factory – without any system discontinuity.

Conclusion: Why Industrial Data Management is becoming a decisive competitive advantage

Industrial data management is no longer an IT side note, but the backbone of the software-defined factory. Those who establish a scalable, auditable data foundation now will reduce downtime, accelerate innovation and create the conditions for AI-driven business models – and secure a decisive advantage long before the competition even starts.