Solutions
Three ISO Standards as the Foundation for Trust
October 2025
The energy sector has never been more inclined to invest in digitalization. Pressure comes from all directions: regulatory requirements for operational safety, societal expectations for reliable delivery, and rapid electrification placing entirely new demands on power grids. Yet despite this enthusiasm, we see a recurring pattern: many projects fail to deliver promised value, or take significantly longer than expected to show results.
The problem rarely lies in the technology itself. Rather, it stems from a fundamental misunderstanding of what "out of the box" actually means in the context of industrial IoT for the energy sector.
Consider the classic example of SCADA systems (Supervisory Control and Data Acquisition). For decades, these systems have been the backbone of energy sector monitoring and control. They're robust, proven, and handle critical processes around the clock. But they were built for a different era and purpose - and when organizations today want to advance to predictive maintenance, advanced analytics, or automated workflows, they encounter limitations:
Data collection ≠ data analysis. SCADA delivers raw data, but organizations must build their own analytical capacity. This requires specialized expertise in both electrical engineering and data analysis - a combination that's hard to find and expensive to recruit.
Visualization ≠ action. Seeing data in real-time is valuable, but the step from "we see a problem" to "the system automatically generates a work order" often requires manual handling or extensive system integrations.
Installation ≠ value creation. When a SCADA system is installed, the organization has functioning monitoring. But actually using this data to optimize maintenance, reduce costs, or improve sustainability? That's an entirely different journey that can take years.
This illustrates an important point: what's included "out of the box" varies fundamentally between different solutions and technology generations.
When energy companies evaluate new IoT solutions for maintenance and condition monitoring, three critical questions should be considered:
It's not enough that data is collected. The question is: how long does it take before this data becomes concrete insights that drive decisions? A solution that's "out of the box" should deliver analyzed, interpreted, and visualized information from day one - not just raw data that the organization must then interpret.
The timeline doesn't begin at installation, but at the planning stage. For solutions requiring extensive customization, the implementation phase can take 6-12 months before the first data point is even collected. In a sector where electrification is happening now and climate goals have deadlines, "time to value" becomes critical. A solution that takes 18 months to implement and another 12 months before the organization sees savings may simply be too slow.
A true "out of the box" solution minimizes this preparation phase and allows the organization to focus on creating value instead of building infrastructure.
Here lies a hidden cost. If the system delivers raw data requiring in-house analytical capacity, the true cost becomes significantly higher than the investment. The ideal solution includes intelligent analysis developed by experts who understand both the technology (transformers, circuit breakers, oil systems) and the methodology (predictive modeling, trend analysis).
But it's not just about the analytical tools themselves. What the user sees - an intuitive user interface with clear recommendations - rests on extensive machinery behind the scenes. Operators ensuring stable communication around the clock, automatic updates rolled out without interruption, security patches, optimization of analytical engines, and continuous development of predictive models.
When an organization chooses to build themselves, they become responsible for every link in this chain. It's not just a technical challenge - it's a long-term commitment that ties up resources and requires specialized expertise rarely found internally in energy companies.
The critical gap in many digitalization projects is the step from insight to action. A complete solution shouldn't just warn of problems, but also prioritize actions, provide recommendations, integrate with existing work systems, and ideally automatically generate work orders.
It's natural to focus on initial product cost when budget is a reality for all organizations. But to get a fair picture of the investment, it's valuable to consider total cost of ownership (TCO). A platform or integration may look favorable on paper, but what does it really cost to make it deliver value?
Consider a typical scenario: An organization chooses to build their own solution around existing or new sensors, thinking "we'll save money by doing it ourselves." But when accounting for the full picture:
• Integration costs: 150-200 hours of development time to build data flows and analytical functions
• Specialized expertise: Recruitment or consultants for predictive modeling and data analysis
• Ongoing maintenance: Security updates, bug fixes, system upgrades
• Support and documentation: When something breaks, who fixes it? How quickly?
• Scaling costs: Each new function or integration requires new development
• Vendor lock-in: Being locked to a specific vendor's ecosystem limits future flexibility
A complete "out of the box" solution may have a higher purchase price, but includes analytical capacity, continuous updates, operational support, and a clear path to scaling - all at a predictable cost.
An agnostic platform that can integrate existing sensors and equipment provides additional advantages: it protects previous investments, reduces initial costs, and provides flexibility to choose the best components over time without being locked to a single vendor's ecosystem. For organizations wanting to focus on their core mission - delivering reliable electricity - rather than becoming systems integrators, the TCO perspective is critical.
Successful digitalization projects in the energy sector are built to grow with the organization. It can start small - monitoring 50 substations with basic temperature and humidity sensors. But when value becomes visible and ambitions grow, more substations, more sensors, deeper analytics, and integration with asset management systems can be added without extensive reconstruction.
A true "out of the box" solution is built for this journey. New functions can be added without creating information silos or requiring new capital investments. That's the difference between a scalable platform and a point solution - and it's future-proofing that's often underestimated in the procurement phase.
When the technical solution is in place - with everything from sensors to analysis to action recommendations - the next critical question becomes: who stands behind it? The partnership behind the technology can be the difference between a project that delivers continuous value and one requiring constant firefighting.
A true partnership means the vendor understands the energy sector's unique challenges, takes responsibility for the entire value chain, has proven the solution works in practice, and can show concrete results from similar implementations.
When these pieces are in place, "out of the box" becomes more than a technical description of something pre-installed. It becomes a description of value created from day one, of organizations able to focus on their core mission instead of becoming system integrators, and of a clear and proven path to predictive maintenance.
Energy sector digitalization isn't a technology project - it's a transformation journey where both destination and path matter. When energy companies evaluate their next investment in IoT and predictive maintenance, it's valuable to ask not just "does it work?" but "how quickly does it actually create value for the organization - and at what true cost?"
The difference between these questions can determine whether digitalization becomes a success story or yet another project that took longer and cost more than anticipated.