Franco Pecchio on why data quality matters more than data quantity

At Hello Energy, we believe better decisions start with better access to data. But what does access to data really mean in practice, and what does it take to turn data into meaningful action? In our Conversations on Energy Data series, we speak with experts from across the energy ecosystem: consultants, engineers, technologists, and industry leaders who work with energy data every day.  

Together, we explore the opportunities, misconceptions, and challenges shaping the future of energy intelligence, and what it will take to make energy data more accessible, reliable, and actionable for everyone.  

For our first conversation, we spoke with Franco Pecchio, an environmental engineer and long-time advisor on energy efficiency and ESG strategy across both the industrial and real estate sectors. His central message is simple and clear: better energy decisions don’t start with more dashboards, they start with better data.

Can you briefly introduce yourself and tell us what drives your work?

“I am an environmental engineer whose career began in applied research at universities and institutions, focusing on energy policy for renewables and energy efficiency. This foundation naturally led into consultancy work across both the building and industrial sectors.

In 2015, after several years at leading consultancy firms, I founded my own company, Nuen. The focus on metering and data collection emerged organically from extensive energy and sustainability audits carried out in compliance with the European Energy Efficiency Directive (EED).”

How would you describe ‘access to data’ to someone outside your field?

“Access to data is often seen as simply having a cloud platform where you can analyse information, but that is only the surface of a much deeper system. In reality, ‘data’ carries many assumptions. It is essentially digital information that has been measured and transmitted, but what exactly was measured, and how? To make meaningful use of it, you need to understand what happens upstream: how the data is captured by different types of meters and sensors, and how it is carried through communication protocols such as Modbus, M-Bus, or BACnet. Each step can introduce errors or distortions.

This is why a system can appear precise while still being inaccurate. Measurements may be consistent but wrong due to installation issues, calibration limits, or operating conditions. Even small factors like sensor placement or communication dropouts can significantly affect the data, creating gaps or spikes that distort consumption profiles if not properly handled.

Without understanding this full chain from physical process to sensor, protocol, and platform, working with data can easily lead to misplaced confidence.”

What is the biggest misconception you encounter about the availability of energy data, among your clients or in the broader market?

“The most common misconception is that energy data is directly measured and available as a clean, raw input. In reality, it is almost always derived from a sequence of processing steps.

What is typically called an ‘energy value’ is usually calculated from cumulative counters that continuously register consumption. The value for a specific time interval is then derived by taking the difference between two successive readings. That simple step already introduces complexity. Cumulative systems are sensitive to issues such as counter resets, overflow, or communication dropouts. These can result in missing, negative, or unrealistic values. On a dashboard, this is often hidden behind smooth charts, while behind the scenes validation or correction logic may be applied.

This leads to a second misconception: that data availability equals data quality. In practice, frameworks like IPMVP (International Performance Measurement and Verification Protocol) explicitly distinguish between measured, estimated, and interpolated data, each with different levels of uncertainty. Mixing these without transparency can undermine the integrity of energy savings calculations.

Many platforms still report ‘100% availability’ while quietly filling gaps through interpolation or default values, without clearly indicating what is actually measured versus reconstructed. While this may be acceptable for operational monitoring, it becomes a serious issue in contexts like Energy Performance Contracting, where data is used to verify contractual savings. In those cases, hidden assumptions in the data chain can directly affect financial outcomes and the validity of verification reports.”

When energy data is well available and reliable, what concretely changes in the way decisions are made?

“Reliable, high-quality energy data doesn’t just improve decisions, it fundamentally changes how decisions are made altogether. Two distinct approaches become available that are simply not accessible when data is sparse or unreliable: CAPEX and OPEX.

In a CAPEX context, good data turns investment decisions from assumption-based to evidence-based. With continuous, validated, granular consumption data, baselines are no longer rough estimates but real profiles that reflect actual seasonal and operational behaviour. This reduces uncertainty, tightens financial risk bands, and can materially improve project viability under metrics like IRR. In EPC frameworks, this is critical: the baseline becomes a contractual asset that must withstand third-party verification, not just an internal model.

In an OPEX context, the value is operational rather than financial modelling. High-resolution time series data enables near real-time anomaly detection, identifying issues like unexpected baseloads, equipment drift, or leaks within days rather than months. This shifts maintenance from reactive to predictive and can significantly reduce energy waste, which is often driven by undetected faults.

Both approaches depend on the same foundation: continuous, trustworthy data without gaps, spikes, or hidden interpolation. If that integrity is missing, analysis becomes overly conservative and decision-making slows down or stalls.

At higher granularity – typically 15-minute intervals – energy data also becomes relevant beyond the asset itself. It enables participation in flexibility and ancillary service markets, where consumption can be actively shifted or reduced in response to grid signals. In that context, validated data is not just analytical infrastructure but a contractual requirement, making data quality a prerequisite for new revenue models in energy systems.”

You work across both the industrial and real estate sectors. Do the barriers to good data access differ between the two, and if so, how?

“The barriers are different, but the core misconception is often the same: many organisations treat data access as a software problem, when in reality it is an infrastructure and governance challenge.

In real estate, the biggest barriers are usually organisational. Much of the data could be collected, but ownership is fragmented across landlords, tenants, asset managers, and portfolio managers, each with different goals and reporting needs. This often leads to misaligned systems and incomplete data strategies. Crucially, good data infrastructure needs to be designed into a building from the start. Retrofitting it later is possible, but much more costly and rarely as effective.

In industry, the barriers are more technical. Energy use is deeply embedded in production processes, so meaningful measurement requires much more granular metering and a strong understanding of operations. Many facilities still lack sub-metering at machine or process level, making it difficult to link energy use to specific activities. On top of that, legacy systems, mixed technologies, and fragmented ownership across departments make data integration especially challenging.

So while real estate tends to struggle more with governance and incentives, industry tends to struggle more with technical complexity. But both need to solve the problem upstream, before any analytics platform can add real value.”

Where do you see the biggest progress on data access in the European market over the next five years?

“The biggest shift over the next five years will be the growing role of AI in energy data platforms, but probably not in the way the market currently imagines.

AI’s most immediate value will be in recognising patterns at scale: identifying anomalies, spotting equipment degradation, and uncovering links between operational variables and energy performance. In that sense, platforms will move from being descriptive (showing what happened) to becoming more prescriptive, helping users decide what to do next.

But AI will only be as good as the data it receives. Poor-quality or poorly understood data will simply lead to faster, more sophisticated mistakes. That makes data validation and critical interpretation even more important: users will need to understand not just the outputs, but also the quality and origin of the underlying data. At the same time, regulation is pushing the market toward better data quality and standardisation, which will help unlock AI’s potential across assets and geographies.

Ultimately, the key question is whether the sector develops enough data literacy to use AI critically. If it does, AI could become a major accelerator of the energy transition. If not, it risks becoming just another generation of impressive-looking dashboards.”