Metering & Data Assessments That Make Your Data Worth Trusting
Engineering-led audits of your metering, data hierarchy and KPIs for industrial sites that have plenty of data but cannot yet trust it enough to make decisions about cost, risk or capital.
- Answers one question: can you trust your data enough to act on it
- Tests the whole data structure, not just individual meters or dashboards
- Validates metered data against what is actually happening on site
- Rebuilds your metering hierarchy and KPIs around real drivers
- Part of SHV Energy
- ISO 50001
What This Service Is
A Metering and Data Assessment focuses on something most industrial sites already have: data, but rarely trust fully. By the time this service is used, the site typically has an Energy Management System, utility meters, dashboards and reports. Data is being collected continuously and KPIs are often defined. There is usually no shortage of information.
The problem is that this information does not reliably support decision-making. The assessment is designed to answer a simple but critical question: can the site trust its data enough to make decisions that involve cost, risk or capital investment? In many cases the honest answer is no, or at least not with confidence. This service is what closes that gap, turning a data environment that generates reports into one that supports decisions.
The Challenge It Solves
Clients usually come to this service when something does not add up: inconsistencies between data sources, energy totals that do not reconcile, KPIs that fluctuate without clear explanation, or system-level data that contradicts what engineers observe on the ground. In other cases the issue is subtler. The data may look clean and the reports well structured, but when it comes to a real decision there is hesitation, because the team cannot fully explain what is driving performance.
The common underlying issue is that metering has evolved over time without a clear structure. New meters are added as systems change, but without a consistent hierarchy or clear alignment to how the site wants to manage energy. That leaves gaps where key systems are not properly captured, overlapping or inconsistent measurements, and KPIs calculated from incomplete or inaccurate data, which makes it hard to link energy use to production, weather or operational drivers.
- Energy totals that do not reconcile across different data sources
- KPIs that fluctuate without a clear, explainable cause
- Metering that grew ad hoc, with gaps, overlaps and no clear hierarchy
- No confident answer to what is really driving energy consumption
How EM3 Delivers It
Map how you measure energy today
We step back from individual meters and dashboards to understand the whole data system: what meters exist, what they measure, how data is collected and stored, and how it is used in reporting and KPIs. The entire structure is treated as something to be tested, not assumed correct.
Examine the data hierarchy
We look at how meters relate to each other and how they roll up into site-level reporting. This is where inconsistencies become visible: systems that should be fully metered captured only in part, and totals that do not reconcile across the levels of the hierarchy.
Check data quality and integrity
We test whether signals are reliable, whether gaps or anomalies exist in the time-series data, and whether the measurement resolution is sufficient to support real analysis.
Link the data back to reality
Wherever possible we compare metered data against observed system behaviour, operating conditions and known drivers such as production levels or weather, because data can appear internally consistent while still failing to reflect what is actually happening in the plant.
Review the EnPIs
We review whether your Energy Performance Indicators reflect the right variables, are structured correctly, and actually explain changes in performance. Often the issue is not the absence of KPIs, but that they do not correspond to the underlying drivers of energy use.
Redesign the framework
We deliver a redefined metering structure, a clean data hierarchy and a reconstructed set of EnPIs aligned to the variables that really drive energy use, with recommendations for measurement and verification so future projects can be tracked accurately.
What You Receive
A clear view of the current state
What is being measured, where the gaps are, and where inconsistencies or data-quality issues are present, tested rather than assumed.
A redefined metering structure
How energy should be measured across the site to properly capture the major systems and flows, identifying missing meters, redundant meters and points where measurement needs to improve.
A clean data hierarchy
A structure where system-level data rolls up correctly to site-level totals and the relationships between systems are properly represented.
Reconstructed EnPIs
Energy Performance Indicators aligned to the variables that actually drive energy use, production, operating hours or environmental conditions, rather than arbitrary or legacy metrics.
KPIs that explain, not just describe
A set of indicators that explain performance, let you track real improvement over time, and can be used to validate projects and operational changes.
M&V recommendations
Where relevant, recommendations for measurement and verification so future projects can be tracked accurately once implemented.
Proven Outcome
A recurring pattern appears across assessments. Sites often believe they are well-metered because a large number of meters are in place, but analysed structurally, key systems turn out to be only partially captured and totals do not reconcile across the hierarchy. Energy attributed to specific systems does not match overall site consumption, KPIs are influenced by variables that are not properly accounted for, and improvements appear in reporting but cannot be linked to real changes on site.
By restructuring the metering approach and redefining the KPIs, these sites were able to clearly identify what was driving energy use, track performance accurately over time, and validate the impact of implemented projects. It often changes how decisions are made, because the conversation shifts from what the report says to what is actually happening.
Why EM3
Validated against reality
Rather than treating data as inherently reliable, we validate it against physical reality. This avoids the common trap where sophisticated dashboards mask underlying measurement issues.
Connected to engineering
Metering and KPIs are not reviewed in isolation. They are evaluated in the context of how your systems actually operate, so problems are understood in terms of what they represent physically on site.
Designed for use
The objective is not better reporting for its own sake, but data that supports real decisions: prioritising projects, validating savings and understanding performance trends.
Independent of platforms
With no ties to a particular meter or software vendor, the redesign is built around how you need to manage energy, not around a product.
How We Engage
Frequently Asked Questions
We already have an EMS and dashboards. Why do we need this?
Having data is not the same as being able to trust it. This assessment tests whether your metering, data hierarchy and KPIs actually support decisions that involve cost, risk and capital, rather than just generating reports.
What does the assessment actually test?
The whole data structure: what meters exist, how data rolls up the hierarchy, data quality and integrity, and whether your Energy Performance Indicators reflect the real drivers of energy use.
How do you know whether our data is trustworthy?
We validate metered data against physical reality, observed system behaviour, operating conditions and known drivers such as production and weather, because data can look internally consistent while not reflecting what is actually happening in the plant.
What is wrong with our KPIs?
Often nothing is missing. The issue is that the KPIs do not correspond to the underlying drivers of energy use, so they describe performance without explaining it. We rebuild them around the variables that actually drive consumption.
Do we get new hardware recommendations?
Where metering gaps prevent proper measurement we identify them, missing, redundant or improvable meters, as part of a redefined metering structure. We are independent of any meter or software vendor, so the recommendation is driven by how you need to manage energy.
How long does it take?
It is a focused analytical engagement, typically a few weeks, depending on site complexity and the state of your existing data systems, and usually shorter than a full audit.
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