Executive Summary:
Most multi-location organizations do not lack energy data. The bigger problem is that the data is fragmented across systems, structured differently by different teams, and difficult to compare across buildings. As a result, organizations often:
This whitepaper outlines a practical way to fix that problem.
It shows how to:
The goal is simple: move from fragmented data to decisions that are clear, explainable, and aligned across teams, especially during critical budgeting and contract renewal cycles.
Key Takeaways:
With more than 550,000 commercial and institutional buildings in use across Canada today, the issue is not a lack of energy data. For most multi-location organizations, the problem is that the data is fragmented, the portfolio is complex, and different teams often work from different structures.
For instance, energy information is spread across bills, supplier portals, spreadsheets, meters, finance systems, and emissions reports. Buildings vary by size, use type, operating pattern, occupancy, geography, and rate structure. At the same time, finance, operations, sustainability, and procurement often organize the data differently because they use it for different purposes.
The result is not simply low visibility. It is often misleading visibility. Data that appears complete but does not support fair, consistent comparisons. As a result, teams often cannot confidently answer basic questions:
Energy is one of the largest operating expenses in buildings
Energy can account for up to 30% of total operating costs in a typical office building.
When energy data does not support fair, consistent comparison across buildings, organizations can end up focusing on the wrong sites, misreading the cause of cost increases, and losing confidence in the numbers.
Here are five signs energy data may be leading teams to the wrong conclusions.
A building can appear to be a top problem site in one report and look normal in the next because the ranking method changed. One report may rank by total energy cost, another by electricity use, another by combined electricity and gas, and another by emissions.
Unless the same metrics and calculation rules are applied consistently, the ranking will not show which buildings are actually driving the most cost, energy use, or emissions.
Finance and operations often report different energy numbers because they are working from different time periods and different data sources. Finance may report total billed costs by invoice or accounting period, while operations may report energy use by service period, meter reads, or site-level data. Without a common reporting structure, the numbers will not align.
Energy costs can increase for several different reasons, including higher usage, higher commodity prices, demand charges, delivery charges, carbon-related charges, or contract changes. When reporting combines those cost drivers into a single total, the cause of the increase is hard to determine.
Building performance cannot be compared reliably using raw totals alone. Two buildings may appear comparable based on size, but differences in occupancy, operating hours, or building use can materially affect energy cost and consumption. Without intensity metrics and appropriate peer grouping, rankings do not show which buildings are actually underperforming.
Emissions reporting often depends on energy data that comes in different units, reporting periods, and fuel types across the portfolio. When teams have to keep correcting spreadsheets, converting units, or applying different emissions factors by province, it is a sign the underlying data is not yet structured consistently enough for reliable reporting.
Raw totals are unadjusted building figures such as total annual energy cost, total electricity use, total gas use, or total emissions. While these numbers are easy to report, they are not enough to compare building performance reliably across a portfolio.
Larger buildings, longer operating hours, higher occupancy, and different building uses all affect total cost and total consumption. A site can rank high on total spend simply because it is larger or more heavily used, not because it is performing poorly.
That is why portfolio analysis needs a comparable baseline before building performance can be assessed with confidence.
A comparable baseline is a standardized portfolio dataset that allows buildings to be measured consistently across sites and over time.
In practice, that means structuring building, account, meter, and billing data using the same fields, units, and calculation rules. For most organizations, the baseline starts with 12 to 24 months of electricity and natural gas data across every building, account, and meter.
Most Canadian buildings are not being benchmarked
Only about 42,000 of Canada’s roughly 550,000 commercial and institutional buildings are tracked in ENERGY STAR Portfolio Manager, which means only about 7.6% are being benchmarked.
Four things need to be in place for the baseline to work.
Cost data should also be broken down by charge type — separating commodity charges from delivery, demand, fixed, tax and regulatory charges — so that cost changes can later be traced to their actual cause rather than reported as a single unexplained total.
Without that structure, two buildings may appear comparable while still being measured on different assumptions.
Example: A comparable portfolio baseline
Once built, the baseline should allow teams to answer a small set of practical questions:
Once a comparable baseline is in place, buildings can be assessed more accurately. Here are two examples using two different ways to compare: against similar buildings, and against their own normalized historical performance.
Consider three office buildings in Alberta:
On raw totals, Building C appears to be the main problem.
But Building C should not be compared directly with A and B. Its longer operating hours and different occupancy pattern make it a different type of site.
A more useful comparison is between Buildings A and B:
Building B is the more expensive building to operate relative to its size, even though Building C has the highest total cost. Without appropriate peer grouping, expected differences in building use can be mistaken for underperformance.
Why peer grouping matters
Energy intensity varies significantly by building type. In Canada, restaurants average 2.60 GJ/m², compared with 1.05 GJ/m² for office space and 0.91 GJ/m² for schools. That is why buildings should be compared against similar peers rather than ranked on raw totals alone.
Now consider one Ontario office building over two years:
At first glance, the increase appears small: 20,000 kWh, or about 1.2% year over year.
But after normalizing for a milder winter and lower occupancy in 2025, the building’s expected electricity use should have fallen to 1,600,000 kWh. Instead, it came in at 1,700,000 kWh.
That means the building used 100,000 kWh more than expected, or about 8.3 kWh/m² above its normalized baseline.
The raw year-over-year increase looks minor. Compared with its own normalized history, it is clearly moving in the wrong direction.
Once buildings have been compared on a consistent basis, the next step is to decide where to look first. A practical starting point is to flag the worst-performing 10% to 25% of sites based on normalized cost, usage, or emissions metrics.
These sites should be treated as a shortlist for investigation, not as proof that a building is actually underperforming.
| Pattern to Flag | Why it Warrants Investigation |
|---|---|
| High cost intensity (e.g., $/m²) | The site is expensive relative to its size or peer group |
| High usage intensity (e.g., ekWh/m²) | The site uses more energy than comparable buildings |
| Rising trend versus stable peers | Performance is worsening relative to similar sites |
| High emissions intensity | The site is driving disproportionate emissions |
| Unusual variance from prior baseline | Performance has shifted materially from expected levels |
This shortlist can then guide where teams spend time first, whether that means a site review, an audit, a controls check, a procurement review, or deeper analysis.
A strong portfolio process does not treat every building equally. It helps organizations direct limited time and budget to the sites where further investigation is most likely to have the greatest effect.
A flagged site does not always have an operational problem, and acting on the wrong diagnosis wastes time and money. A building’s total energy cost can rise for reasons that have nothing to do with how the building is operating: a commodity price increase, a change in rate class, a new regulatory charge, or a billing statement adjustment can all show up as a cost increase with no changes in consumption whatsoever.
Consider a straightforward example: a building’s cost rose 12% year over year, but consumption held flat. That is not an equipment story or an occupancy story. It is a tariff, contract, or billing story, and it belongs with procurement or finance, not facilities.
The reserve is equally important. A building where consumption is rising but unit costs have fallen may look fine on a total-cost basis while quietly becoming less efficient. Without separating the two, that signal gets lost.
That is why cost and consumption should be reviewed separately. Once a site has been flagged, the next step is to determine whether the issue is operational, commercial, tariff-related, or billing-related before deciding who needs to act and what they should do.
| Potential Cost Driver | What It May Indicate |
|---|---|
| Higher consumption | A potential operational or equipment issue |
| Higher commodity prices | A market-driven cost increase rather than site inefficiency |
| Higher demand charges | A peak load or load profile issue |
| Higher fixed or delivery charges | A utility rate or billing change |
| Regulatory or carbon-related charges | A policy-driven cost increase |
| Contract timing or rate class changes | A commercial or procurement issue |
This distinction matters because the response should match the cause. A usage issue may call for an operational review. A tariff, billing, or contract issue may require procurement, finance, or utility review instead.
Cost, consumption, and emissions need to be reviewed together from the same underlying standardized dataset. Otherwise, teams can end up with conflicting conclusions about which sites are underperforming, why costs changed, and where to act first.
When cost, consumption, and emissions live in separate reports, teams reach different conclusions and act on the wrong one.
| What Jotson Supports | Why It Matters |
|---|---|
| ✓ Standardized building, meter, and billing data | Creates a comparable baseline across the portfolio |
| ✓ Consistent cost, usage, and emissions metrics | Supports like-for-like comparison across buildings |
| ✓ Cost breakdown by charge type | Helps isolate pricing, tariff, and billing effects |
| ✓ Historical trend and baseline analysis | Helps explain whether performance is improving or worsening |
| ✓ Cross-site benchmarking | Helps focus attention on the highest-priority sites |
| ✓ Anomaly detection and variance analysis | Helps surface issues that need deeper review |
| ✓ Shared reporting across teams | Reduces conflicting conclusions across operations, finance, procurement, and ESG |
Jotson brings bills, contracts, usage, and emissions into one portfolio-level view so organizations can standardize data, compare buildings on a consistent basis, identify high-priority sites, and investigate whether the issue is operational, pricing-related, or billing-related.
A practical way to move from fragmented utility data to better portfolio decisions is:
Collect → Structure → Normalize → Compare → Focus → Explain
This process gives organizations a repeatable way to move from fragmented utility data to more confident portfolio decisions.
When portfolio energy data is not comparable, organizations can end up investigating the wrong sites, misattributing cost increases, and making slower or weaker decisions about budgeting, retrofits, procurement, and emissions reporting.
That does not just create reporting friction. It increases the risk of missed savings, poorly directed capital, and unresolved performance issues across the portfolio.
A more structured approach makes it easier to see where the real problems are, explain what is driving them, and act with confidence.
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