A facility manager's guide to utility data
Energy is the largest controllable operating cost in most buildings. This guide shows facility managers how to turn scattered utility data into decisions that cut it.
For most facility managers, utility data arrives as a pile: PDF bills in an inbox, meter exports in a shared drive, a spreadsheet someone started and left. It gets paid, filed and forgotten. That is a missed opportunity, because energy is usually the single largest cost a facility manager can actually influence, and the data needed to influence it is already sitting there unused.
This guide is about turning that pile into decisions: what to collect, what to check, what to watch, and how to stop the whole thing from living in a spreadsheet that breaks the moment someone is on vacation.
Why utility data is worth a facility manager's time
Energy is one of the biggest levers a facility manager holds. In office buildings, energy is the single largest controllable operating expense, and can represent roughly a third of operating costs. It is also leaky: buildings commonly waste up to 30 percent of the energy they use through inefficiency. For scale, the whole picture is large, since residential and commercial buildings together account for roughly 40 percent of energy use in the United States.
The payoff from managing it well is measurable. Buildings that earn ENERGY STAR certification use about 35 percent less energy than typical peers. None of that is reachable without knowing, per site and per month, what you are actually using and paying.
What to collect, and in what shape
Good facility energy management starts with a complete, consistent record. That means every account and every fuel, not just electricity, captured monthly and reduced to comparable fields.
- Every utility account and meter across the portfolio, electricity, gas, water and steam
- Consumption and cost per billing period, with the exact start and end dates
- Peak demand and any demand-based charges for accounts that carry them
- Rate class and tariff, so charges can be checked against the schedule in force
- Interval or meter data where available, for load-shape analysis
The hard part is consistency. Bill layouts differ by provider and change over time, billing periods rarely line up to calendar months, and estimated reads distort any month they touch. Until those are resolved, comparisons across sites are unreliable, and unreliable comparisons send budgets to the wrong building.
Gas measured in cubic meters, electricity in kilowatt-hours, steam in pounds: comparing sites means converting everything to a common energy basis first. A single unit mismatch can make an efficient building look like your worst performer.
What to check on every bill
Once data is structured, the same checks an auditor performs by hand can run on every bill, every month. The point is not to distrust the utility, but to catch the ordinary drift that goes unnoticed when nobody has time to read each line.
- Does billed usage reconcile with the meter reads and the number of days?
- Do the demand and energy charges match the tariff actually in force?
- Is the account on the correct rate class for how the building operates?
- Are there estimated reads that never trued up, or duplicate charges?
- Does this month sit within the building's own historical range?
What to watch over time
A single month is a snapshot. The value compounds when you track the same metrics across seasons and years, because that is where creeping waste and quiet errors reveal themselves.
Track energy use intensity to compare buildings of different sizes fairly, and normalize for weather so a cold winter does not read as a management failure. Watch load factor for signs that peaks are outrunning usage, and watch baseload for equipment running when it should not. Each trend points at a specific action rather than a vague sense that the bill went up.
Getting off the spreadsheet
Most facility teams manage all of this in a spreadsheet, and it works until it does not. The person who built it moves on, a formula silently breaks, a month gets skipped, and confidence in the numbers quietly erodes. Manual collection is also expensive in its own right: benchmarking and data entry for even a modest portfolio consumes hundreds of staff hours a year that could go to analysis instead.
The alternative is to automate the collection and validation, and keep people for the judgment calls. MartinAI reads every bill and meter file, reconciles usage against reads and tariffs, converts everything to comparable units, and keeps one current record per site. Your team stops re-keying and starts acting on exceptions and trends. For a deeper look at what to require from a system that does this, see our guide on energy data management requirements.
Frequently asked questions
Why should a facility manager care about utility data?
Because energy is usually the single largest controllable operating expense in a building, often around a third of operating costs, and buildings commonly waste up to 30 percent of the energy they use. Utility data is the only way to see where that waste is and to prove that a fix worked.
What utility data should a facility manager collect?
Every account and fuel type, not just electricity, captured monthly. For each, record consumption and cost with exact billing dates, peak demand where it applies, the rate class and tariff, and interval or meter data where available. Consistency across sites matters as much as completeness.
What is the problem with managing energy data in a spreadsheet?
Spreadsheets work until the person who built one leaves, a formula breaks, or a month gets skipped, and confidence in the numbers erodes without anyone noticing. Manual collection also consumes hundreds of staff hours a year that could go to analysis. Automating collection and validation keeps people for the judgment calls.
How do I compare energy use across buildings of different sizes?
Convert every fuel to a common energy basis, then divide by floor area to get energy use intensity, and normalize for weather so climate differences do not distort the comparison. Without those steps, unit mismatches and weather swings can make an efficient building look like your worst performer.
What an energy data management system needs
A practical requirements list for an energy data management system (EDMS): ingest every format, validate and clean, one data model, cost allocation, exports and audit trails.
Energy benchmarks compared: CBECS, ENERGY STAR, BOMA
CBECS, ENERGY STAR and BOMA BEST answer different questions. Here is what each benchmark measures, how the scores are built, and how to use them together.