Three spreadsheets and a PDF bill
A distribution warehouse wants a rooftop system. Their facilities manager sends half-hourly data: one spreadsheet from their current supplier, another from the previous supplier with a different layout, a month missing, and a PDF bill for the site's second meter. Your commercial designer spends two days turning it into a usable year of consumption before any design work starts.
Then another prospect sends data in yet another format.
Why consumption data is so messy
- Suppliers and data collectors export half-hourly data in different layouts.
- Clock changes, missing days and duplicated periods appear in most years of data.
- Sites have several meters, some half-hourly, some not.
- Some data is only available as bills, not interval data.
- Each designer cleans data their own way, so proposals are not consistent.
What the cleaning costs
Designer time on data rather than design. Slower proposals for commercial prospects, who often get several. Errors, such as a doubled day or a missed clock change, that affect how the system is sized. And proposals that are hard to compare with each other because the data was treated differently each time.
An import built for consumption data
- Files are uploaded by the designer or the prospect through a secure link.
- The import recognises common half-hourly layouts and maps them to one standard format; new layouts are added as they appear.
- Clock changes, gaps and duplicates are detected and listed, not silently fixed. The designer chooses how each is handled, and the choice is recorded.
- Several meters on one site are combined or kept separate as the designer chooses.
- Consistent profiles are produced: by month, by day type, by time of day, ready for the generation modelling your designers already use.
- Everything is stored against the opportunity in your CRM, with the notes, so the proposal can be explained later.
The import prepares data. Sizing the system and any figures in the proposal remain your designers' work.
Common data problems and how they are handled
| Problem | What the import does |
|---|---|
| Missing days | Flags them, designer chooses how to fill or leave |
| Duplicated periods | Flags and shows both |
| Clock changes | Detects the short and long days |
| Several meters | Combines or separates on request |
| Bills only, no interval data | Records monthly totals, flags the limitation |
Designers designing
Data is ready in hours rather than days. Every proposal starts from data treated the same way. Assumptions are recorded, so a proposal can be defended when the client's energy manager asks. And your designers spend their time on the part of the job only they can do.
Why recording the choices matters
Commercial clients often have an energy manager, a finance director or a consultant who will question the proposal. The first questions are usually about the data: which year was used, what happened to the missing month, whether the second meter was included. If those decisions were made in a spreadsheet that has since been overwritten, the designer has to reconstruct them.
Recording each choice alongside the cleaned data means the answer is on file. It also helps when the same client comes back a year later for a second site or an extension, because the method can be repeated rather than reinvented.
And where two designers work on similar proposals, recorded choices make their approaches consistent, which matters when the client compares your proposals across their estate.
Is this your commercial workflow?
- Designers spend days cleaning consumption data.
- Every prospect sends data in a different format.
- You have found errors in data after a proposal went out.
- Data handling differs between designers.