1. Align with the client’s specific requests
👉 Clients might have specific requests for data processing → they will have an impact on which module format you should opt for.
🎯 Examples of common requests
Suppliers or other custom EFs (outside of Greenly’s database):
The client must have paid for their integration, and the EFs should have been validated by the R& M team
These EFs must be added to the EF Manager as company-specific EFs before you can retrieve their IDs
Example 2:
Some clients may need specific outputs or formats for their datacenter analysis
They might need to match specific regulatory frameworks
2. Choose the module that best fits your needs
🈵 Manual processing (Advanced module)
Ideal for most datacenter assessments where detailed equipment information is available
Can handle multiple data categories: electricity consumption, equipment amortization, data transfer, and refrigerants
Use this option when clients have detailed information about their servers, network equipment, and datacenter infrastructure
This is the most commonly used module for datacenter analysis
Note: Electricity consumption and equipment might already be included in the Buildings and IT Inventory module for on-premises datacenters.
3. Data Processing
The goal of an activity-based study is to categorize a client’s data, meaning associating each activity of a specific category to the most suited emission factor.
⚠️ if you are working on a multi-year account, don’t forget to check the Rebaselining section.
🈵 Manual processing (Advanced module)
💡 When you do an analysis for a company, you should first check if a similar analysis has been done for the previous years, and check with the deal owner/client if a rebaselining of the data has been planned.
The Google Sheet to do the analysis can be found here.
Steps to follow:
Save the data collection file as well as the analysis file as Gsheets.
First, you'll need to duplicate the data analysis file in your client's folder. You'll mostly work in the "Input data" tab.
Import client's data in the model
Fill the red cells in column C: Company name, year of the study, and link to the data collection file. Then, click on the yellow cells and give access to the connected sheets.
While going over the client data, check electricity consumption to be a reasonable amount for a Datacenter. kWh consumption should not be in single digits, for example.
Also check if the datacenter consumption has already been included in the buildings module.
Make sure you saved the data collection files as a Gsheet and not as a.xslx (otherwise you’ll not be able to connect your sheets)
Methodology tip
Tick the cell “K16” if you’re working under the GHG Protocol methodology
Make sure your client didn’t change the name of the tabs or the columns order in the data collection template
Make also sure that the names of the sites are matching in every tab (some clients can use an abbreviation in one tab and the full name in another). Don’t hesitate to names the data in the collection template if needed
This will import General information, electricity consumption, data transfer and refrigerant data in the tab “Data collection”.
At this point, most of your input data tab must already be filled automatically.
Check that there is no error in the different tables, up to the Equipments amortization part (row 158).
Equipments amortization
The equipment amortization part needs to be filled “manually”, by copying and pasting “site name”, “type of equipment”, “manufacturer”, “model”, “quantity”, “manufacturing date” and “new or refurbished” columns.
Use cmd+maj+v to past your data without changing the format of the cells
Here is the list of potential errors you’ll need to correct:
Type of equipment, manufacturer and model: Make sure the data matches what is available in the dropdowns. If a model isn’t in our database, select the closest one (example: R740 instead of R730). As a fallback, select the “Medium model” option.
If manufacturer data is completely unavailable but the client wants to keep this section in the datacenter module: Set all manufacturers to "Unknown" and all models to "Median Model", leaving country and new/refurbished blank. This is an acceptable estimation approach, but should be confirmed with the PM/deal owner before proceeding.
Use the search and replace function on each column to replace multiple names at once
Manufacturing date: Make sure the format used are recognized as dates
Equipments amortization (NEW)
A script has been added to a copy of the template to automate this process.
Authorize the Script: A new menu named**"Utility"** will appear on your toolbar.
Click Utility>Clean & Validate Data.
A pop-up will appear asking for "Authorization Required". This is normal.
Follow the prompts: Click Continue, choose your Google account, click**"Advanced"(if it appears), and then click"Go to Datacenter Validation"**.
Finally, click "Allow" to give the script permission to run.
If everything works well, the results table on the right will look like this:
Once everything looks coherent, you can export your data 👇
Data Transfer (optional)
Data route region is required to proceed with the calculation, if is unknown: You can assume all data transfer occurs within the same country as the datacenter sites (e.g. if all sites are in the UK, set all data route regions to "United Kingdom").
But as always, please confirm this assumption with the PM/deal owner before proceeding.
Data export
Now is time to import data on the platform.
Go to the tab “To Advanced Module”
You’ll find a table generated that you can download as a csv, and upload on admeenly in the advanced module “Datacenter on-premise & outsourced (cloud excluded)”.
This tab is able to handle different methodologies (GHG Protocol and Bilan carbone), and is using emission factor IDs when possible, or custom EF when we need to match the right reglementary entry or take into account refurbished equipment.
4. Review the data
Once data has been uploaded, there’s the “2. Review” section. There, you need to:
Check for any inconsistencies in site names across different tabs
Check that all equipment, datacenters etc. have an EF.
Usually, electricity and equipment impacts are more or less equal. Of course it can vary if the client is keeping their hardware a long time, of if they have a specific activity using a lot of data transfer.