Overview
This article explains how to fill in and submit the data collection file for the Outsourced & On-Premise Datacenters (Cloud excluded) module. It covers the mandatory and optional data required, how to upload it via the Greenly template, and common formatting errors to avoid.
Key benefits / use cases
Accurately calculate the carbon footprint of your company's data centers (electricity, hardware, air conditioning, data transfer)
Ensure data completeness for a granular and auditable GHG analysis
Avoid common formatting errors that delay analysis
1. What data should be included in your data collection file?
The aim of this advanced module is to calculate the impact of your company's data centers during the studied financial year.
⚠️ Data provided must be in French 🇫🇷 or English 🇬🇧 to be processed by Greenly.
You can find the data collection file by clicking on the module > tab "2. Data collection">"Bulk import - via template".
The file contains two types of data:
Mandatory data – the minimum required to calculate CO₂e emissions. Without it, no emissions can be computed.
Optional data– additional data that increases the quality and granularity of the analysis.List of mandatory data:
Electricity data:
Site name - Name of your data center location.
Location - Country where the data center is located
Type - Outsourced or On-premise
Electricity consumption - Total consumption in kWh
Indicate if PUE is included in the electricity consumption
Equipment inventory:
Site name - Must match the site names in the electricity tab
Type of equipment - Server, Router, Switch, etc.
Quantity - Number of each equipment
List of optional data:Data transfer:
Site name - Must match the site names in the electricity tab
Region of data transfer
Type of data transfer (internet or between datacenters)
Volume (GB)
Air-conditioning:
Site name - Must match the site names in the electricity tab
Type of refrigerant
Quantity (kg) - Amount of refrigerant reloaded during the year
Equipment details:
Manufacturer - If unknown, use "- Unknown / other -"
Model - If unknown, use "Medium model"
Manufacturing date - Year when the equipment was manufactured
New or refurbished - Indicates if the equipment was purchased new or refurbished
Business Unit, Entity, Entity Country - For organizational classification
2. How to upload your data to the Greenly platform?
To upload your data, click on the corresponding task on the Progress page or go to Data > Data Collection > the Outsourced & On-Premise Datacenters (Cloud excluded) module 1/ It’s an advanced module so you will need to import your data through the Greenly template by clicking on "Bulk import". The template is downloaded in the same language as the platform. When using it, we ask you to fill the English one with English data and the French one with French data. You can always download the other language on the READ ME page of the template.
Import your data in the section “Upload file”. You can also provide additional files to prove or support the data you’re submitting—for traceability and auditability, in the section “File Information (optional)” 2/ Once your data uploaded, click on**“Submit for analysis”**. An analyst will review it and complete the module.
If you do not have the necessary information to complete this module, you can click on "Skip” at the top right of the page.
3. Frequent errors in data format
Site name: Inconsistent naming across the file. The location names should be consistent over all tabs.
Electricity consumption: Missing units or providing in MWh instead of kWh
PUE: Providing unrealistic values (typical range is 1.1-2.0)
Equipment types: Using non-standard equipment types that don't match the dropdown options
Manufacturer/Model: Using models that aren't in our database (if a model isn't in our database, select the closest one or use "Medium model" as fallback)
Manufacturing date: Using formats that aren't recognized as dates
Data transfer: Providing volumes in TB or MB instead of GB
Refrigerant type: Using vague or non-standard refrigerant names
Data completeness: Missing data across one or more tabs leading to incomplete analysis
4. Review your data and read your results
Once you have uploaded your data, you can flag the expenses related to the module to avoid double counting and access your results. To do so, please read the related article.
If you have any questions, feel free to reach out to [email protected]