We do not recommend grouping data because that would limit the level of granularity.
Our recommendation is to not do any data transformation prior to uploading the data, beyond that which we recommend in this article [link article about what is expected].
The reason why we do not recommend grouping data by supplier is because it limits the level of granularity (and therein the accuracy) of the emissions results.
You might be asking how?
You are likely to be purchasing more than one unique product or service from the same supplier. Emitwise offers customers the ability to calculate emissions at the activity level (i.e. each unique product or service would be assigned an emissions factor). If you were to group your purchase data by supplier, for example, then you risk losing granularity on what you actually bought from that supplier; and the emissions for each unit of currency spent with that supplier would result in the same emissions.
This is a unique offering of Emitwise, and is one example of how we leverage machine learning in our technology (i.e. to be able to classify millions of unique products or services to a product, and then assign a product-level emission factor as opposed to a supplier-level emission factor for a group of products or services).
In conclusion, it is recommended to give us your purchase data with as much granularity as possible (i.e. product or service bought, supplier you bought it from, location of the purchase, location of the supplier — where available). This will results in the least uncertainty in your emissions results and you would benefit from more specific hotspot analysis and in doing so, identify more specific opportunities to reduce your emissions.