See frequently asked questions and answers about the integration of statistical footprint data from partners with Ducky Footprints
For a general intro to the terminology used by Ducky, see the glossary, questions and answers about footprints.
What's a Data Partner?
A Data Partner provides Ducky with statistical footprint data from sources like energy producers, grocery chains and banks. Being a Data Partner is a long-term commitment which can give great benefits. See some examples of value here, or head to this guide to get started as a Data Partner with Ducky Footprints.
What are some examples of Data Partners?
Here are some examples of partners and the value they see in providing footprint data to Ducky Footprints:
- Some wish to develop green business opportunities but don't know where to start. Becoming a Data Partner is a very efficient process for examining the quality and utility of footprint data sets and discover use cases.
- Some are already Ducky Data API-customers and see that their contribution will help improve the accuracy and utility of the very API endpoints they're using
- Some seek to donate statistical data in a region where they have a strong presence, to help local authorities with climate plans and foster public-private collaboration
Why is Ducky seeking Data Partners?
External data improves Ducky' footprint calculations and gives us more insight into what drives variations in a country's footprint per region. This subsequently improves our estimations of the footprint of households, which is the basis for the footprint map in Ducky Footprints, the APIs delivered by Ducky Insights and the climate activities in Ducky Challenge.
What's an example of useful data?
The most useful data shows clearly how people behave over time, for example the aggregated transactions of bank customers. When such data is made available for Ducky, the calculations of footprints (in this case particularly for the category Goods and services) are updated more often and become much more accurate and informative. This provides municipalities with insights into where sustainable purchasing activity could be encouraged and what is the actual effect of local initiatives. It also provides Ducky Data APIs with an improved baseline calculation and comparisons of user footprints.
How detailed would the data need to be?
The best for Ducky, in the example of bank transactions, is to get total amount spent on each category aggregated to a neighborhood (basic statistical unit) level. This typically means some dozen or hundreds of people. Individual data is not stored by Ducky, though it helps to know how many individuals were included in the aggregation.
How is my data treated securely?
All partner data is handled securely in Tietoevry data lakes and only reaches Ducky in aggregated form. We have internal documents which outline how the data pipeline works for different cases and sectors. We need to get a sample of your data before we could describe exactly how the data would be integrated.
For a general outline of data flows to and from our partners, please see this article on Ducky's system architecture.