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Transparency is the first step to understanding how companies impact people and the planet.
The information that results from it, forms the basis on which informed decisions can be made to change things for the better. But transparency is not only about what information is made available. It is also about how the information is made available.
What is open data?
Simply put, 'data' refers to bits of information. What makes data ‘open’ is the unrestricted potential for (re)use.
As explained in the Open Knowledge Foundation’s Open Data Handbook, in practice, openness translates into two dimensions:
licensing that allows re-use, modification and sharing (for free or at most, no more than a reasonable reproduction cost) by everyone.Note: Wikirate's content is licensed under CC BY 4.0 and Wikirate's database infrastructure is licensed under CC BY-SA 4.0
making the data available and discoverable as a whole, in bulk, and in a machine-readable format.Note: Wikirate’s content can be extracted for free, in bulk, by export or API
Why should company data be open data?
To address global challenges, we need to mobilize companies at scale and transform industries as a whole. That means we need to look beyond the top 10, 100 or even 1000 companies.
It is estimated that there are more than 300 million companies in the world.
Multiply that by the number of measurements that are used to understand the impacts of companies and it is blatantly obvious that it is impossible for humans to process that amount of information.
To understand company impacts, we need the help of computers to parse all of that information and for that, open data is the key.
Collective problems require collective action
Datasets need to be shared and connected for us to have a real shot at tackling these issues.
This is where interoperability comes in - the technical ability to connect and integrate different data systems, and agreed data standards that enable sharing.
Open data is the foundation that makes interoperability possible. Without it, resources are inefficiently spent on duplicative efforts, data life cycles are short, impact is capped, and we will continue to run after the facts.