Elianne Cornfield, Data Coordinator at Clean Clothes Campaign, talks to WikiRate about her views on and relationship with data.
1. How many years have you been working with data?
It has only been about two years since I started as Data Coordinator at Clean Clothes Campaign (CCC). Having a background in both the social and data sciences, this data position — with its strong connections to CCC’s advocacy and human rights work — has been a great place to start my career.
2. What kind of data do you work with?
Human rights data, more specifically, data on human rights violations in the garment industry. CCC supports garment workers when their rights are being infringed upon in what we call an urgent appeal case. In these cases, we pressure companies and governments to take positive action and end worker rights violations. Examples of such violations include union busting, not paying wages, and gender-based violence. Information about these cases goes into our urgent appeal database, which I manage.
I also work with other data in our organization. With CCC being a global network of over 235 organizations, we have lots of contact information to manage and various projects we are working on. About two years ago, we also started our PMEL (planning, monitoring, evaluation, and learning) process. I work with the data generated during this process too.
3. How do you use data in your work today?
The urgent appeal information feeds into all areas of our work. For instance, CCC uses it as leverage in our campaigns, lobby and advocacy work, and urgent appeal cases. Anyone in our Network can request information from the database, which a colleague and I provide. Besides performing data queries and analysis, I am also involved in developing and implementing our Network’s data strategy. This, among others, entails decisions on the kind of data we collect, how we structure it, how we improve our data entry processes, and what data access levels we have.
4. Where do you get your data, any recommendations?
Most of the data I work with is generated by our Network. In terms of recommendations, wherever you get your data from, check the source’s reliability and be transparent in case of uncertainties.
5. If you could wave a magic wand, what data would you wish for?
The list is long, but as Liv Simpliciano said in her interview: ‘Everything required to hold major brands and retailers accountable.’ Let’s start with (machine-readable) supplier lists of companies, including the number of workers in the factory, wages, gender ratio, etc., also going beyond Tier 1.
More selfishly, I’d also wish for our internal databases to be filled without people having to do the admin. Then, my colleagues would be able to have their complete focus on the content work while having complete and accurate data to learn from. One can dream…
6. What’s your top tip when working with data and it gets frustrating?
Take a break. Chat with a colleague and/or friend. They may suggest another approach; if not, sharing your experiences still helps.
7. Do you have a data project or resource you’d like to share with the world?
Fashion Checker to look up the transparency levels of- and wages paid by your favorite fashion brands (a tool that would not be possible without WikiRate).
Open Supply Hub to explore and/or publish global supply chain data in various sectors.
I am sure my colleague Paul Roeland will also highlight these tools in his interview.
8. Lastly, what’s your favorite joke or quote about data?
‘Garbage in, garbage out.’
