Paul Roeland: My Data Values

Wikirate
3 min readFeb 1, 2023
Paul Roeland, Transparency Coordinator at Clean Clothes Campaign

Paul Roeland, Transparency Coordinator at Clean Clothes Campaign, talks to WikiRate about his views on and relationship with data.

1. How many years have you been working with data?

For a very long time.

I’ve worked at Clean Clothes Campaign for 12 years now, mostly on supply chain transparency. But before that, I used data in other settings as well. My background is in theoretical geophysics, during the late 1980’s I had my own access to a Cray 1 supercomputer to do calculations…

2. What kind of data do you work with?

Nowadays, mostly supply chain transparency data, as garment brands are disclosing more and more. We started the Transparency Pledge in 2016, when disclosing this kind of data was almost unheard of. Since then, we’ve made massive progress, where this data is often now available in a machine-readable, easily searchable format, for instance, at the Open Supply Hub.

We try to combine supply chain data with other data points, such as the garment workers’ real wages, to co-create tools like Fashion Checker.

In addition, I’m trying to keep up with new data developments like machine learning, satellite imagery and OSInt tools, to help analyze trends and find and disclose human rights violations.

3. How do you use data in your work today?

Data for us is never an endpoint; it is a way to tell stories better. In our lobby for stronger legislation to hold companies accountable and empower garment workers, our stories are more powerful when we have data to back those up. Companies are really good at green- and social washing; the best weapon against that is hard cold evidence.

4. Where do you get your data, any recommendations?

Wherever we can get our hands on it!

As said, we have campaigned for a long time to get more data released, and we hope that stronger reporting legislation will unlock even more data. Our challenge will be to analyze and verify this at scale — something that will only be possible if the people affected by that data have access to it. The people that can easily spot a fake social audit in a factory are the factory workers.

For unusual ways of getting data and cleaning up messy data, I can’t recommend the collection of tools at https://github.com/cipher387 highly enough. There are so many nuggets hidden there.

5. If you could wave a magic wand, what data would you wish for?

Full supply chain data beyond Tier 1, especially volume data, meaning the quantity of goods, all the way to the raw materials. A first step, for example, could be opening up the EU Import/Export registry — something that is needed to combat, for instance, state-driven forced labor. Or by delving into the data of logistics companies — anything that gets produced also gets moved, and somebody keeps a record of when, where, how much, and where to…

6. What’s your top tip when working with data and it gets frustrating?

Take a walk, take a nap. And chat with others, for instance, at various open data conferences — there are always amazing people to meet that can give new pointers.

7. Do you have a data project or resource you’d like to share with the world?

Two, actually. I really think the Open Supply Hub has made fantastic progress, and hopefully expands into new sectors. And, of course, WikiRate — I don’t think people realize how much power lies in combining data from different sources, all based on open access.

8. Lastly, what’s your favorite joke or quote about data?

“CSV is the data Kalashnikov: not pretty, but many wars have been fought with it and kids can use it.” (@pudo / @pudo@berlin.social, OKFest 2012)

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