Your mission matters: A sustainable future with citizen data science at Dow

Transcript#

This transcript was generated automatically and may contain errors.

How do we keep food fresh? How can we reduce water usage in our daily lives? How do we make trash valuable? How do we innovate to make the world a better place? That is what we do.

So at Dow, we have this graph that measures our net carbon emissions of our entire product portfolio. We've made very ambitious targets related to our carbon neutrality, where by 2050, we want to reach complete carbon neutrality. And with this graph, we can see the various projects, investments, and opportunities we have to reduce our carbon footprint.

And what's also very special about that set of goals is that it's not purely aspirational. We know we have understood the technologies required. We have projects in place to account for pretty much 95% of that CO2 mitigation. That remaining 5%, it's going to require a lot of creativity and ingenuity from all different functions across Dow. It's going to be up to us to figure out how to get there.

Dow's mission and culture of innovation

I work at Dow because I love Dow's mission. We touch Dow products all the time. They're in food packaging, mattresses, car parts, adhesives. And I like to do science to make people's lives better. We're also very ambitious because we don't want to just deliver these solutions. We want to do it in a sustainable way. And so all of that involves great technical and technology gaps.

We have a very capable and innovative bunch of researchers that are able to get a lot done with just about any set of tooling that you throw out of them. However, the Posit tools, what they've enabled is a extremely low barrier to entry that's allowed people who have an idea to go test that idea in a matter of minutes and to be able to share the work product of exploring a concept, a Friday afternoon activity, and go and share that with some of their colleagues with relative ease.

However, the Posit tools, what they've enabled is a extremely low barrier to entry that's allowed people who have an idea to go test that idea in a matter of minutes and to be able to share the work product of exploring a concept, a Friday afternoon activity, and go and share that with some of their colleagues with relative ease.

So I think as scientists, we're very driven towards questioning, questioning the sense of making sure that we're doing the right thing or that we should be pursuing the thing that we want to pursue. It's our critical thinking superpower, and I think it's healthy. Once we started not only talking about the vision of what these tools could do for us, or started building prototypes, a lot of light bulbs started going off in people's head. Once you have those initial success stories, and then all of these exciting ideas were on the table, the conversation was not anymore, why should we do this is how do we do it better? How do we do it more intentionally?

But there is a moment when it all converges, and it clicks, and it starts flowing. And now you have this rhythm.

Growing the citizen data science program

Since 2019, we've grown from this handful of researchers to over 300 people now have access to Posit Workbench internally, and even more are using apps that are deployed on Posit Connect. That growth has been, in part, through our investment in Posit Academy, where we're teaching people how to code using an example and a project that's tailored to the work that we expect that they'll use these tools for. But also, we've been very intentional about trying to grow this community. We also have a citizen data science program. We have more and more people asking us for these capabilities, asking us for these tools that they can go use to get more value out of their data, to improve their own productivity.

Seeing people who so often may hesitate to put on that I'm a data scientist hat or label, but seeing what they're able to create gets me really excited. The goal is really to make sure that every person in the organization has some level of fluency so they can understand and respect data generated the best possible way. When they are showing someone data, they will show with the highest standards of science and statistics, and that they document properly all the metadata around what they generated so in the future, people can use them for advanced modeling the best possible way.

That connection between research and development, between science and doing good, this ability to use science to make the planet a bit better and to leave it in better conditions for my kids is extremely important to me.

That connection between research and development, between science and doing good, this ability to use science to make the planet a bit better and to leave it in better conditions for my kids is extremely important to me.

In a typical workday, it's easy to get lost in all of those meetings and the busy calendars or the kids at home. It's easy to get lost in that and forget about the impact that we're going to have. And it's a huge challenge that we have to face because we're literally taking what previously we've looked at as trash and now we want to stick it back into our products. Not only is it something we want to do, it's something that we are doing. When I think about a career at Dow, I want to be able to look back and see the maybe small but real contribution I will have made to help us reach that, and I think we will.