Webinar discussion: What tips would you provide for organizations where data science is not fully established?

Continuing the discussion from the Building Effective Data Science Teams Webinar:

What tips would you provide for organizations where data science is not fully established?

Some discussion from the webinar:

Our panelists for this webinar were:

  • Kobi Abayomi, Senior VP of Data Science at Warner Music Group
  • Gregory Berg, VP of Data Science at Caliber Home Loans
  • Elaine McVey, VP of Data Science at The Looma Project
  • Jacqueline Nolis, Head of Data Science at Saturn Cloud
  • Nasir Uddin, Director of Strategy & Inspirational Analytics at T-Mobile
  • Moderated by Julia Silge, Software Engineer at RStudio, PBC

Elaine: I think one of the answers, which I wish were not the answer, is that a lot depends on finding the right home in the organization. I don’t think there’s one clear answer to what that is. It depends a lot on your company and stakeholders.

In terms of scaling up, even if you have a lot of credibility, have produced a lot of great work and people are excited about the data science team - if you’re not in a place in the organization that fits in terms of the business and how the company is organized, it’s hard to grow the team.

There can be a lot of uncertainty about what it means if we have more data scientists. An executive who’s not a data science person may not quite understand what we get from that. This is not an easy problem to solve because it can be a lot easier to add people to more classic business things that they understand. For leaders, this is a really important thing to think about. Where in your organization can you find the best long term fit? Where do your highest level leaders understand the value of what you do?

Nasir: At one of my previous employers, I was hired as the first data scientist - actually, the first person to explore whether AI/machine learning would be a value-add. They had a huge amount of data available within the organization. I took the challenge of generating confidence among the stakeholders with limited resources.

I defined some low hanging fruit types of problems and solved them by providing access to self-service tools. In that case, Shiny applications were tremendously helpful to me. I took the sample data and generated outcomes the way they wanted, interacted with them, and put them into the driver’s seat. They were so happy. From this, I was able to get buy-in from most of the stakeholders so that I could grow the team. I then built their engineering team, data science team, and system administration team. It was all about generating confidence among the stakeholders and creating values for the business.

Greg: I think that makes complete sense. To add a point to that, it seems like you have to shift from being a data scientist and put your advertising hat on. You need to advertise what you’re doing and show the value. Then, shift to be an economist and say, “here is the return on investment of adding more data scientists and this is what you can get.” You need to have this broader perspective, rather than just wanting to build models. You need to be an advertiser and I think your example, Nasir, was great. You did that.