Jul 15 2021
4 min

A CEO’s View of Open Source Data Science in the Enterprise

Thumbnail Art Steinmetz

Sharing customer stories

While the use and scope of open source data science continues to grow, we still sometimes hear from RStudio users and customers that they face some opposition, or at least questions, from IT or other stakeholders when championing a code-first, open source approach.

Of course, thousands of organizations have adopted open source data science as part of their analytics platform, and often the best way to reassure skeptics is to share their stories first hand. To help get these stories out, we feature RStudio customers in our Customer Stories, encourage our users to share their RStudio reviews on TrustRadius, organize RStudio Enterprise Community Meetups on various industries, and share blog posts (like this one on using Shiny applications to optimize COVID vaccine distribution in West Virginia, and this one on Strategic Analytics at Monash University).

Recently, I had the pleasure of sitting down with Art Steinmetz, the former Chairman, CEO and President of OppenheimerFunds. In this interview, Art gave his unique perspective on the value and suitability of open source, code-first data science for the enterprise.

Highlights from my interview with Art Steinmetz

Art earlier shared an in-depth perspective on Open Source Data Science in Investment Management as a guest contributor to this blog, so I was curious to learn more about his experience, both as an CEO encouraging his teams to use open source data science, and as an R user himself.

How and why did you get started with open source data science?

Art shared that he started using R, a major language for open source data science, when he became frustrated with the limitations of Excel. As he describes it,

“One of the things that really bugged me was my current self had no idea what my past self did, when I opened a spreadsheet from a year or two prior,”

and he was forced to puzzle through the obscure formulas and the critical dependencies between spreadsheets. He started using R more and more, because he found he was “getting answers faster, and with reusable code.”

Video: How did you get started with open source data science, and why?

Is open source software appropriate for enterprise-level data science?

From Art’s perspective, it is absolutely appropriate, because it is “a great way to boost productivity, by empowering all the interested parties in the organization”. Art related that because of the reach and availability of open source, there were many different people at his organization working on analytic problems. Open source “lets a thousand flowers bloom”, but critically this can be done in a managed, curated way that addresses IT’s concerns, using platforms like RStudio Team to support the full data science production life cycle.

Video: Is open source software appropriate for enterprise-level data science?

How do you build support for open source software within an organization?

Finally, I asked Art for his advice on how to build support for open source with an organization. While he says this is much easier than it used to be, as open source software has become more accepted, his primary advice was:

  • Start small, with quick projects to demonstrate value.
  • Inspire others, who will want the same power and flexibility.
  • Don’t “go rogue” and appear to be rejecting IT standards. Instead, work with IT as much as possible.
Video: How do you build support for open source software within an organization?

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