industries placeholder image

Building a Business Case

At RStudio, we have the opportunity to speak with people all over the world who are at varying stages of their analytics journey.

You may be:

  • A new Data Science Manager just starting to build out your team and curious about which tools will help the most
  • An individual analyst that wants to be more efficient in their every day work and needs to start the conversation internally about getting access to R/Python, in a way that will meet your organization’s IT requirements for security, administration, etc.
  • Any one of the millions of people using open-source data science today :)

If you’re starting at the beginning, know that you are not alone. So, where do you go from here?

Start the process of introducing data science tools

  • Consider all the tools that you already have available to you today. Under what conditions it would make more sense for you to use R/Python and why? Think in terms of the Return on Investment for the business as you start to build your business case.
  • If possible, develop prototype reports or apps to demonstrate what would be possible with the right tools.
  • Let your leadership know you are interested in acquiring new tools to make you more effective in your current role and ask about the process for this. There may be additional budget available to your team for new tools that you weren’t aware of before
  • Talk to the right people in IT (or through your manager), to identify the process that your company uses to onboard software through official channels. If you are part of a large organization, your IT department probably has a review board (for example: Architecture Review Board, Decision Review Board) whose purpose is to review and make decisions about new tools. Show IT that you take security seriously and recognize the challenges with introducing new software.
  • If you receive pushback with regards to budget or resources, consider working together with another team across the business. Teams that have received funding previously, may have ideas for you as well. See the section on building community if you have yet to find others in the company.
  • Approach resistance with curiosity and ask good open-ended questions. "What does our toolchain look like for next year?" "What commitment is necessary to use open source tools?"

Here’s an example of how this worked at Brown-Forman:

Paul's playbook:

  • Start off by doing things with free and open-source locally
  • Once you have that - install R Markdown, Shiny, and pull in local data. Show a Shiny application running locally from your own desktop or make a flexdashboard and send that out as an html file to people so they can see what it looks like.
  • Start to build your strategy. Make a slide deck that shows the current state diagram of how you do analytics and how people consume them. Then show what you could do if you had some set of data science tools and include literal examples of that.
  • Communicate value by using a stair-step approach and start small. Find a business need that you can solve better. People tend to come along for the ride when you’re interested in helping solve their problems.
  • Start a conversation with your immediate manager and have them help you work up the chain to then start the conversation with IT.
  • While you're helping people solve business problems, you’re also building examples you can include in your next presentation.
    1. Look, this is why we should do this.
    2. I have been doing it.
    3. Here's the feedback from that.
    4. Here's the solution to make this a real thing - instead of something just living on my laptop.
  • If you face questions such as, "Why do we need this, we have a BI tool," adjust your presentation to include a table of what each tool is good and not good at. Explain why you need both of them to solve this problem. (Additional info in the FAQ below.)

Share analyst reports and customer reviews

Often it can be helpful to provide third-party validation to help support your case for RStudio software. More than 1 million people use RStudio products every week. RStudio's enterprise products are used by thousands of commerical organizations, including 59 of the Fortune 100. On the peer review site, TrustRadius, RStudio has been top rated for the past 4 years.

If you'd like to help others advocate for RStudio and would be comfortable sharing your experience anonymously, your review would be greatly appreciated too! We love to hear feedback from customers.

Build your case with a presentation and proof of concept

Think about the business problems you could help solve with the right data science tools and share that as a proof of concept (including your company branding can make a huge difference.) Focus on the value this will bring to the company and the stakeholders you are presenting to.

What if I'm being asked to use existing BI tools instead?

Both Business Intelligence (BI) and data science platforms share a common goal: delivering rich interactive applications and dashboards that can be shared with others to improve their decision-making. However, this common purpose often leads to the tools (and the teams that support and use them) being seen as competitors for software budgets and executive mindshare in a large organization.

BI tools are powerful and have a lower barrier to entry for most users, but have limits to their flexibility and analytic depth. This limits the complexity of the questions they can answer.

Open source data science has a higher barrier to entry, requiring coding skills for development. But its flexibility and analytic power is nearly limitless. This allows organizations to answer the most complex questions they have.

Organizations must consider this balance, between the barrier to entry and the complexity of the questions that need to be answered, when choosing an approach. There are a number of RStudio blog posts and a BI & Data Science Overview that address this in further detail as well.

It can be helpful to include a table of what each tool is good and not good at. You can use this to help explain why you need both of them to solve this problem.

A customer shared this example (removing their branding) from their own internal presentation:

BI tools compared to open-source

What if they say no to open-source?

An incredible variety of the world’s computation runs on top of open source software. Open source means that the code for these programming languages is developed in public and is available for public review. This does not mean that these bits of code are ill-maintained or unloved.

R and Python have been around since the early 1990s and have millions of users every year. Both R and Python are complete programming languages that are able to do a wide array of complex statistical calculations, machine learning tasks, dashboarding and reporting, and more.

They form the foundation of data science practices for many different kinds of organizations that include governmental organizations, major pharmaceutical companies, banks and other financial institutions.

The reality is that most organizations are already supporting open-source software. The The 2021 State of Enterprise Open Source report survey shares that 90% of IT leaders (1,250 surveyed) are using enterprise open source today.

With regards to the license, RStudio's professional products replace the AGPL open-source license with a commercial license.

What are some additional benefits of open-source that I can share?

By adopting an open source core, you can make it easier to recruit and retain data scientists. The comprehensive nature of open source ensures you will always have the right tool for any analytic problem, including the ability to connect to all your other analytic investments. You also avoid putting yourself at the mercy of any specific vendor, since your core data science work is based on R or Python.

Complex, sometimes vaguely-defined analytic problems require the power of code. Code is flexible, without any black box constraints, and enables you to access, transform and combine ALL of your data. Code enables fast iteration and updates in response to feedback, or new circumstances. And most importantly, by its very nature code is reusable, extensible and inspectable, allowing you to modify and apply it to new problems, and track where changes occurred. Code becomes a core source of intellectual property in your organization, the value of which grows over time.

It’s better for everyone if the tools used for research and science are free and open. Reproducibility, widespread sharing of knowledge and techniques, and the leveling of the playing field by eliminating cost barriers are but a few of the shared benefits of free software in science.

For some suggestions on how to position the value of open source, code-first data science, see our Serious Data Science page.

What is the difference between RStudio open-source and professional products?

RStudio's mission is to make data science available to everyone, regardless of their economic means. Our professional products exist to scale, secure and deploy our open source products.

RStudio Team is a modular platform of commercial software products (RStudio Workbench, RStudio Connect, and RStudio Package Manager) which gives organizations the confidence to adopt R, Python and other open-source data science software at scale. RStudio Team allows organizations to leverage large amounts of data, deploy applications securely,integrate with existing enterprise systems, platforms, and processes, and be compliant with security practices and standards.

Together, RStudio’s open-source software and commercial software form a virtuous cycle: The adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone.

You can view a comparison of the open-source and enterprise products below:

Is RStudio just for R?

Nope! Many of our customers' data science teams are bilingual, leveraging both R and Python in their work. In line with our ongoing mission to support the open-source data science ecosystem, we've made the love story between Rand Python a happier one.

RStudio has focused on helping teams tackle key challenges of bilingual environments by making it easy to combine R and Python in a single data science project. You can launch and manage Jupyter Notebooks, JupyterLab and VS Code in RStudio Workbench, and share Jupyter Notebooks, Flask APIs, and interactive Python applications like Streamlit and Dash with your business stakeholders through RStudio Connect.

You can find more info on this here.

Any other tips for organizations where data science is not fully established?

Answers here are from our webinar on Building Effective Data Science Teams

Elaine McVey

VP of Data Science at The Looma Project

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 Uddin

Director of Strategy & Inspirational Analytics at T-Mobile

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 Berg

VP of Data Science at Caliber Home Loans

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.

What about resistance to buying vs. building?

A helpful economic argument from Gordon Shotwell's meetup presentation:

What does it mean to be a Public Benefit Corporation (PBC) and a Certified B Corporation?

RStudio endeavors to create free and open-source software for data science, scientific research, and technical communication in a sustainable way, because it benefits everyone when the essential tools to produce and consume knowledge are available to all, regardless of economic means.

We believe businesses should fulfill a purpose beneficial to the public and be run for the benefit of all stakeholders including employees, customers, and the community at large.

As a Delaware Public Benefit Corporation (PBC) and a Certified B Corporation®, RStudio’s open-source mission and commitment to a beneficial public purpose are codified in our charter, requiring our corporate decisions to balance the interests of community, customers, employees, and shareholders.

B Corps™ meet the highest verified standards of social and environmental performance, transparency, and accountability. RStudio measures its public benefit by utilizing the non-profit B Lab®’s “Impact Assessment”, a rigorous assessment of a company’s impact on its workers, customers, community, and environment. In 2019, RStudio met the B Corporation certification requirements set by the B Lab. The Certification process uses credible, comprehensive, transparent, and independent standards of social and environmental performance. Details of this assessment are available at In accordance with B Lab practices, our next certification will be done in December 2022.

More details on our transition to Benefit Corporation are available in this blog post as well as our Public Benefit Report.

Making the next move

Get in touch with RStudio

Please don’t hesitate to reach out to us if we can help. Our team has worked with thousands of customers that have implemented RStudio at their organization and always love to learn about the ways people are using data science around the world.

Schedule a call

Ask others who have done this before

We host a Data Science Hangout every Thursday at 12 pm ET. It's a low-barrier get together on Zoom for aspiring and current data science leaders. It's very casual, so there's no need to register or RSVP to attend. Each week, host Rachael Dempsey invites a data science leader to discuss their experience and answer questions from the audience.