Champions
Building a Business Case
Getting buy-in for new tools can be hard. We get it. We've put together resources to make it easier.
Steps for getting buy-in:
- Identify a problem that matters to the organization. The best place to find it? The thing everyone complains about but no one has had time to fix. Look at your team's backlog or listen in on stakeholder meetings.
- Build something tangible with open source tools: a Shiny app, a Quarto doc, a dashboard, a Streamlit app. It doesn't have to be perfect.
- Make it visible to management. Don't just describe what you built, show it. Let them see the output with their own eyes. A working prototype/demo will do more for your case than a polished slide deck.
- Let the speed do the talking. That thing sitting at #25 on the backlog for months? You solved it in a week. That contrast is your pitch.
- Open the aperture of leadership. The goal of the demo isn't to get a budget approved immediately, it's to get them to think "maybe there is something here."
- Fit it into the larger enterprise solution. By the time you're asking for formal buy-in, you're not pitching a concept, you're asking to scale something that works. The final ask becomes: how do we take this from a proof of concept into production?
Talking with IT
You need two types of production
The word 'production' can mean something very specific to IT teams: uptime guarantees, load testing, and systems that cannot afford to fail. That definition makes sense for certain kinds of work, but applying it across the board can quietly kill your team's ability to move fast. The app that helps someone make a better decision this week does not need the same guardrails as the systems your business literally cannot afford to take offline. Most data science work is 'lowercase p production', where speed and iteration are the whole point. Mixing the two up is one of the most common ways organizations slow themselves down without ever realizing it.
Lessons from the community
Show proof from your peers
Show the impact that other data scientists have had at organizations like yours. When the numbers come from your peers rather than us, it can land differently. Browse real examples from teams who've made the same case you're trying to make.
Gen Re
TruDiagnostic
NASA
Unity Health Toronto
FAQs
It's a fair question, and one that comes up a lot, because both BI tools and data science platforms are often pitched as solutions to the same problem: helping organizations make better decisions with their data. That overlap can make them feel like competitors for budget and executive attention, but they're really built for different kinds of work.
BI tools are powerful and approachable. They have a lower barrier to entry, which makes them great for getting insights in front of a broad audience quickly. But that accessibility comes with tradeoffs. There are limits to their flexibility and analytic depth, which means there are limits to the complexity of 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.
This can be a common questions, and it's worth reframing before you answer it. An incredible variety of the world's computation already runs on open source software. Open source simply means the code is developed in public and available for review. That transparency is actually a feature, not a liability. It means vulnerabilities get spotted and fixed faster, not slower.
R and Python have been around since the early 1990s and have millions of users every year. They're complete programming languages capable of complex statistical calculations, machine learning, dashboarding, reporting, and more. They form the backbone of data science at governmental organizations, major pharmaceutical companies, banks, and financial institutions worldwide.
The reality is that most organizations are already running on open source, whether they realize it or not. The 2021 State of Enterprise Open Source report found that 90% of IT leaders surveyed are using enterprise open source today.
So the question isn't really whether open source is trustworthy, it's whether your organization has the right infrastructure around it. That's where Posit comes in. Posit's professional products replace the open source AGPL license with a commercial license and give your organization the security, support, and accountability that enterprise IT requires.
There's a strong strategic case that goes well beyond cost. By building on an open source core like R or Python, you avoid putting yourself at the mercy of any single vendor. Your data science work belongs to your organization, not to a platform. You'll also find it easier to recruit and retain data scientists, since the best talent already works in these languages. And because the ecosystem is so comprehensive, you'll always have the right tool for any analytic problem, including the ability to connect to your existing investments.
There's also something deeper here about the nature of code itself. Complex, sometimes vaguely-defined analytic problems require the flexibility that only code provides with no black box constraints, full access to all your data, and the ability to transform and combine it however the problem demands. Code lets you iterate quickly in response to feedback or changing circumstances. And crucially, code is reusable, extensible, and inspectable. It becomes a core source of intellectual property in your organization, one whose value compounds over time as it's modified, extended, and applied to new problems.
And more broadly: it's better for everyone when the tools used for research and science are free and open. Reproducibility, widespread sharing of knowledge and techniques, and the removal of cost barriers benefit the entire field, not just your team.
Posit'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.
Posit Team is a modular platform of commercial software products (Posit Workbench, Posit Connect, and Posit Package Manager) which gives organizations the confidence to adopt R, Python and other open-source data science software at scale. Posit 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, Posit'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 Posit's commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone.