There are many reasons for this:
Data Science teams using R and Python struggle to collaborate and share their work consistently with their stakeholders. Read More
BI and Data Science teams are stuck in silos, failing to collaborate, or even competing for resources and executive mindshare. Read More
Data Science work is stuck on local hardware, and not integrated into your organization's cloud strategy. Read More
Analytics teams struggle to access and combine all their data in reproducible data pipelines. This data can be spread across Excel files, corporate data sources, and the cloud, in the form of structured, unstructured and nontraditional data sources. Read More
Data Science teams find it difficult to utilize the job management systems that DevOps/IT provides. These systems, built using frameworks like Kubernetes and Slurm, may sit idle, while data science teams struggle to find the compute power they need to create insights. Read More
Data Science insights are stuck on laptops, instead of supporting the human and automated workflows that drive an organization's decision making. Read More
By adopting Serious Data Science, open source, code-first, scaled and managed on RStudio's professional products, your organization can fulfill the promised value of your analytic investments.
Eric Nantz, a Research Scientist at Eli Lilly and Company, spoke at rstudio::conf 2020 about the importance of interoperability in R.
Leverage all your data
Access, transform and combine all your data, tailored to your specific applications
According to a recent Forrester report, "Anecdotal evidence shows that no more than 20% of all enterprise data that could be used to drive actionable insights is leveraged for that purpose." (Forrester report by Boris Evelson and Cinny Little).
Analytics teams struggle to access and combine all their data in reproducible data pipelines, especially when this data is spread across Excel files, corporate data sources, and the cloud, in the form of structured, unstructured and nontraditional data sources (like web scraping).
RStudio's open source, code-first approach gives you the access and flexibility you need to leverage all your data, and build reproducible data pipelines to serve all your analytic requirements.
Scale with Kubernetes, Slurm and more
Use all your available compute resources, from your familiar development tools
Analytic infrastructures such as Spark or Kubernetes require considerable resources to set up and maintain. If data scientists have to leave their native tools to access this infrastructure, they have to switch contexts and remember how to use systems they might only rarely touch. Often, this means they won’t fully exploit the resources available, leading to these resources being underutilized.
By providing native access to these tools from the languages and development environments data scientists use everyday, data scientists can leverage these tools without having to switch contexts. The data scientists get the compute power they need, while making better use of IT resources. This higher utilization helps the organization achieve the expected ROI from these analytic investments.
Integrate your Data Science via APIs
Deliver your data science insights directly where they add value
Once a predictive model or other analysis has been created, there are many different ways to share that analysis so that it can have an impact. Data scientists may
However, one of the most scalable ways to share an analysis is to create an API. APIs can empower real time interaction with statistical models and analysis outcomes. This enables other developers either inside or outside of an organization to integrate directly with and build upon work that’s already been done without the need for costly re-implementation.
Even more possibilities
Integrate with other modeling environments, Git, CI/CD workflows and more
One of the great benefits of using open source R and Python is the vast array of integration options these environments provide. New integrations are continually added, either directly by the community, or by vendors like RStudio.
Because of this, no list of integration points can ever be truly comprehensive, but you can be confident that if you need to integrate R or Python into another system, chances are someone else has already solved the problem for you.