BI and Data Science

Break down your analytic silos and get deeper insights to drive more value from your data

Many organizations fail to get the full value out of their data because:

• BI and data science teams work in separate silos, unable to collaborate effectively, or
• Analytic teams adopt a one-size-fits-all approach, relying solely on BI tools to solve their analytic problems.

This means that the deeper insights that the data science team can provide fail to reach decision-makers effectively.

Analytics Executives
Focus on guiding and supporting their teams to solve meaningful analytic problems for the business. When their analytics teams fail to collaborate and share their work efficiently, that means missed insights, lost opportunities and unnecessary expense. Read More

Data Scientists
Are frustrated when their work competes with the BI teams, or they are forced to awkwardly use unfamiliar BI tools to get their insights out to a wider audience.Read More

BI Analysts
Enjoy the power and ease of use of self-service BI tools but struggle with the limitations of their tools, including inadequate data science and machine learning capabilities, depriving them of deeper insights into their data. Read More

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Augment your BI tools with Serious Data Science

While data science and BI teams share the same goals, to help their organizations make better decisions by getting the most valuable insights possible out of their data, each approach has distinct strengths and challenges. By leveraging Data Science tools to augment your BI investments, you can maximize the value of the insights you deliver to your decision-makers.

RStudio helps break down analytic silos and enhance an organization's analytic investments, so that all members of the data organization (BI analysts, data scientists, ML engineers, data engineers) can collaborate, get deeper insights, support better decision making and drive results.

RStudio Team not only provides a single home for data science teams using R and Python, it enables Data Science teams to augment the great work that BI teams provide:

• Enrich your data using R and Python, for visualization and exploration in BI tools. Read more about Data Handoffs
• Build tailored applications using open source frameworks such as Shiny, Dash and Streamlit, and share them with your business users to complement standard BI reporting. Read more about RStudio Connect
• Securely call R and Python APIs from Tableau dashboards, to make the models, forecasts and other insights from your data science team available on demand. Read more about RStudio Connect and Tableau
• Easily embed interactive Shiny applications in Tableau, to augment Tableau's standard visualizations and leverage the visual insights your data science team develops. Read more about ShinyTableau
• Augment your BI tools existing R and Python integrations: Many BI tools have some form of integration with R or Python. RStudio Teams provides a secure, scalable, collaborative development environment for your data science teams to develop the open source code for deployment in your BI tools, as well as a way of managing your open source package use to ensure consistency and reproducibility across all your analytics platforms.