BI and Data Science
Serious Data Science
The Ease of Use
of Self-Service BI
Many organizations fail to get the full value out of their data because:
This means that the deeper insights that the data science team can provide fail to reach decision-makers effectively.
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
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
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:
“Power BI is an easy to build visualization tool widely used in our organization to make data accessible to non-data people. This is a really great tool when we want to create a dashboard for trends and track some metrics. But it becomes very difficult when we want to enable high levels of user interactivity with the dashboard. That’s where R Shiny helped us to build intuitive and highly interactive user interfaces.”
– A marketer at a large telecommunications firm
“RStudio is code based, so in the beginning tools like Spotfire and Tableau have [their] advantages since many things are already built in, but in terms of flexibility RStudio will win over the longer term.“
– A team lead in a biotech company
“Most of the work the data scientists did used the R language. They did a great job satisfying management’s constant barrage of questions because iterative analysis is so easy with tools like R, and the powerful visualization tools made communication of results easy for sales people to grasp. As the CEO, I was gratified at how clear the presentations were and at how quickly presenters answered my difficult questions, in some cases on the fly during the presentations.
As an R user myself, I know its code-based workflow lends itself to rapid iteration while, at the same time, documenting the process used. It was easy to unroll the tape to see every step that led to any conclusion.”
– Art Steinmetz, former Chairman and CEO of Oppenheimer Funds
“The R ecosystem has vast power to quickly solve problems. With R, I can incorporate nearly any AI/ML model into a dashboard or Shiny app, without being reliant on proprietary data science tools. Executives can be confident I am using the best analytic approach for a given problem, and I can rapidly apply new approaches as they become available.”
– Paul Ditterline, Director of Data Science at Heaven Hill Brands