Tableau users have become data Jedi masters, pushing beyond BI into advanced analytics. Not only can advanced analytics find insights more quickly, but when used in the right manner they can also reveal the “why” behind past performance or behavior.
Tableau includes built-in capabilities for performing straight forward advanced analytics such as clustering and forecasting. For more complex analytics, Tableau allows for easy integration with data science tools like Python and R. When combined with the statistical horsepower of these third-party tools, Tableau’s visualizations allow analysts to awaken the Force within and conduct deep analysis.
In this on-demand webinar, you’ll learn the pros and cons and hear real use case studies of doing advanced analytics in Tableau using various methods including
- Tableau’s built-in capabilities
- Tableau’s integration with Python and R
- Stand-alone analytical programming languages Python and R
- Enterprise software platforms such as Alteryx
You may also be interested in reading these related blogs
- Get instructions to help you install, configure and start a R server to allow Tableau integration
- See a matrix that includes functionality ratings and discussion on the options for performing advanced analytics in Tableau
Presenter
Arik T. Killion
Advanced Analytics Architect
Senturus, Inc.
Arik has 16-years of experience in advanced and predictive analytics. Before joining Senturus, Arik was a consultant working at IBM as a data scientist/ technical professional for the North American Analytics Channel Development Team, Prior to IBM, Arik was the director of analytics at a large national marketing agency.
Arik has developed deep experience using a variety of methodologies from simple quantitative statistics to predictive modeling techniques and text/sentiment analysis to produce actionable insights, guide strategic management decisions and acquire meaningful business intelligence. He has created solutions for clients such as Chrysler Group, Kraft Foods, Allergan, Lowe’s, Verizon Wireless, JD Power, Nielsen Ratings Group, Sony Entertainment and many others.
Read morePresentation outline
Advanced Analytics Options within Tableau
- The Analytics Maturity Curve
- Tableau’s Built-In Analytics
- Using 3rd Party Analytics with Tableau – integrated and stand-alone architecture
- Open Source Analytic Programming Languages
- R
- Users primarily have been from academia and research, but is expanding into the enterprise
- Steep learning curve, but quite user friendly and easy to move into more advanced things once this is achieved
- Many packages available to perform various advanced analytics tests and models
- Most common IDE is RStudio
- Python
- Users primarily have been programmers crossing over into advanced data analysis
- Gradual learning curve due to code readability and simplicity
- Came a little later to the analytics game, but has many libraries available that are comparable in R
- Various IDEs available, but most common for data science work is Spyder or Rodeo
- Tableau Integration with R & Python
- Requires a server to handle and process requests coming from Tableau: R uses Rserve and Python uses TabPy
- Configuration on Tableau is as easy as telling it where the server is and what port address – found under Help > Settings
- Both server apps can run on a local machine for personal use or in a multi-user environment on an actual server
- The Rserve server for R for can only accommodate a single user when it is being run on Windows OS
- R
Built-In Analytics: Clustering Demo
- Clustering with Tableau
From Built-In Analytics to Advanced Forecasting in R: Time Series Demo
- Demand Forecast – Built-In Function
- Demand Forecast Using R – Monthly Seasonality
- Demand Forecast Using R – with Holiday Variable
A/B Marketing Test Models: Using Alteryx
- A/B Test for Offer Response
Evaluating Options for Using Advanced Analytics with Tableau
- The (Functionality) Matrix
- Benefits of Using Advanced Analytics
- Greater insights and visualization capability
- More powerful clustering and forecasting capabilities over the built-in functions
- Allows the analytics to find insights that may not be readily apparent
- Flexibility to use any available analytic applications, such as:
- Extracting sentiment from text
- Outlier & anomaly detection
- Geocoding & spatial analytics
- Identify & measure risk
- Prediction models for attrition or conversion
- Association & sequence analysis
- Optimize & prioritize
- Root-cause analysis
