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Advanced Analytics at Scale:Deploying Data Science in the Enterprise
# T C 1 8
Erik Polano
Associate Solution Architect
Tableau
Erwin van Laar
Team Lead, Product Consultancy
Tableau
Session Overview
Agenda
• Bridging the Gap
• Implementation
• Scaling Out
• Q&A
Introduction
Who are we?
MSc Applied Economic AnalysisMSc Public Health: Healthcare Management,
Innovation and Policy
Thesis:
“Fraternity, sorority and stereotyping. A field
experiment examining gender dynamics in referral-
based hiring” presented at American Economics
Association Conference, San Fransisco,2016
Thesis:
“Patient Satisfaction with Care among Patients with
Hematuria”
Oracle, Financial Applications Consultant MaetisArdyn (occupational health service),
Management traineeship and team leader
Loves Linux Loves Theatre
Two worlds……(One Love)
Why is Tableau useful for Advanced Analytics?
Visually Present Results in Interactive Format
Why is Advanced Analytics useful for Business
Users?
Make Better Business Decisions with more informed
analysis
Statistics/Machine Learning Expert Domain Expert
Highly concerned with Validity Highly concerned with Output
Problem
Problem
Experiment
• Ideal World: run double blind controlled experiment
• Causal Impact(Google Data Science team)
• Approximate an experiment using control variables
Problem
• Business users are not trained in (advanced) statistical analysis
• Data Scientists have a critical framework without necessarily having access to what problems are most pressing to their business users
How to Solve This?
Result
• Quicker Development of More Relevant and Informed Analysis.
• Improve ROI on work done by Data Scientists by making their results more visible, understood and relevant to Business Users.
External
Service
Connection
What is the End Goal?
What is the End Goal?
Data-Driven
Experience
People
Time
Want
Can
Conventional Self-Reliant
Want Can
Report Factory
App
Tableau empowers the whole organization. It brings advanced analytics into the hands of people who don’t necessarily have an analyst’s or programmer’s skill set.
Alexs Thompson, Ph.D.
Data Scientist, Hallmark Cards
Bridging the Gap
Visual Analytics v. Advanced Analytics
Visual Analytics (‘viZH(oo)əl ,anə'lidiks)
Data access, discovery, exploration, and information-sharing elevated by visual interactivity.
Advanced Analytics (əd' vanst ,anə'lidiks)
Smart, automated, or otherwise advanced data access, discovery, exploration, and information-sharing, meant to push the boundaries of traditional analytics.
Key Principles
Solutions should have four key characteristics:
• Easy to use
• Fast
• Powerful
• Visual
What do you need?
• Tableau Desktop
• Tableau Server
• External Services• Rserve• TabPy• MatLab
Tableau Desktop
Tableau Server
How could the setup look?
Tableau Desktop Tableau Server End users
Implementation
Implementation Framework
• Discovery
• Prototyping
• Foundation Building
• Scaling Out
Assessing business question/value (Discovery)
• Establish champions from all aligned departments
• Create a Centre of Excellence• Develop and promote best practices
• Provide support
• Promote culture of (advanced) analytics
Prototyping
Model Creation
• Data Discovery Starts.
• Build Presentation of Results through Tableau Desktop
• Calculation types • SCRIPT_REAL, SCRIPT_STR,
SCRIPT_INT, SCRIPT_BOOL
• Calculations are handled as Table Calculations
Connect to Tableau Server
Connect to Tableau Server
tsm configuration set -- key vizqlserver.extsvc.host --value <IP address>
tsm configuration set -- key vizqlserver.extsvc.port --value <Port>
tsm apply-pending changes
Dashboard Design (Foundation Building)
• Use of titles/tooltips
• Guided Analytics
• Interactivity
• Parameters• Selecting the required variables
• Selecting the needed statistical significance
Dashboard Design (Foundation Building)
• Visual cues (design principles)
• Robustness of model
• Can you trust the calculation?
• Use of triggers/shapes/colors
Scaling Out
Scaling out
Two Types:
• Iterative processes across the organization (Number of people involved in building dashboard).
• Availability of Advanced Analytics Dashboards (Number of people consuming the dashboard).
Load Balancing
• HAProxy
• Free, Open Source application for load balancing
• Very useful for scaling out Rserve instances
• Easy to Configure
Tableau ServerHAProxy
Pre-Configuring / Performance
• Heating Up External Environments
• Define How Rserve Should Start
• This can include:• Preloading Libraries
• Create Environments
• Define Timeouts
Can I use both R & Python?
• Flask• Routing requests to either environment
Governance
• Best in Class security and permissions through Tableau Server
• Users
• Groups
• Projects
• Permissions
Continuous Development
• Foster the Centre of Excellence
• Set up fun activities• Organize hackathons
• Makeover Monday
• Set up a Tableau Doctor hour
Questions?
Summary
Summary
• By scripting directly into Tableau, you’re able to:• Perform sophisticated statistical analysis
• Empower end users to set their own parameters and resulting scripts
• Easily and immediately communicate visual results
• Use lessons learned
• Don’t let perfect be the enemy of the good
Summary
• Remember the four key characteristics• Easy to Use, Fast, Powerful & Visual
• Use the framework• Discovery, Prototyping, Foundation Building & Scaling Out
• Get your champions involved
• Create an iterative environment, promote feedback
Conclusion
• Just get out there, and start unlocking more possibilities!
R E L AT E D S E S S I O N S
Tableau + Python = ❤Thur | 10:45pm – 1:15pm | MCCNO - L2 - 217
R…you ready? Jedi stats with R & TableauThur | 10:45am – 1:15pm | MCCNO – L2 – 260
Embedding Tableau for self-service data science
R E L AT E D S E S S I O N S
Thur | 2:15pm – 3:15pm | MCCNO – L2 – 238
Data science applications with TabPy/R
Wed | 12:00pm – 1:00pm | MCCNO – L2 – New Orleans Theater B
Please complete the
session survey from the
Session Details screen
in your TC18 app
Thank you!
#TC18
Resources - Whitepapers
Advanced Analytics with Tableau:
https://www.tableau.com/learn/whitepapers/advanced-analytics-tableau
Define Analytics: The changing role of BI’s favorite catch-all term
https://www.tableau.com/learn/whitepapers/define-analytics
Using R and Tableau
https://www.tableau.com/learn/whitepapers/using-r-and-tableau
Resources - Framework
Whitepaper: The Drive Methodology
https://www.tableau.com/sites/default/files/pages/whitepaper_roadforward_lt.pdf
Whitepaper: The Tableau Drive Manual
https://www.tableau.com/sites/default/files/pages/whitepaper_drivemanual_eng.pdf
Resources – Causal Impact
Brodersen, K.H., GALLUSSER, F., KOEHLER, J., REMY, N., & SCOTT, S.L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, vol. 9, pp. 247-274
Blog
https://opensource.googleblog.com/2014/09/causalimpact-new-open-source-package.html
Github
https://google.github.io/CausalImpact/CausalImpact.html
Paper
https://research.google.com/pubs/pub41854.html