- Sanitas is a health insurance company founded in 1958 with over 822,500 customers, 750 employees, and annual revenues of 2.7 billion CHF - The presenter works as a data manager at Sanitas and discusses how they have improved their data science workflow using R for data preparation, modeling, operationalization, and ensuring reproducibility - Key changes include gaining direct access to data sources through RStudio Server, leveraging cloud resources for modeling, automating reporting, and implementing version control and packages to reproduce results

1. Welcome to Sanitas
04.09.2017 Zürich R User meetup1
822’500customers
750employees
#1in customer satisfaction1
1.4 mncustomer contacts
2.8 bn assets under management
61 mnCHF operating profit
Founded in 1958
2.7 bn CHF total revenues
2.5 bn CHF total paid-out claims
1 K-Tipp-Survey no. 15/2016
Source: annual report 2016

2. Zürich R User meetup
R at Sanitas – Workflow, Problems and
Solutions
Patrik Lengacher
Zürich, 04. September 2017
Source: Photoshopped adoption from : http://www.ecns.cn/cns-wire/2015/02-26/155833.shtml/

3. Who am I?
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Patrik Lengacher
Data Manager
Analytics
MSc, ETH, Mathematics
BSc, ETH, Mathematics
Sanitas – Health Insurance
Layzapp AG – Start up
Accenture – Consulting
Paul Scherrer Institute
github.com/plengacher
linkedin.com/in/plengacher
patrik.lengacher@{sanitas,gmail}.com

4. Agenda
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Zürich R – R at Sanitas
The data part of the data science workflow
Data Preparation
Modeling
Operationalize
Reproducibility
corporate identity & corporate design (CI/CD)

5. The data part of the data science workflow
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Data Sources
Data Prep
Optimize Data
Data blending
Data cleansing
Feature engineering
…
Modeling
Apply statistics / machine learning
Prototyping
Train & testing
Validation
Visualization
….
Operationalize
Deploy models
Dashboards
Reports
Presentations
…

6. Agenda
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Zürich R – R at Sanitas
The data part of the data science workflow
Data Preparation
Modeling
Operationalize
Reproducibility
corporate identity & corporate design (CI/CD)

7. Old New
Data Preparation
Firewalls and tool incompatibilities prohibit a clean workflow. Changes in the IT Infrastructure and the
use of our Rstudio Server lets R interact directly with the data sources.
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8. Agenda
11/6/2017Zürich R – R at Sanitas12
Zürich R – R at Sanitas
The data part of the data science workflow
Data Preparation
Modeling
Operationalize
Reproducibility
corporate identity & corporate design (CI/CD)

9. Old New
Modeling
If the local machine runs out of resources, isolated solutions were created. Due to the nature of the
solutions, they were not integrated in our infrastructure.
Zürich R – R at Sanitas13

10. Agenda
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Zürich R – R at Sanitas
The data part of the data science workflow
Data Preparation
Modeling
Operationalize
Reproducibility
corporate identity & corporate design (CI/CD)

11. Old New
Operationalize (I/II)
Results of Ad-Hoc Requests were written as comments into the source files. Furthermore emailing the
results became a time consuming repetitive task.
Zürich R – R at Sanitas15

12. Old New
Operationalize (II/II)
Results of Ad-Hoc Requests were written as comments into the source files. Furthermore emailing the
results became a time consuming repetitive task.
Zürich R – R at Sanitas16

13. Agenda
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Zürich R – R at Sanitas
The data part of the data science workflow
Data Preparation
Modeling
Operationalize
Reproducibility
corporate identity & corporate design (CI/CD)

14. Old New
Reproducibility (I/III)
Using markdown, shiny, MicroStrategy helps us to distribute results, presentations and reports. The
tools help to keep the documents up to date.
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15. Old New
Reproducibility (II/III)
Version Control and a SanitasR Package with the commonly used functions help us to reproduce our
results.
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16. Old New
Reproducibility (III/III)
Version Control and a SanitasR Package with the commonly used functions help us to reproduce our
results.
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17. Agenda
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Zürich R – R at Sanitas
The data part of the data science workflow
Data Preparation
Modeling
Operationalize
Reproducibility
corporate identity & corporate design (CI/CD)

18. Old New
CI/CD
Defining colors and themes help to get our plots CI/CD ready.
Zürich R – R at Sanitas22

19. 06.11.2017Zürich R – R at Sanitas23