There is no doubt about it: A variety of methods which are easy to access can make R a valuable tool for plenty application scenarios in business, e.g. optimizing sales campaigns by scoring potential customers, predicting machine failures with patterns from sensor data or forecasting weather conditions in order to have some guidance for the trade and supply of energy. The knowledge in these kinds of scenarios has emerged from the young discipline of data science and is based on contemporary methods of data mining and predictive analytics. At the same time there are many more application scenarios for R which are not directly connected to data science but are substantially impacted by classical analytics used in business and particularly in industry, for example in process controls, process validation or cyclic reports. Even though R has reached incredible popularity for data science methods, companies still struggle to make R accessible for their classical analytics. The talk will highlight the differences of data science and classical analytics and reveal the underrated potential of R in business processes which are dominated by a particular software. Furthermore, the talk will give an outlook for R in the field of analytics.

1. © 2010 – 2016 eoda GmbHAndreas Wygrabek
Application fields for in
classical industrial-analytics
industry
eRum 2016
Andreas Wygrabek
Data Scientist

2. © 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
Objectives
1. Differentiation of data science and classical analytics tasks
in industry
2. Application scenarios for R in industry
3. Reveal the potential of R in scenarios of classical analytics
What is this talk about?

3. © 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
Representative applications of ...
Data Science

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AnalyticMetrological Data Data Science
Data Science | Energy Forecast
Weather Forecast

5. © 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
AnalyticSensor Data Data Science
Data Science | Predictive Maintenance
Machine Failure Predictions

6. © 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
AnalyticCustomer Data Data Science
Data Science | Scoring
Conversion Probability

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Representative applications of ...
Classical_Analytics

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AnalyticAcceptance Sampling Analytic
Classical Analytics | Incoming Goods Inspection
Results (Acceptance, Rejection)

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Analytic
Sampling Data
Analytic
Classical Analytics | Statistical Process Control
Process Charts

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Analytic
Sampling Data
Analytic
Classical Analytics | Design of Experiments
Cause-Effect-Relations

11. © 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
How do the applications of data science and
classical analytics in industry differ?

12. © 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
Exploratory Data Analysis
(Data Science Methods)
Predictive
Classification
Regression
Clusteranalysis
Modelling
PCA
Forecasts
Data-Mining
Algorithms
Statistics
Mu Sigma
alpha/beta
ProbabilitiesNormal Distribution
Confidence
Inference Statistics
Descriptive
Statistics
Mean
Range
Variance
Correlation
…
…
Time Series
…
Testing
Deep Learning
Analytics and Data Science | Methodology
Analytics Data Science

13. © 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
Analytics and Data Science | Project Management
Analytic: Traditional PM
- Solution known
- Standardized Issues
- Ressource estimation
Data Science: Agile
- Interative solution
- Individual Issues

14. © 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
Analytics and Data Science | Data
Analytic: Data is certain
- Data certainly contains
all required informations
Data Science: Data is uncertain
- Often it is not certain that
the data contains the
required information

15. © 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
Analytics and Data Science | Difficulty
Analytic is complicated
- Experts will solve the issue
Data Science is complex
- No guarantee

16. © 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
Analytics and Data Science | Location
Analytic:
Data Science:
- Quality Management
- Production
- IT
- Business Intelligence
- Data Science Lab

17. © 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
Analytics and Data Science | Implementation
Analytic: Choosing the right tool
Data Science: Proof of Concept

18. © 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
How capable is R to solve the issues which are
typical in industry?

19. © 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
- Incoming Goods Inspection
- Sampling
- AcceptanceSampling
- Statistical Process Control
- spc
- qcc
- IQCC
- qualityTools
- SixSigma
- DoE
- CRAN Task View: Design of Experiments (DoE) &
Analysis of Experimental Data
- Most used package: agricolae
Classical_Analytics
Reporting
- knitr
- shiny
- interfaces to external
software
in

20. © 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
Mind the gap
1. R is powerfull – all the tasks mentioned can be solved
2. R is free
3. R is able to report
4. The need for new tools in classical analytics is growing

21. © 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
What are the reasons?
1. Qualification
2. Old processes
3. Established Software

22. © 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
To Do´s
1. Bring R-Developers closer to QM and
production
2. Upskill engineers in R
3. Further package development (AQL, ISO
2859, ISO 3951)
4. Create stable R environments

23. © 2010 – 2016 eoda GmbHAndreas Wygrabek www.eoda.de
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The Data Science Specialists.