Business Analytics: A Knowledge Community and Repository Infrastructure for R Models. Master Teamproject Prof. Dr. Alexander Mädche, Martin Kretzer

Transcription

1 Business Analytics: A Knowledge Community and Repository Infrastructure for R Models Master Teamproject Prof. Dr. Alexander Mädche, Martin Kretzer

2 Agenda 1. The Challenge 2. The Master Teamproject 3. Benefits for Students 4. Summary

3 The Challenge: Generating business value from Big Data through Predictive Analytics Analytics Discover meaningful patterns in data Guide decision-making Descriptive Analytics Summarize what happened Example: number of Facebook likes Predictive Analytics Forecast what might happen Example: regression analysis Prescriptive Analytics A subset of predictive analytics Recommend certain actions 1. Capture Data 2. Discover Patterns 3. Make Predictions Big Data A B C Predictions about the future are highly valuable for organizations Predictive Analytics encompass Prescriptive Analytics 3

4 The Challenge: A single BI platform cannot provide the functionality to compute all types of predictive analytics What is R? R is an open source programming language and software environment Mainly used for statistical analyses Huge community worldwide R code for (almost) all predictive analyses already exists somewhere on the web Business Intelligence (BI) platforms cannot provide unlimited analytical capabilities Observable trend: Many supplier of BI platforms allow users to integrate R BI platforms need to be supplemented by a software environment for analytical computing that is open source and has many contributors Supplier of BI platforms rely on the integration of R into their platforms 4

5 The Master Teamproject: Develop a platform for central storage of R code and a webpage for presenting your results So which problems remain unsolved? No reuse of R code Business analysts are not trained in R R Knowledge Community Task 1: R Code Repository / Integration Infrastructure Develop a platform that stores and provides R code for analytical data processing Enable integration of R code into multiple platforms Task 2: R Knowledge Community Develop a web-based knowledge community that supports business analysts in using R Capture and publish knowledge that explains the use and integration of R into different BI platforms Company 1 Company 2 Company 3 Storing R models centrally enables reusing them across BI platforms Providing hands-on knowledge allows data analysts to perform analytics 5

6 0-1 Leader 1-2 Designer 1-3 Analysts 2-4 Developers The Master Teamproject: Students will work in one of four roles depending on their abilities and personal interests Major contact for supervisor Will be determined by the team and the supervisor together Distribute workload Manage milestones Set up, design and maintain web page Document and publish results online Develop tutorials about analytics in R Select BI platforms for the project Compute predictive analytics Integrate R into different BI platforms Develop a repository for storing R Select BI platforms for the project Assist designer with webpage development Assist analysts with R integration Develop a repository for storing R There will be close collaboration between all team members Every team member will be programming predictive analytics in R 6

7 Benefits for Students: Students will receive a skill set that is highly demanded in industry in academia Statistics knowledge and ability to perform predictive analytics on Big Data Experience in using different BI platforms Preparation for official BI certifications which are offered by BI platform supplier R programming skills and ability to integrate R into different BI platforms Skill set for numerous job roles: business analyst, data scientist, analytics solution architect, consultant... Possibility for scientific publications and opportunity to write the Master s thesis Hands-on training in using state-of-the-art BI platforms Combined knowledge of statistics, programming, and business analysis 7

8 Summary: Master Teamproject Fact Sheet Title: Predictive Analytics: Integration of R Models across different Business Intelligence Platforms Supervisor: Prof. Dr. Alexander Mädche, Martin Kretzer Language: German and English Number of students: 4-10 Mandatory requirements: At least 3 students need programming experience! Beneficial skills: Practical experience in usage and/or administration of BI platforms Programming (especially in R) Web page design and development License: All results will be published under an Open Source license 8

9 Summary: Web Resources for Getting Started University of Mannheim Chair of Information Systems IV (ERIS) ( Web ) ( Facebook ) Institute for Enterprise Systems (InES) ( Web ) ( Facebook ) Predictive Analytics Demo for predictive analytics with SAP HANA (with and without R) ( Web ) Interesting blog on the adoption barriers of predictive analytics ( Web ) R Official R Webpage ( Web ) Introduction to R ( Web ) and a good collection of R resources ( Web ) Good overview of statistics and R functions ( Web ) Comparison between R and alternative statistical software environments ( Web ) A great blog on R integration in Tableau ( Web ) 9

10 We would like to work with you! Prof. Dr. Alexander Mädche Phone Martin Kretzer (primary contact person) Phone Chair of Information Systems IV and Institute for Enterprise Systems L 15, 1-6, Mannheim, Germany (4 th floor)