This document provides a summary of a presentation by Prof. dr. Bart Baesens on rocking analytics in a big data world. It discusses how customers, corporate data, and partners generate large amounts of data that can be analyzed. It presents the analytics process model of data selection, cleaning, transformation, and mining to discover patterns. Examples are given of using analytics in marketing contexts and for response modeling. The document also discusses research partnerships with industry for master's projects, PhDs, and research chairs and international partners.

1. Rocking Analytics in a Big Data World
Prof. dr. Bart Baesens
Department of Decision Sciences and Information Management,
KU Leuven (Belgium)
Vlerick Leuven Ghent Management School (Belgium)
School of Management, University of Southampton (United Kingdom)
Bart.Baesens@kuleuven.be
Twitter/Facebook: DataMiningApps
www.dataminingapps.com

2. Living in a Data Flooded World!
Customers Analytics
Web/email
Call center
Survey
Corporate data
Partners

3. The Analytics Process Model
Interpretation
and Evaluation
Data
Transformation
Analytics
Dumps of operational data (Binning, alpha to
numeric, etc)
Data Cleaning
Understanding
what data is
needed for the Data
Selection Patterns Analytics
application
Application
Transformed
Data
Preprocessed
Source Data Mining Data
Data Mart

4. Example: marketing context
Customer Age Recency Frequency Monetary Churn
John 35 5 6 100 Yes
Sophie 18 10 2 150 No
Victor 38 28 8 20 No
Laura 44 12 4 280 Yes

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6. Analytics: is it relevant to me?
• Today your bank computed your probability of default
for the next 12 months
• Today your Telco operator computed your probability
of churn for the next 3 months
• Facebook analysed the messages you posted today
and uses it to do social advertising
• When you visited CNN, BBC, De Standaard, Sporza, …
information about your visit was stored and analysed

7. Research partnerships with industry
• Master project
• PhD level
– IWT Baekeland
– European projects (e.g. FP7)
– Research Chair
• RSZ, FAIR: Forecasting and Network Analytics for Fraud detection
• Ticketmatic, Cultural Data Analytics

8. International partners