An introduction to Big Data, developed for training purposes. All rights reserved by their respective owners.

1. How to Create New Business Models
with Big Data & Analytics
Aki Balogh
Calpont Corporation

2. Agenda
1. What is Driving Big Data?
2. What is Big Data?
3. What is Analytics?
4. What can you do with Big Data & Analytics?

3. What is Driving Big Data?

4. Where is Big Data today?

5. What is driving Big Data?
1. Rising volumes of data
2. Falling cost of data management tools
3. Rising number of Data Scientists

6. #1: Data volumes are growing
Growth in Unstructured Data Types of Unstructured Data
• Social Media
• Clickstream data
• Machine-Generated Data (e.g. logs)
• Internal Documents
• Notes (e.g. Patient Charts)
• Images
• Video
• Sound

7. #2: Data management tools like Hadoop
are driving down cost

8. #3: Data Science as a discipline is growing

9. What is Big Data?

10. Big Data is about turning data into
insights to drive decision-making
Source: Allen (1999)

11. A Simple Framework: 3 Vs of Big Data
•Volume
•Variety
•Velocity
11

12. #1: Volume
Source: Christopher Bingham, Crimson Hexagon. “Better Algorithms from Bigger Data.”

13. #2: Variety
A few examples how combining data can dramatically change the way marketers gain
customer intelligence and measure campaign effectiveness:
1. CRM Data + Web Data = Understand actual lead quality not just lead quantity and
drive more intelligent drip marketing, lead nurturing and re-marketing programs
2. Call-Center Data + Web data = Analyze calls you can avoid and calls you should avoid.
(Example; calls to the Call-Center for simple customer-support or operational needs
that are already serviced online)
3. Past Purchase Data + Web Data = Segment customers based on past buying behavior,
and use this to drive targeted web campaigns to loyal customers.
4. Campaign Data + Web Data = Understand multi-touch attribution – and optimize your
campaign mix based on behaviors.
5. Social Media Data + Web Data = Measure traffic to your website from social media
campaigns and track actual conversions.
Source: “Why Web Analytics is Not Enough.” Quantivo.

14. #3: Velocity
Source: Guavus Reflex Platform. http://www.guavus.com/#/solutions/guavus-platform/

15. What is Analytics?

16. A Simple Framework for Analytics
•Descriptive
•Predictive
•Prescriptive

17. Types of Analytics you Could Use…
• ARMA • Logistic/Lasso Regression
• CART • Logistic Regression with Adaptive
• CIR++ Platform
• Compression Nets • Monte Carlo Simulation
• Discrete Time • Multinomial Regression
Survival Analysis • Neural Networks
• D-Optimality • Optimization: LP; IP; NLP
• Ensemble Model • Poisson Mixture Model
• Gaussian Mixture Model • Random Forests
• Genetic Algorithm • Restricted Boltzmann Machine
• Gradient Boosted Trees • Sensitivity Trees
• Hierarchical Clustering • SVD
• Kalman Filter • Support Vector Machines
• K-Means
• KNN
• Linear Regression

18. Analytics that are Actually Used
Classification and
regression trees /…
69% 25% 6%
Linear Regression 66% 33%
Logistic regression or
other discrete choice…
61% 29% 10%
Association rules 49% 37% 14%
K-nearest neighbors 36% 42% 21%
Neural networks 30% 36% 34%
Box
Jenkins, Autoregressive…
30% 35% 35%
Exponential smoothing /
double exponential…
22% 43% 34%
Naïve Bayes 21% 43% 36%
Support vector machines 20% 23% 57%
Survival analysis 15% 41% 44%
Monte Carlo Simulations 13% 47% 40%
Frequently Occasionally Not at all
Classification and regression trees / decision trees and Linear Regression are
the most popular predictive analytics techniques used.
Source: Ventana Research Predictive Analytics Benchmark Research
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19. Who does Analytics?
You don’t need to have a PhD…

20. Five Common Types of Analytics
• Classify
o Segmentation, discriminant analysis
o Clustering
o Unsupervised and supervised machine learning
• Trend
o Time-series analysis
• Optimize
o Find the optimal outcome of an objective function (min/max)
• Predict
o Predict the outcome of a single event
• Simulate
o Explore the consequences of different choices to help drive decision-
making
o Open-ended: Scenario planning, DSS

21. So, what is Analytics?
•Descriptive
•Predictive
•Prescriptive

22. What can you do with Big Data & Analytics?

23. What does Big Data Analytics require?
Data: data availability + storage + integration + data management
tools
+
Analytics: analytic formulas + statistical integrity + analytic
applications
+
Interpretation: business problem + domain expertise + visualization
+ decision-making
This typically requires a team of people with different skillsets.

24. What can you do with Big Data & Analytics?
1. New revenue models
o Ex: Rapleaf scraping the web, collecting contact information and
selling full datasets
1. New user experiences
o Ex: Gmail recommendations for people to CC: on your email
2. Cost optimization (i.e. deliver same product or service at less
cost)
o Ex: Give your financial advisors tools to help automate your
investment decisions