1. USING PREDICTIVE ANALYTICS FOR DATA-DRIVEN DECISIONS

2. Quiz
2
 Which course do students
hate the most?
 What is the sexiest job
today?
Data Scientist

3. What Has Changed?
Why Is Predictive Analytics a Hot Topic?
3
The time for decision-making has SHORTENED!!!
More real-time, on-the-job, data-driven decision-making

4. Explosion of Multi-Dimensional Data
Why Is Predictive Analytics a Hot Topic?
4
Advanced analytics helps you organize the world of big, multi-dimensional data into
actionable knowledge
Multi-source, multi-
dimensional data has
hidden patterns
Advanced analytics
reveals the underlying
patterns and allows us to
make predictions and
other actionable decisions

5. Explosion of Multi-Dimensional Data
Why Is Predictive Analytics a Hot Topic?
5
High Dimensional Data
Identify combinations of the data that have
distinct monetizable characteristics
The Cube
Slice and dice the data along
neatly cut dimensions

6. Explosion of Multi-Dimensional Data
Why Is Predictive Analytics a Hot Topic?
6
High Dimensional Data
 Identify clusters
 Identify patterns and exceptions
 Identify marginal effects of outcomes
Business Benefit:
 Better marketing & targeting
 Revenue growth or loss prevention
 More effective resource allocation
The Cube
 OLAP like reports
 High level of aggregation
 Drill down to details
Business Benefit:
 Get to the bottom of issues
 If sales are declining, which region or
product is causing it

7. How Does Predictive Modeling Work?
Copyright 2007, Information Builders. Slide
7
Detect a pattern
Trend
Outlier
Cluster
Detect which
variables explain It
Not important
Important
Marginally
important
Detect variable
relative importance
Variable weight

8. Data-Driven Decisions Are Not Solely About New Technology
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9. Goal of Data-Driven Decisions
9

10. 10
Goal of Data-Driven Decisions

11. Predictive Analytics System
Need more than a modeling factory
Integrating Data
• Access to 100s of data
sources
• Seamless inclusion of new
sources for new analyses
• Merging, filtering,
aggregating, deriving,
transforming, sampling
• Embed data quality
• Often 2/3 of the effort!
Designed for the Analyst
• No coding or scripting
• Graphical user interface
• Workbench for advanced
analytics
• Stats analysis, visualizations,
transforms, models for pattern
discovery and model testing
• Rapid model creation!
Data Access Predictive Modeling Results Deployment
Social
Hot
Bad
Feedback
Self-Service for Everyone
• Automated information
delivery
• Interactive smart statements
• Interactive portal experience
• Dashboards, ad hoc reporting,
• Feeding downstream apps
• Deliver predicted results for
action!

12. Optimizing Decisions via Predictive Modeling
Increase products
used per customer
Increase prices /
fees
Decrease
processing costs
Improve collections/recovery rate
Proactive retention of profitable customers
Increase switching costs
Target more profitable segments
Increase cross / up sell volume
Differential Tariffing
Increase fee based income
Risk based pricing
Increase share of wallet
Decrease marketing costs
Decrease transaction processing costs
Decrease new account processing costs
Decrease customer service costs
Decrease delinquency rate
Decrease default rate
Increase activation rate
Decrease campaign cycle time
Increase Response Rate
Increase
Profits
Increase
revenues
Decrease
costs
Increase customer
acquisition
Increase customer retention
Grow customer lifetime
value
Decrease operating
expenses
Decrease charge-offs
Related ProgramsOrganizational Objectives

13. Manufacturing And Logistics – Predictive Part Failure
Use Case: Airline Maintenance Dashboard alerting station manager of likelihood of maintenance issues on specific
aircraft, when they arrive and is the part in stock or not.
Impact: Whether it vehicles transporting goods, service or people; or machines on a plant floor, predicting the
likelihood of part failure keeps operations running smoothly and improves service and saves on costs

14. Healthcare – Patient Volume and Staffing
Use Case: One of a Hospital’s biggest areas of expense are labor costs that are directly related to patient volume.
Patient volume is impacted by a number of factors, CDC Alerts, Weather conditions, Lunar Cycles, Special events
etc.
Impact: Predicting patient volume can help the hospital optimize staffing levels, which helps better manage labor
costs and improves the patients overall experience because they are less likely to wait for service.

15. Healthcare – Predictive Readmission Performance
Use Case: Predict and Score Patients Most Likely to Readmit based on Previous Clinical History based on Demographics, Consumer
Habits and Clinical Factors
Impact: Per CMS readmissions within 30 days for many conditions will not reimbursed therefore it is incumbent on hospitals to
proactively reduce readmissions and ED “Frequent Flyers.” Improved discharge procedures and transitions to post acute care.

16. Insurance - Claim Analysis and Fraud Detection
Use Case: Both Warranty and Insurance Claims have a mix of structured and unstructured data that are useful for identifying fraud
Impact: Being able use both types of data in your analysis will help organizations become more efficient in
identifying possible fraud or abuse.

17. Higher Education – Student Retention
Use Case: Successful enrollment management includes retaining current students. Often institutions have not used available data to
understand who stays and who leaves.
Impact: The ability to identify and develop profiles of students who are most likely to leave your school and then recommend
intervention strategies to reduce student attrition

18. Government - Predictive Policing
Use Case: Local agencies such as police departments can leverage advanced, real-time analytics to provide actionable
intelligence.
Impact: Understand criminal behavior, identify crime/incident patterns, and uncover location-based threats.

19. Use Data to Drive Better Business Decisions
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20. Goal
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21. Thank you
The value of your information assets is maximized when you
implement: (1) a fully integrated data experience, (2) a systematic
data quality and integrity validation, (3) pervasive business
intelligence, and (4) robust analytics for every decision maker.