7de BI congres van het BICC-Thomas More: 3 april 2014 Geen meningen maar Feiten! Big Data heeft de wereld van BI en Analytics veranderd. Of toch niet? Wat is nog altijd hetzelfde en wat is er veranderd? Wat is er vandaag voor nodig om een volledig data gedreven organisatie te worden? Ik zal laten zien hoe bedrijven als Netflix, Full Tilt Poker, en Wells Fargo nieuwe en bestaande technologien gebruiken om hun bedrijven te draaien en te verbeteren.

1. 1 4/21/2014
FACTS NOT OPINIONS
BUSINESS ANALYTICS IN ACTION
Tobias Temmink
Tobias.Temmink@Teradata.com
nl.linkedin.com/in/tobiastemmink/
@TobiasTemmink

2. 2 4/21/2014 Teradata Confidential
“To meaure is to know”
Lord Kelvin
David Kirkaldy
“Measure what is measurable, and
make measurable what is not so”
Galileo Galilei

3. 3 4/21/2014 Teradata Confidential
DATA INSIGHT ACTION

4. 4 4/21/2014
A large global bank
reduced customer churn
amongst profitable
customer segment
– Integrated data from multiple
channels into a single
enterprise view
– Identified most frequent path
to account closure across all
customer interactions
Teradata UDA
– Teradata EDW for historical
customer transaction, profile
and product information
– Teradata Aster to discover
actions leading to account
closure
– Hadoop for loading, storing
and refining data
– Teradata Applications to make
right offers at the right time
preventing account closure and
growing the customer
relationship

5. 5 4/21/2014
Reduce Musculoskeletal Surgical Costs
Objective: Increase the percentage of members incorporating low-risk and
cost-effective care plans with early intervention within the medical life cycle of
members with musculoskeletal diagnoses.
Approach:
Use the Teradata Aster Path Analysis modules to identify members trending towards
medical care cycles resulting in high-cost musculoskeletal surgery. Results will be
incorporated into care management/case management application for outreach.

6. 6 4/21/2014
Path Prediction Methodology
• Use Aster out-of-the-box and
custom Path and Pattern SQL-MR
functions to create a set of
frequently occurring patterns.
• The initial input data set is
essentially a “training set” where
the outcome is already known.
• Use either nPath or a custom
pathing function to pore over one
or more data segments in search of
interesting paths.
• Path statistics include the number
of individuals following each path
as well as significant timestamps.

7. 7 4/21/2014
Frequent PROC CODES Preceding Back Surgery
• In the visualization above, the GREEN represents the average number of days from the first recorded visit to the
beginning of the pattern, the BLUE represents the average number of days from the beginning of the pattern to the
end of the pattern and the ORANGE represents the average number of days from the end of the pattern to the date
of the surgical procedure.

8. 8 4/21/2014
Netflix
Kurt Brown – Director data Platform at Netflix :
Netflix Webinar
“No magic algorithm for all your analysis.”
Example analysis they do:
• AB Testing
• Most popular list -> Don’t look just at popularity

9. 9 4/21/2014
Full Tilt Poker
Big Data Problem Big Graph Model/Analytics
Social Network Analytics
People are nodes; relationship/interactions are edges. Find social
communities, influencers, bridge people,
Fraud Detection
Companies are graph nodes; transactions/interactions are edges. Find the
potential fraudulent companies
Money Laundering
Bank accounts are graph nodes; money transfers are bank edges. Find
possible participants, “sinks” where money exits the system
Product Recommendation
Products and customers are nodes; purchase/browsing, customer
relationship are edges; find products purchased together, find “bridge”
products, who purchases similar products
Text/Email Analytics
Emails (email nodes) are connected to senders/receivers (people nodes)
and words they contain (word nodes). Find interaction pattern for
organization optimization; find code violation
Many business problems can be modeled as
Graph problems and better solved by graph
analytics

10. 10 4/21/2014
1993

11. 11 4/21/2014

12. Math
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