BI congres 2014-3: facts not opinions - Tobias Temmink - Teradata
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1 4/21/2014 FACTS NOT OPINIONS BUSINESS ANALYTICS IN ACTION Tobias Temmink [email protected] nl.linkedin.com/in/tobiastemmink/ @TobiasTemmink
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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.
Transcript of BI congres 2014-3: facts not opinions - Tobias Temmink - Teradata
1 4/21/2014
FACTS NOT OPINIONS
BUSINESS ANALYTICS IN ACTION
Tobias Temmink
[email protected]/in/tobiastemmink/@TobiasTemmink
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 4/21/2014 Teradata Confidential
DATA INSIGHT ACTION
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 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 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 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 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 4/21/2014
Full Tilt Poker
Big Data Problem Big Graph Model/Analytics
Social Network AnalyticsPeople are nodes; relationship/interactions are edges. Find social communities, influencers, bridge people,
Fraud DetectionCompanies are graph nodes; transactions/interactions are edges. Find the potential fraudulent companies
Money LaunderingBank accounts are graph nodes; money transfers are bank edges. Find possible participants, “sinks” where money exits the system
Product RecommendationProducts and customers are nodes; purchase/browsing, customer relationship are edges; find products purchased together, find “bridge” products, who purchases similar products
Text/Email AnalyticsEmails (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 4/21/2014
1993
11 4/21/2014
Math
and Stats
Data
Mining
Business
Intelligence
Applications
Languages
Marketing
ANALYTIC TOOLS & APPS
USERS
INTEGRATED DISCOVERY PLATFORM
INTEGRATED DATA WAREHOUSE
ERP
SCM
CRM
Images
Audio
and Video
Machine
Logs
Text
Web and
Social
SOURCES
DATAPLATFORM
ACCESSMANAGEMOVE
TERADATA UNIFIED DATA ARCHITECTURESystem Conceptual View
Marketing
Executives
Operational
Systems
Frontline
Workers
Customers
Partners
Engineers
Data
Scientists
Business
Analysts
TERADATA DATABASE
HORTONWORKS
TERADATA DATABASE
TERADATA ASTER DATABASE
