This document discusses how big data and HP's HAVEn analytics platform can be used by various industries and organizations. It provides examples of how NASCAR uses big data to better understand fans, how Guess uses it to optimize store layout and inventory, and how HP uses it for customer service and improving products. The document also discusses using big data for operations purposes like fixing complex IT problems, optimizing systems like the London Police, and detecting fraud through credit card transaction analysis.

1. © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.1
Mike Shaw
Director, HP Software marketing
Baveno, Italy
June 17th
HP Software
Performance Tour 2014

2. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.2
HP Big Data
What is big data?
Using it to get closer to customers
Using it to do better operations

3. Much higher
Volumes.
Processed
with more
Velocity.
With much
more Variety.

4. Regular structured data – but more
Longer
period
Faster
analysis

5. 2010 2015
Micro-transactions from machines
McKinsey : Big Data – The next frontier for
innovation, competition and productivity
Automotive
Utilities
Travel / logistics
Security
Retail
The Internet of Things
• Medical equipment
• Utility networks and meters
• Car and truck fleets
• Security sensors
• Home automation
• Touch-streams from games
• Drones
• Pollution sensors
• Transport sensors

6. Meaning from human interaction
Social media
Images
Video
Audio
Email
Documents

7. HAVEn : 360 Degree Big Data – no Blind Spots
Transactions Social media Images AudioVideoEmail Documents TextsHadoop
Catalogue massive
volumes of
distributed data
Meaning from
human interaction
Analyze at extreme
scale in real-time
Hadoop VerticaAutonomy Vertica
Enterprise
Security
Collect and unify
machine data
Data Lake

8. Customers
Know your customers better
Design better products
How is HAVEn
being used?

9. Get closer to your customers : NASCAR
“... takes fan data, collects, it stores, and stitches
it together…that helps us understand what
is being talked about across the ecosystem
of the sport.”
Steve Phelps, Senior VP and CMO, NASCAR
Understands sentiment, identifies
emerging issues, and uncovers trends that
help the NASCAR team share and enrich the
fan and broadcast experience
Race-day
displays
FOX
Broadcasting
Fan
Site

10. Targeted marketing : Guess
• Analysis from all stores and online
• What’s trending up?
• What’s not selling?
• What “has affinity” with what?
Arrange the store to maximize sales
and minimize inventory wastage
Bought Skirt
Buy Shoes –
54% probability

11. Adding human interaction to your CRM
HP Service Anywhere and Service Manager both use HP Big Data
DSL random reset
1 CTI : auto-
grab documents
2 Plain text search
3 Hot topic
clusters

12. Better products: “Everything is an experiment”
Build
Limited
Deploy
& Test
Analyze
Full
Deploy
Design
Collect
1-5 days 1-10 days 1-3days
3-30 days1-2 days
“... we view our online games as experiments. Big data
allows us to quickly and accurately improve our games,
based on how our customers experience them”
Portman Wills, Chief Data Monger, GSN.com

13. a
HP Digital Marketing Hub
Answering the key questions for marketers
Who are our best customers?
Which customer attributes can I target to get better results?
What type of content drives desired customer behavior?
What does the typical customer journey look like?
How do my multiple touch points combine to drive conversions?
Product affinities?

14. Operations
Fixing complex problems
System optimization
Fraud, cheating and compliance
How is HAVEn
being used?

15. Record everything in IT
Correlate using service map
Go back in time to find problem start
point
Fixing complex problems: HP Operations Analytics

16. System optimization :
London Police working with communities
Massive amounts of social media traffic
(30 sources, 3.3 million tweets per day)
Clusters
Detects community sentiment
Identifies key influencers in
the community

17. Fraud : Card skimming
Credit card skimming - 15% of credit card fraud
Regression analysis to identify when skimming
occurred – billions of transactions
Identify merchants most likely responsible –
tens of thousands of merchants
Use case applicable to payment processing

18. Email compliance analysis : ArcSight + IDOL
IDOL “judgment”
ArcSight

19. © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.19