1. BIG DATA TRENDS
11/15/2012
David Feinleib
THE BIG DATA GROUP THEBIGDATAGROUP.COM

2. WHAT IS BIG DATA?
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3. THEBIGDATAGROUP.COM

4. THEBIGDATAGROUP.COM

5. +
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6. Outline
1) Landscape - Number of Big Data
Companies and Categories Is Growing
2) Basic Stats - Consumer Scale Driving Big
Data Transformation
3) Transformation - Many Industries
4) Top 8 Laws Of Big Data
5) Big Data Whitespace
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7. BASIC STATS - BIG DATA MARKET ROBUST,
CONSUMER SCALE FUELING TECH ADOPTION
METADATA GROUP
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8. Apps
Vertical Ad/Media Business Analytics and
Intelligence Visualization
Operational Intelligence
Data As A Service
Infrastructure
Analytics Operational As A Service Structured DB
Technologies
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Copyright © 2012 Dave Feinleib dave@thebigdatagroup.com bigdatalandscape.com

9. MARKET GROWTH
METADATA GROUP
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10. Big Data Overall Revenue $5.1B in ’11
Vendors With Big Data Revenues Over $100M
IBM
Intel
HP
Fujitsu
Accenture
CSC
Dell
Seagate
EMC
Teradata
Amazon
SAS
Capgemini
Hitachi
0 250 500 750 1000
Source: Wikibon
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11. Big Data Pure-Play Revenue $468M in ’11
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12. Big Data Overall Revenue $53.4B By 2017
Billions
Source: Wikibon
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13. DATA GROWTH
METADATA GROUP
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14. Facebook at 1B Users in Oct ’12
1000
750
Users
In Millions
500
250
Source: Benphoster.com
0
Dec 04 Apr 07 Jan 09 Jul 09 Feb 10 Jan 11 Sep 11 Oct 12
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15. Twitter at 400M Tweets Per Day in Jun ’12
400
300
Tweets
Per Day In 200
Millions
100
Source: Twitter blog and news reports
0
Jan 07 Jan 08 Oct 09 Sep 10 Jun 11 Oct 11 Jun 12
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16. 94% Corporate Data Growth Y/Y
Area Growth Rate
Database systems 97%
Overall corporate data 94%
Data of average organization 50%
Source: Forrester
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17. Big Data: By The Numbers
• Walmart handles 1M transactions per hour
• Google processes 24PB of data per day
• AT&T transfers 30PB of data per day
• 90 trillion emails are sent per year
• World of Warcraft uses 1.3PB of storage
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18. Worldwide Data Growth at 7.9EB / Yr in ‘15
8000
6000
Exabytes
4000
2000
0
2005 2010 2015
Source: IDC, EMC. 1EB = 1 Billion GB.
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19. Bandwidth of Our Senses
THEBIGDATAGROUP.COM Source: Hans Norretranders

20. Data Sensed Per Year
0.04 EB 7,910 EB
200,000X
1EB = 1 Billion GB
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21. THE BIG DATA CYCLE
METADATA GROUP
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22. As Efficiency Increases, So Does Consumption
Source: Cloudyn
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23. The Big Data Cycle
1 Consumer scale and
1)
speed requirements have
introduced new
technologies that have
increased the efficiency of
22) This increased efficiency
is leading to adoption of Big
using Big Data
Data across a wide variety
of industries
33) This results in the
generation and
consumption of more
data
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24. 6 Insights From Facebook’s
Former Head Of Big Data
Analytics on 900M users
25PB of compressed data - 125PB uncompressed
1) “What data to store” => “What can we do with more data”
2) Simplify data analytics for end users
3) More users means analytics systems have to be more robust
4) Social networking works for Big Data
5) No single infrastructure can solve all Big Data problems
6) Building software is hard; running a service is even harder
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25. BIG DATA TRANSFORMING BUSINESS
METADATA GROUP
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26. Transformation of Retail
THEN... NOW...
Data driven pricing and
Sales
recommendations
THEBIGDATAGROUP.COM Image: webdesignerdepot.com 26

27. Transformation of Online Marketing
THEN... NOW...
Marketing and Sales
Leads
Recommendations
Company First Last Oppty Created
Acme Fred Langan $250K 6/08/12
BigCo Tom Jones $100K 6/17/12
Campaign
DealCo Jan Sedor $50K 7/01/12
Recommendations
Stor Works Liza Grear $750K 7/14/12
RF Group Carl Tomer $47K 7/18/12
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28. Transformation of IT
THEN... NOW...
Log files Operational intelligence
THEBIGDATAGROUP.COM Image: blog.getsocialize.com/2012/location-showcase 28

29. Transformation of Customer Service
THEN... NOW...
Unhappy
Customer insight
customers
THEBIGDATAGROUP.COM Chart: Zendesk 29

30. Transformation of Billing
THEN... NOW...
Manual coding Intelligent coding
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31. Transformation Of Fraud Management
THEN... NOW...
Credit databases Social profiles
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32. Transformation Of Operations Management
THEN... NOW...
Automated
Operations?
operations
THEBIGDATAGROUP.COM Images: Jalopnik.com, SFPark 32

33. Transformation of Law Enforcement
THEN... NOW...
Crime hotspot
Gut instinct
prediction
THEBIGDATAGROUP.COM Images: Fanpop.com, paleo.sscnet.ucla.edu 33

34. Transformation of Medical Research
THEN... NOW...
Keyword
Relevance
searches
THEBIGDATAGROUP.COM Images: PubMed, Atigeo 34

35. Transformation of Fitness
THEN... NOW...
Manual
Goals +
tracking
measurement
Walk 45 minutes
Smoothie
Lift weights 20 minutes
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36. TOP 8 LAWS OF BIG DATA
METADATA GROUP
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37. BIG DATA LAW #1
The faster you analyze your data,
the greater its predictive value.
Companies are moving away from batch
processing to real-time to gain competitive
advantage.
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38. BIG DATA LAW #2
Maintain one copy of your data, not
dozens. The more you copy and move your
data, the less reliable it becomes (example:
banking crisis).
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39. BIG DATA LAW #3
Use more diverse data, not just
more data. More diverse data leads to
greater insights. Combining multiple data
sources can lead to the most interesting
insights of all.
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40. BIG DATA LAW #4
Data has value far beyond what you
originally anticipate. Don’t throw it
away.
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41. BIG DATA LAW #5
Plan for exponential growth. The
number of photos, emails, and IMs while
large, is limited by the number of people.
Networked “sensor” data from mobile
phones, GPS, and other devices is much
larger.
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42. BIG DATA LAW #6
Solve a real pain point. Don’t think of
Big Data as a stand-alone new, shiny,
technology. Think about your core business
problems and how to solve them by
analyzing Big Data.
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43. BIG DATA LAW #7
Put data and humans together to
get the most insight. More data alone
isn’t sufficient. Look for ways to broaden the
use of data across your organization.
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44. BIG DATA LAW #8
Big Data is transforming business
the same way IT did. Those that fail to
leverage the numerous internal and external
data sources available will be leapfrogged by
new entrants.
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45. D2: DATA + DESIGN
METADATA GROUP
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46. Data Informs Design
“The racing technology on the yachts competing for
the America’s Cup will be the most advanced ever”
- The Wall Street Journal MarketWatch
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47. Design Informs Data
“Visualization is a form of knowledge compression”
- David McCandless
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48. Big Data Versus Romance
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49. SUMMARY
METADATA GROUP
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50. Big Data White Space
1) Visualization - cloud, mobile, collaboration
2) Big Data Apps - verticals
3) Trend analysis across multiple data sources
4) Consumer behavior
5) Public data for scoring
6) New information / data service businesses
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51. Summary
1) Consumer company speed and scale requirements driving
efficiencies in Big Data storage and analytics
2) Quantity of machine data vastly increasing (examples:
networked sensor data from mobile phones and GPS devices)
3) Move away from batch to real-time
4) Cloud services opening Big Data to all
5) New and broader number of data sources being meshed
together
6) Big Data Apps (BDAs) means using Big Data is faster and
easier
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52. Contact
David Feinleib
dave@thebigdatagroup.com
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