Big Data Trends
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A presentation on Big Data Trends. Covers market growth, industry transformation, the 3 I's of Big Data, and the 8 Laws of Big Data.

A presentation on Big Data Trends. Covers market growth, industry transformation, the 3 I's of Big Data, and the 8 Laws of Big Data.

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  • The amount of detail is so impressive you might think this requires an elaborate and expensive set of monitoring gear. \n
  • But unbelievably this can all be captured using a low cost off the shelf device combined with the online Garmin connect software. This truly epitomizes Big Data.\n
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Big Data Trends Presentation Transcript

  • 1. BIG DATA TRENDS11/15/2012David FeinleibTHE BIG DATA GROUP THEBIGDATAGROUP.COM
  • 2. WHAT IS BIG DATA?THEBIGDATAGROUP.COM
  • 3. THEBIGDATAGROUP.COM
  • 4. THEBIGDATAGROUP.COM
  • 5. +THEBIGDATAGROUP.COM
  • 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 WhitespaceTHEBIGDATAGROUP.COM 6
  • 7. BASIC STATS - BIG DATA MARKET ROBUST, CONSUMER SCALE FUELING TECH ADOPTION METADATA GROUPTHEBIGDATAGROUP.COM 7
  • 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 8Copyright © 2012 Dave Feinleib dave@thebigdatagroup.com bigdatalandscape.com
  • 9. MARKET GROWTH METADATA GROUPTHEBIGDATAGROUP.COM 9
  • 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: WikibonTHEBIGDATAGROUP.COM 10
  • 11. Big Data Pure-Play Revenue $468M in ’11THEBIGDATAGROUP.COM 11
  • 12. Big Data Overall Revenue $53.4B By 2017Billions Source: Wikibon THEBIGDATAGROUP.COM 12
  • 13. DATA GROWTH METADATA GROUPTHEBIGDATAGROUP.COM 13
  • 14. Facebook at 1B Users in Oct ’12 1000 750 UsersIn Millions 500 250 Source: Benphoster.com 0 Dec 04 Apr 07 Jan 09 Jul 09 Feb 10 Jan 11 Sep 11 Oct 12 THEBIGDATAGROUP.COM 14
  • 15. Twitter at 400M Tweets Per Day in Jun ’12 400 300 TweetsPer 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 THEBIGDATAGROUP.COM 15
  • 16. 94% Corporate Data Growth Y/Y Area Growth Rate Database systems 97% Overall corporate data 94% Data of average organization 50% Source: ForresterTHEBIGDATAGROUP.COM 16
  • 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 storageTHEBIGDATAGROUP.COM
  • 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.THEBIGDATAGROUP.COM
  • 19. Bandwidth of Our SensesTHEBIGDATAGROUP.COM Source: Hans Norretranders
  • 20. Data Sensed Per Year 0.04 EB 7,910 EB 200,000X 1EB = 1 Billion GBTHEBIGDATAGROUP.COM
  • 21. THE BIG DATA CYCLE METADATA GROUPTHEBIGDATAGROUP.COM 21
  • 22. As Efficiency Increases, So Does Consumption Source: CloudynTHEBIGDATAGROUP.COM 22
  • 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 dataTHEBIGDATAGROUP.COM 23
  • 24. 6 Insights From Facebook’sFormer 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 harderTHEBIGDATAGROUP.COM 24
  • 25. BIG DATA TRANSFORMING BUSINESS METADATA GROUPTHEBIGDATAGROUP.COM 25
  • 26. Transformation of Retail THEN... NOW... Data driven pricing and Sales recommendationsTHEBIGDATAGROUP.COM Image: webdesignerdepot.com 26
  • 27. Transformation of Online Marketing THEN... NOW... Marketing and Sales Leads RecommendationsCompany 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 RecommendationsStor Works Liza Grear $750K 7/14/12RF Group Carl Tomer $47K 7/18/12THEBIGDATAGROUP.COM 27
  • 28. Transformation of IT THEN... NOW... Log files Operational intelligenceTHEBIGDATAGROUP.COM Image: blog.getsocialize.com/2012/location-showcase 28
  • 29. Transformation of Customer Service THEN... NOW... Unhappy Customer insight customersTHEBIGDATAGROUP.COM Chart: Zendesk 29
  • 30. Transformation of Billing THEN... NOW... Manual coding Intelligent codingTHEBIGDATAGROUP.COM 30
  • 31. Transformation Of Fraud Management THEN... NOW... Credit databases Social profilesTHEBIGDATAGROUP.COM 31
  • 32. Transformation Of Operations Management THEN... NOW... Automated Operations? operationsTHEBIGDATAGROUP.COM Images: Jalopnik.com, SFPark 32
  • 33. Transformation of Law Enforcement THEN... NOW... Crime hotspot Gut instinct predictionTHEBIGDATAGROUP.COM Images: Fanpop.com, paleo.sscnet.ucla.edu 33
  • 34. Transformation of Medical Research THEN... NOW... Keyword Relevance searchesTHEBIGDATAGROUP.COM Images: PubMed, Atigeo 34
  • 35. Transformation of Fitness THEN... NOW... Manual Goals + tracking measurement Walk 45 minutes Smoothie Lift weights 20 minutesTHEBIGDATAGROUP.COM Image: Store.Nike.com 35
  • 36. TOP 8 LAWS OF BIG DATA METADATA GROUPTHEBIGDATAGROUP.COM 36
  • 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.THEBIGDATAGROUP.COM 37
  • 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).THEBIGDATAGROUP.COM 38
  • 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.THEBIGDATAGROUP.COM 39
  • 40. BIG DATA LAW #4 Data has value far beyond what you originally anticipate. Don’t throw it away.THEBIGDATAGROUP.COM 40
  • 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.THEBIGDATAGROUP.COM 41
  • 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.THEBIGDATAGROUP.COM 42
  • 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.THEBIGDATAGROUP.COM 43
  • 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.THEBIGDATAGROUP.COM 44
  • 45. D2: DATA + DESIGN METADATA GROUPTHEBIGDATAGROUP.COM 45
  • 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 MarketWatchTHEBIGDATAGROUP.COM
  • 47. Design Informs Data “Visualization is a form of knowledge compression” - David McCandlessTHEBIGDATAGROUP.COM
  • 48. Big Data Versus RomanceTHEBIGDATAGROUP.COM
  • 49. SUMMARY METADATA GROUPTHEBIGDATAGROUP.COM 49
  • 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 businessesTHEBIGDATAGROUP.COM 50
  • 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 easierTHEBIGDATAGROUP.COM 51
  • 52. Contact David Feinleib dave@thebigdatagroup.comTHEBIGDATAGROUP.COM 52