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Big Data Trends
The document discusses the growth and transformation of the big data market, highlighting consumer-scale demand and advancements in technology. It outlines key statistics, laws, and insights related to big data across various industries, emphasizing the importance of real-time analysis and diverse data use. It also identifies emerging trends and applications that are reshaping how businesses leverage big data for operational efficiency and decision-making.
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Big Data Trends
- 1. BIG DATA TRENDS 11/15/2012 DavidFeinleib THE BIG DATA GROUP THEBIGDATAGROUP.COM
- 2. WHAT IS BIGDATA? THEBIGDATAGROUP.COM
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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 THEBIGDATAGROUP.COM 6
- 7. BASIC STATS -BIG DATA MARKET ROBUST, CONSUMER SCALE FUELING TECH ADOPTION METADATA GROUP THEBIGDATAGROUP.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 8 Copyright © 2012 Dave Feinleib dave@thebigdatagroup.com bigdatalandscape.com
- 9. MARKET GROWTH METADATAGROUP THEBIGDATAGROUP.COM 9
- 10. Big Data OverallRevenue $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 THEBIGDATAGROUP.COM 10
- 11. Big Data Pure-PlayRevenue $468M in ’11 THEBIGDATAGROUP.COM 11
- 12. Big Data OverallRevenue $53.4B By 2017 Billions Source: Wikibon THEBIGDATAGROUP.COM 12
- 13. DATA GROWTH METADATAGROUP THEBIGDATAGROUP.COM 13
- 14. Facebook at 1BUsers 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 THEBIGDATAGROUP.COM 14
- 15. Twitter at 400MTweets 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 THEBIGDATAGROUP.COM 15
- 16. 94% Corporate DataGrowth Y/Y Area Growth Rate Database systems 97% Overall corporate data 94% Data of average organization 50% Source: Forrester THEBIGDATAGROUP.COM 16
- 17. Big Data: ByThe 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 THEBIGDATAGROUP.COM
- 18. Worldwide Data Growthat 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 OurSenses THEBIGDATAGROUP.COM Source: Hans Norretranders
- 20. Data Sensed PerYear 0.04 EB 7,910 EB 200,000X 1EB = 1 Billion GB THEBIGDATAGROUP.COM
- 21. THE BIG DATACYCLE METADATA GROUP THEBIGDATAGROUP.COM 21
- 22. As Efficiency Increases,So Does Consumption Source: Cloudyn THEBIGDATAGROUP.COM 22
- 23. The Big DataCycle 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 THEBIGDATAGROUP.COM 23
- 24. 6 Insights FromFacebook’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 THEBIGDATAGROUP.COM 24
- 25. BIG DATA TRANSFORMINGBUSINESS METADATA GROUP THEBIGDATAGROUP.COM 25
- 26. Transformation of Retail THEN... NOW... Data driven pricing and Sales recommendations THEBIGDATAGROUP.COM Image: webdesignerdepot.com 26
- 27. Transformation of OnlineMarketing 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 THEBIGDATAGROUP.COM 27
- 28. Transformation of IT THEN... NOW... Log files Operational intelligence THEBIGDATAGROUP.COM Image: blog.getsocialize.com/2012/location-showcase 28
- 29. Transformation of CustomerService THEN... NOW... Unhappy Customer insight customers THEBIGDATAGROUP.COM Chart: Zendesk 29
- 30. Transformation of Billing THEN... NOW... Manual coding Intelligent coding THEBIGDATAGROUP.COM 30
- 31. Transformation Of FraudManagement THEN... NOW... Credit databases Social profiles THEBIGDATAGROUP.COM 31
- 32. Transformation Of OperationsManagement THEN... NOW... Automated Operations? operations THEBIGDATAGROUP.COM Images: Jalopnik.com, SFPark 32
- 33. Transformation of LawEnforcement THEN... NOW... Crime hotspot Gut instinct prediction THEBIGDATAGROUP.COM Images: Fanpop.com, paleo.sscnet.ucla.edu 33
- 34. Transformation of MedicalResearch 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 THEBIGDATAGROUP.COM Image: Store.Nike.com 35
- 36. TOP 8 LAWSOF BIG DATA METADATA GROUP THEBIGDATAGROUP.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 GROUP THEBIGDATAGROUP.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 MarketWatch THEBIGDATAGROUP.COM
- 47. Design Informs Data “Visualization is a form of knowledge compression” - David McCandless THEBIGDATAGROUP.COM
- 48. Big Data VersusRomance THEBIGDATAGROUP.COM
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- 50. Big Data WhiteSpace 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 THEBIGDATAGROUP.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 easier THEBIGDATAGROUP.COM 51
- 52. Contact David Feinleib dave@thebigdatagroup.com THEBIGDATAGROUP.COM 52
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- #5 The amount of detail is so impressive you might think this requires an elaborate and expensive set of monitoring gear. \n
- #6 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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