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Cloud_Big_Data_Analytics_Mobile_Social_modern_internet_scale_business_models_2014_John_Sing
 

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John Sing's latest April 2014 technology presentation overviewing the interaction of Cloud, Big Data Analytics, Mobile, and Social technologies upon today's business models. Competitive Advantage ...

John Sing's latest April 2014 technology presentation overviewing the interaction of Cloud, Big Data Analytics, Mobile, and Social technologies upon today's business models. Competitive Advantage value in today's business models comes from the proper *blending* of these technologies, in the proper way unique to one's business model. We discuss the Journey from Data to Value, provide big picture review of these technology's impact on today's world. My goal here is to provide a holistic overview of technologies for multiple business audiences to understand. You may use this material to further your own business goals. All I ask is that you give full attribution to me or to the original authors (attributed on each slide), use proper business ethics, courtesy. If you are further interested in what I have to say / contribute, please contact me! http://www.johnsing.us

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    Cloud_Big_Data_Analytics_Mobile_Social_modern_internet_scale_business_models_2014_John_Sing Cloud_Big_Data_Analytics_Mobile_Social_modern_internet_scale_business_models_2014_John_Sing Presentation Transcript

    • © 2014 John Sing – All Rights Reserved Big Data: the Big Picture For your 2014+ Business and Career Opening video John Sing, Executive IT Consultant http://johnsing.us
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 2 John Sing  32 years of experience in enterprise servers, storage, and software – 2009 – 2014: IBM Executive IT Consultant: IT Strategy and Planning, Enterprise Large Scale Storage, Internet Scale Workloads and Data Center Design, Big Data Analytics, HA/DR/BC – 2002-2008: IBM IT Data Center Strategy, Large Scale Systems, Business Continuity, HA/DR/BC, IBM Storage – 1998-2001: IBM Storage Subsystems Group - Enterprise Storage Server Marketing Manager, Planner for ESS Copy Services (FlashCopy, PPRC, XRC, Metro Mirror, Global Mirror) – 1994-1998: IBM Hong Kong, IBM China Marketing Specialist for High-End Storage – 1989-1994: IBM USA Systems Center Specialist for High-End S/390 processors – 1982-1989: IBM USA Marketing Specialist for S/370, S/390 customers (including VSE and VSE/ESA)  john@johnsing.us  http://johnsing.us  Follow my daily IT research blog – http://www.delicious.com/atsf_arizona  Follow me on Slideshare.net: – http://www.slideshare.net/johnsing1  LinkedIn: – http://www.linkedin.com/in/johnsing
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 3  Data, the new natural Resource  Big Data in context:  Cloud, Analytics, Mobil, Social  Innovating using Big Data:  Monetizing, innovating, creating competitive advantage out of Big Data Agenda
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 4 1. Data + Analytics = Information 2. Information + Context = Insight 3. Insight + Actions = Desired Outcomes Today’s message: The Big Data Journey to Value
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 5  Data, the new natural Resource Data, the new natural resource
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 6 Time ComputingPowerGrowth Traditional IT “sensemaking” capability Available data for observation What we see in the world today…… Chart by: Jeff Jonas/Las Vegas/IBM, Chief Scientist, IBM SWG Entity Analytics http://jeffjonas.typepad.com/
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 7 Time ComputingPowerGrowth Traditional IT “sensemaking” capability Available data for observation Context Enterprise Amnesia What we see in the world today ……….. Chart by: Jeff Jonas/Las Vegas/IBM, Chief Scientist, IBM SWG Entity Analytics http://jeffjonas.typepad.com/
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 8 Enterprise Amnesia, definition A defect in memory, resulting in missed opportunity, wasted resources, lower revenues, unnecessary fraud losses, and other bad news.
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 9 Time ComputingPowerGrowth Traditional IT “sensemaking” capabilities Available Observation Space Because traditional IT methods could not keep pace WHY? Chart by: Jeff Jonas/Las Vegas/IBM, Chief Scientist, IBM SWG Entity Analytics http://jeffjonas.typepad.com/
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 10 Time ComputingPowerGrowth This is the Big Data Opportunity Add: Big Data Sensemaking Algorithms Available Observation Space Context Big Data capability New/Useful Information Data Analytics
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 11 Think of the Gold Mine analogy – in the “Olden Days”  Miners could actually see nuggets / veins of gold  There was much more gold out there…. – but it wasn’t visible to naked eye…  It was a big gambling game – You dig like crazy, but you’ve no idea where more gold will be found
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 12 In the “olden days”, no one could afford to dig everywhere Where gold is mined on Earth (as of 2006) Despite gold rush fevers, no one could afford to mobilize millions of people to dig everywhere
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 13 Gold mining in 2012:  Massive capital equipment  Millions of tons of dirt  Ore of 30 mg/kg (30 ppm) – Needed to even see the gold  By using the right equipment  On a massive scale  We can process lots of dirt affordably and keep the gold we find That’s like Big Data!
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 14 Processor power: Google, Yahoo, Facebook surpassed the Supercomputer community in compute power and scale…… in 2008  Google in 2012: – 200+ petaflops – Processes 1 TB / hour – 2003: Batch – 2005: Warehouse – 2011: Instant – Dumped MapReduce – Wrote replacement real-time indexing (“Percolator”) – Click here for architecture  Facebook in 20 Minutes in 2012 – 30 PB cluster of storage – 2.7M Photos, 10.2M Comments, 4.6M Messages – Facebook's New Realtime Analytics System: Hadoop HBase To Process 20 Billion Events Per Day May 21, 2008: http://www.circleid.com/posts/85218_google_surpasses_supercomputer/ http://highscalability.com real life internet architectures http://highscalability.com/display/Search?searchQuery=facebook&moduleId=4876569
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 15 Hmmmm. What might we find in all this data? And How? Cisco estimate: by 2015, will be annual 4,8 zettabytes of data center traffic flowing through Internet, Only 5% will be traditional OLTP database Data in existence today = 1,000 exabytes = 1 million petabytes http://venturebeat.com/2011/11/29/cisco-global-cloud-traffic/
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 16 Visualizing Big Data Source: Wikibon March 2011 Goal: Analyze *all* the data real time Original source: Wikibon.org, March 1, 2011 public broadcat on “Big Data”, http://wikibon.org/blog/ten-%E2%80%9Cbig-data%E2%80%9D-realities-and-what-they-mean-to-you/ Very large Distributed aggregation Loosely structured Often incomplete Sampling not strategically competitive
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 17 Visualizing Big Data…. Source: Wikibon March 2011 Original source: Wikibon.org, March 1, 2011 public broadcat on “Big Data”, http://wikibon.org/blog/ten-%E2%80%9Cbig-data%E2%80%9D-realities-and-what-they-mean-to-you/ Petabytes Exabytes Millions / Billions of people Billions / Trillions of records Time- stamped events Unknown inter- relationships Flat files
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 18 Visualizing Big Data….. Source: Wikibon March 2011 Original source: Wikibon.org, March 1, 2011 public broadcat on “Big Data”, http://wikibon.org/blog/ten-%E2%80%9Cbig-data%E2%80%9D-realities-and-what-they-mean-to-you/ Connections determined by probability Process entire (huge) data set Data generated by collective action over the Internet Open Source innovation It’s more than the algorithms….
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 19 It’s also: Source: Wikibon March 2011 Original source: Wikibon.org, March 1, 2011 public broadcat on “Big Data”, http://wikibon.org/blog/ten-%E2%80%9Cbig-data%E2%80%9D-realities-and-what-they-mean-to-you/ Its collaboration of algorithms Combined / Collaborated innovative ways A software Ecosystem is essentialOn a worldwide scale Multiple Worldwide “Pockets of Value”
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 20  Kevin Slavin at TEDGlobal July 2011  “How algorithms shape our world” http://www.ted.com/talks/kevin_slavin_how_algorithms_shape_our_world Visualizing what Algorithms are doing
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 21 Big Data and Hadoop: worldwide usage  eBay  Linkedin  Yahoo!  Facebook  Major Fortune 500 customers  Including all IBM industries: – Financial – Healthcare – M&E – Telecom – Utilities – Retail http://www.datanami.com/datanami/2012-04-26/six_super-scale_hadoop_deployments.html One source for Hadoop users (but not the only one!): http://wiki.apache.org/hadoop/PoweredBy
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 22 Hadoop is a well-developed ecosystem for Big Data app development  Hadoop – Overall name of software stack  HDFS – Hadoop Distributed File System  MapReduce – Software compute framework • Map = queries • Reduce=aggregates answers  Hive – Hadoop-based data warehouse  Pig – Hadoop-based language  Hbase – Non-relationship database fast lookups  Flume – Populate Hadoop with data  Oozie – Workflow processing system  Whirr – Libraries to spin up Hadoop on Amazon EC2, Rackspace, etc.  Avro – Data serialization  Mahout – Data mining  Sqoop – Connectivity to non-Hadoop data stores  BigTop – Packaging / interop of all Hadoop components http://wikibon.org/wiki/v/Big_Data:_Hadoop%2C_Business_Analytics_and_Beyond http://blog.cloudera.com/blog/2013/01/apache-hadoop-in-2013-the-state-of-the-platform/ http://www.ibm.com/developerworks/data/library/techarticle/dm-1209hadoopbigdata/
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 23 Visualizing why Hadoop was created for Big Data Traditional approach : Move data to program Big Data approach: Move function/programs to data Database server Data Query Data return Data process Data Master node Data nodes Data Application server User request Send result User request Send Function to process on Data Query & process Data Data nodes Data Data nodes Data Data nodes Data Send Consolidate result Traditional approach Application server and Database server are separate Analysis Program can run on multiple Application servers Network is still in the middle Data has to go through network Designed to analyze TBs of data •Big Data Approach  Analysis Program runs where the data is : on Data Node Only Analysis Program has to go through the network Analysis Program is executed on every DataNode Designed to analyze PBs of data Highly Scalable : 1000s Nodes Petabytes and more Thank you to: Pascal VEZOLLE/France/IBM@IBMFR and Francois Gibello/France/IBM for the use of this slide
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 24 Example of Hadoop in action Database server Data Query Data return Data process Data Application server User request Send result Master node Data nodes Data User request Send Function to process on Data Query & process Data Data nodes Data Data nodes Data Data nodes Data Send Consolidate result Example: How many hours of Clint Eastwood appears in all the movies he has done? Task: All movies need to be parsed to find Clint’s face •Traditional approach : 1)Upload a movie to the application server through the network 2) The Analysis Program compares Clint’s picture with every frame of the loaded movie. 3) Repeat the 2 previous steps for every movie •Big Data Approach : 1)Send the Analysis Program and Clint’s picture to all the DataNodes. 2) The Analysis Program in every DataNode (all in parallel) compares the Clint’s picture with every frame of the loaded movie. 3) The results of every DataNodes are consolidated. A unique result is generated. Traditional approach : Move data to program Big Data approach: Move function/programs to data Thank you to: Pascal VEZOLLE/France/IBM@IBMFR and Francois Gibello/France/IBM for the use of this slide
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 25 Hadoop in action – details:  Hadoop Distributed File System = HDFS : where Hadoop stores the data – HDFS file system spans all the nodes in a cluster with locality awareness  Hadoop data storage, computation model – Data stored in a distributed file system, spanning many inexpensive computers – Send function/program to the data nodes – i.e. distribute application to compute resources where the data is stored – Scalable to thousands of nodes and petabytes of data MapReduce Application 1. Map Phase (break job into small parts) 2. Shuffle (transfer interim output for final processing) 3. Reduce Phase (boil all output down to a single result set) Return a single result setResult Set Shuffle public static class TokenizerMapper extends Mapper<Object,Text,Text,IntWritable> { private final static IntWritable one = new IntWritable(1); private Text word = new Text(); public void map(Object key, Text val, Context StringTokenizer itr = new StringTokenizer(val.toString()); while (itr.hasMoreTokens()) { word.set(itr.nextToken()); context.write(word, one); } } } public static class IntSumReducer extends Reducer<Text,IntWritable,Text,IntWrita private IntWritable result = new IntWritable(); public void reduce(Text key, Iterable<IntWritable> val, Context context){ int sum = 0; for (IntWritable v : val) { sum += v.get(); . . . Distribute map tasks to cluster Hadoop Data Nodes Data is loaded, spread, resident in Hadoop cluster Performance = tuning Map Reduce workflow, network, application, servers, and storage http://www.ibm.com/developerworks/data/library/techarticle/dm-1209hadoopbigdata/ http://blog.cloudera.com/blog/2009/12/7-tips-for-improving-mapreduce-performance/ http://www.slideshare.net/allenwittenauer/2012-lihadoopperf
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 26 What is being done with Big Data today?
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 27 Humans are collecting useful data on massive scale Chart in public domain: IEEE Massive File Storage presentation, author: Bill Kramer, NCSA: http://storageconference.org/2010/Presentations/MSST/1.Kramer.pdf:
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 28 We are building real-time, integrated stream computing on massive scale Chart in public domain: IEEE Massive File Storage presentation, author: Bill Kramer, NCSA: http://storageconference.org/2010/Presentations/MSST/1.Kramer.pdf: n d Chapter 1
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 29 • Unlimited in amount, but you have to refine it • Basis of competitive advantage, no matter what industry • Every market being transformed by data Data is the new natural resource
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 30 Aerospace / defense transformation
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 31 Automobile transformation Ford: https://www.youtube.com/watch?v=nFUszkSv5X0
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 32 Energy & utilities transformation
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 33 Government transformation Miami-Dade County: https://www.youtube.com/watch?v=toL4Yx9WYPo Miami-Dade Police: https://www.youtube.com/watch?v=1b5RiPWd-Pw
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 34 Media and entertainment transformation
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 35 Predictive Analytics: Movement in a City •10 minute-ahead volume forecast (blue) vs. actual value (black) •10 minute-ahead speed forecast (blue) vs. actual value (black). Blue line: analytics prediction 10 minutes in advance Black line: actual result Stockholm: http://www.youtube.com/watch?v=rfMylzF4lv8
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 36 Predictive Analytics: Using Information to Ensure Public Safety: Blue CRUSH in Memphis, TN & Richmond, VA  Blue CRUSH predictive analysis for officer deployment & risk management generated easy-to-read crime maps every four hours  Richmond, VA: Violent crime decreased in the first year by 32%, another 40% thereafter, moving Richmond from #5 on the list of the most dangerous US cities to #99 Memphis Blue CRUSH Map Police videos: http://www.youtube.com/watch?v=8SJQtn4RO7I Play video https://www.youtube.com/watch?v=_xsffIAHY3I
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 37 A new class of data-rich industries has already emerged Yesterday’s Hyperscale Data Companies New business models: company’s value based on amount of information stored, exploited Today’s Hyperscale Data Companies Aerospace Banking Energy Government Healthcare Insurance Manufacturing Media and Entertainment Retail 3.5 PB in 2010 1 TB CT scanner → 2.5 PB/Year/Scanner 20 PB in 2011 Grow 300 TB per month, every month ExamplesIndustries Healthcare Provider Claims Processor
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 38 How much data?
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 39 1. Data + Analytics = Information 2. Information + Context = Insight 3. Insight + Actions = Desired Outcomes Solution: take Big Data on the Journey to Value
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 40 Data + Analytics = Information Information + context = Insight So…. What is “context”?
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 41 Time ComputingPowerGrowth Review: this is the Big Data Opportunity Add: Big Data Sensemaking Algorithms Available Observation Space Context Big Data Capability “context” New/Useful Information Data Analytics
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 42 No Context scrila34@msn.com
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 43 Context, definition Better understanding something by taking into account the things around it.
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 44 Information in Context … = Insights Top 200 Customer Job Applicant Identity Thief Criminal Investigation scrila34@msn.com
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 45 The Puzzle Metaphor: what we mean by “Context”  Imagine an ever-growing pile of puzzle pieces of varying sizes, shapes and colors  What it represents is unknown – there is no picture on hand  Is it one puzzle, 15 puzzles, or 1,500 different puzzles?  Some pieces are duplicates, missing, incomplete, low quality, or have been misinterpreted  Some pieces may even be professionally fabricated lies  Until you take the pieces to the table and attempt assembly, you don’t know what you are dealing with
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 46 Puzzling 270 pieces 90% 200 pieces 66% 150 pieces 50% 6 pieces 2% (pure noise) 30 pieces 10% (duplicates)
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 47
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 48
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 49 First Discovery
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 50 More Data Finds Data
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 51 Duplicates in Front Of Your Eyes
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 52 First Duplicate Found Here
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 53
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 54
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 55 Incremental Context – Incremental Discovery 6:40pm START 22min “Hey, this one is a duplicate!” 35min “I think some pieces are missing.” 37min “Looks like a bunch of hillbillies on a porch.” 44min “Hillbillies, playing guitars, sitting on a porch, near a barber sign … and a banjo!”
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 56 150 pieces 50%
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 57 Incremental Context – Incremental Discovery 47min “We should take the sky and grass off the table.” 2hr “Let’s switch sides, and see if we can make sense of this from different perspectives.” 2hr10m “Wait, there are three … no, four puzzles.” 2hr17m “We need a bigger table.” 2hr18m “I think you threw in a few random pieces.”
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 58
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 59
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 60
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 61 How Context Accumulates  With each new observation … one of three assertions are made: 1) Un-associated; 2) placed near like neighbors; or 3) connected  New observations sometimes reverse earlier assertions  Some observations produce new discovery  As the working space expands, computational effort increases  Given sufficient observations, there can come a tipping point. Thereafter, confidence improves while computational effort decreases!
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 62 Big Data [in context] = Insights. More data: better the predictions – Lower false positives – Lower false negatives More data: bad data … good – Suddenly glad your data was not perfect More data: less compute
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 63 1. Data + Analytics = Information 2. Information + Context = Insight 3. Insight + Actions = Desired Outcomes Quiz: The Big Data Journey to Value
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 64 The most competitive organizations are going to make sense of what they are observing fast enough to do something about it while they are observing it.
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 65 65 Data in Motion Data at Rest Data in Many Forms Information Ingestion and Operational Information Decision Management BI and Predictive Analytics Navigation and Discovery Intelligence Analysis, Raw Data Structured Data Text Analytics Data Mining Entity Analytics Machine Learning Landing Area, Analytics Zone, Archive Video/Audio Network/Sensor Entity Analytics Predictive Real-time Analytics Exploration, Integrated Warehouse, and Mart Zones Discovery Deep Reflection Operational PredictiveStream Processing Data Integration Master Data Streams Information Governance, Security and Business Continuity Batch parallel Big Data processing Real-Time In-memory servers Data Warehouse Traditional IT Thus, there is a Workflow in a Big Data infrastructure
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 66  In order to build a workflow for Big Data, you must know:  Where/how is Big Data is stored, analyzed, delivered? Understanding Big Data in Context
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 67  C = cloud  A = analytics  M = Mobile  S = Social Remember this acronym: C.A.M.S.
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 68  C = cloud  A = analytics  M = Mobile  S = Social Big Data in Context:  Where data is generated and collected  Where data is stored  How data is analyzed  Where data is analyzed  How data is delivered  Who is consuming it
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 69 Cloud – today’s Delivery Model
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 70 Where is the Big Data? Answer: Cloud Data Centers
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 71 71 Bandwidth availability is tipping point for adoption of “The Cloud”………  Worldwide broadband bandwidth availability is becoming commonplace  Facilitates a pervasive web services delivery model – (i.e. “The Cloud”)  Hosted in mega data centers with massive amounts: – Processors, Storage, Network  As a result: – We are seeing on-premise data centers worldwide rapidly disappearing, off-premise, into the cloud
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 72 72 http://wikibon.org/blog/wp-content/uploads/2011/10/5-top-data-centers.html
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 73http://wikibon.org/blog/wp-content/uploads/2011/10/5-top-data-centers.html
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 74 Large Cloud Data Centers 10. SUPERNAP, LAS VEGAS, 407,000 SF 9A and 9B. MICROSOFT QUINCY AND SAN ANTONIO DATA CENTERS, 470,000 S
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 75 75 Container Data Center Architecture 7. PHOENIX ONE, PHOENIX, ARIZ. 538,000 SF 5. MICROSOFT CHICAGO DATA CENTER, Chicago 700,000 SF 2. QTS METRO DATA CENTER, ATLANTA, 990,000 SF Microsoft’s Chicago Container Data Center
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 76 76 More data centers…. 4. NEXT GENERATION DATA EUROPE, WALES 750,000 SF 3. NAP OF THE AMERICAS, MIAMI, 750,000 SF 1. 350 EAST CERMAK, CHICAGO, 1.1 MILLION SQUARE FEET Consumes 100 megawatts of power, 2nd-largest power customer for Commonwealth Edison, trailing only Chicago’s O’Hare Airport.
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 77 Now….. what about the web giants?  i.e. Apple, Facebook, Google, Amazon, etc? That’s Big! Great Technology Wars of 2012 – Future of the Innovation Economy - Fast Company.com
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 78 Apple Here’s what powers iCloud, see Jobs at WWDC 2011 iCloud announce (YouTube) Rendering of Apple's new North Carolina Data Center. Credit: Apple Other Apple data centers: Cork, Ireland Munich, Germany Newark, California Cupertion, Calif Apple Data Center FAQ Maiden, North Carolina 500K sq ft USD $1B This is phase 1 only Apple Data Center Newark, California Purposes for all these data centers: •iCloud •Support Apple’s WW install base of devices •Futures: Move Content Delivery Network in-house? •Futures: Streaming video? Under construction: Prineville, Oregon
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 79 Facebook Facebook’s North Carolina Data Center Goes Live Lulea, Sweden - 290K sq ft (27K sq meters) by late 2012 Facebook – Prinville, Oregon Has spent $1B on it’s data centers Open Compute Project http://www.wired.com/wiredenterprise/2011/12/facebook-data-center/all/1
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 80 Amazon Web Services Amazon Web Services 1Q12: 450,000 servers Amazon Perdix Modular Datacenter EC2 17K core, 240 teraflop cluster 42nd fastest supercomputer in world 1Q12: 450,000 Servers estimated 1Q13: > 2 trillion objects in S3 1Q13: 1.1 M req/sec http://aws.typepad.com/aws/2012/04/amazon-s3-905-billion-objects-and-650000-requestssecond.html http://gigaom.com/cloud/how-big-is-amazon-web-services-bigger-than-a-billion/ http://aws.typepad.com/aws/2013/04/amazon-s3-two-trillion-objects-11-million-requests-second.html
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 81 What is Google? Google is not a search engine Google is a real-time “Data Factory” ecosystem – Defacto organizer of all human internet data – Provides worldwide Patterns of Life data • Search, analytics, etc as processing • Interactive maps as visualization – Android as ingest / output devices • Motorola Wireless acquisition $12B – Supporting businesses and ecosystem roles: • Google+, Play, Shop, Books, Gmail, Docs • Voice recognition software The history of search engine http://www.wordstream.com/articles/internet-search-engines-history
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 82 82 Google: The Dalles, Oregon internet scale data center 82 Google Data Center – The Dalles, Oregon
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 83 83 Google Data Center Photo Gallery http://www.google.com/about/datacenters/gallery/#/
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 84 84 Google Data Centers in 2008:
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 85 85 Google Data Center CAPEX worldwide  Capital expenditures on datacenters: – YTD 2013: USD$ 2.4B – 2012: USD$ 3.2B – 2011: USD$ 3.4B – 2010: USD$ 4.0B – 2009: USD$ 809M The Dalles, Oregon Each data center between $200M and $600M
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 86  Time to market  Cost Reduction  Data proximity  Better/faster technology support  Self-service  Shift the culture/business process  New kinds of applications  At scale never before imagined Why Cloud Delivery Model, Cloud Data Centers
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 87 Primary drivers for move to cloud = business reasons http://www.kpmg.com/global/en/issuesandinsights/articlespublications/cloud-service-providers-survey/pages/service-providers.aspx Competitive Advantage, Revenue
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 88  Value delivered IT Infrastructure Provisioning Continuous Access to data From traditional Weeks To cloud Minutes For users Reduced admin costs Up to 50% savings For IT Reduced energy costs Up to 36% Increased utilization Up to 90%From 50% Localized, any time any where Dynamic (Elastic) Centralized FixedCapacity Cloud Infrastructure Business Value Time-to-Delivery Competitive Advantage Revenue “Time is Money”
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 89 Growth of The Cloud by 2016  Mobile  Geo-locational  Real-time data  Shift to cloud mega-data centers http://www.datacenterknowledge.com/archives/2012/10/23/cisco-releases-2nd-annual-global-cloud-index/ Source: > 50% in cloud Cisco already knows > 50% workload is in the cloud
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 90 Visualizing Mobile and Social  C = cloud  A = analytics  M = Mobile  S = Social
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 91 Space-Time-Travel Source - blog by: Jeff Jonas/Las Vegas/IBM, Chief Scientist, IBM SWG Entity Analytics http://jeffjonas.typepad.com/ 6 billion mobile phones 6.8 billion people
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 92 Space-Time-Travel 6 billion mobile phones 6.8 billion people Source - blog by: Jeff Jonas/Las Vegas/IBM, Chief Scientist, IBM SWG Entity Analytics http://jeffjonas.typepad.com/ Re-Identify (figuring who is who) is somewhat trivial Reveal Where you spend time Who with (e.g., friends) Geo-location data Mobile Phones 600B transactions / day (in US) De-Identify in volume in real-time share with third parties
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 93 Space-Time-Travel 6 billion mobile phones 6.8 billion people Source - blog by: Jeff Jonas/Las Vegas/IBM, Chief Scientist, IBM SWG Entity Analytics http://jeffjonas.typepad.com/ Here Now More to come Unravel All of one’s secretsAbsolute identification Ultimate biometric Reshape Tough problems Image classification Identification Enormous Opportunity Challenge all notions of privacy
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 94 Possible….. Like Magic … Source - blog by: Jeff Jonas/Las Vegas/IBM, Chief Scientist, IBM SWG Entity Analytics http://jeffjonas.typepad.com/jeff_jonas/2009/08/your-movements-speak-for-themselves-spacetime-travel-data-is-analytic-superfood.html 87% certainty where you will be this Thursday at 5pm Top 10 people you co- locate with (home / work) High quality traffic- avoid predictions pushed to you real-time Transactions not consistent with your pattern = reduce credit card theft 90% Political opponent crushed, resigns two days after announcing candidacy Governments change Due to mass online social networking Cannot truly be turned off 6 billion mobile phones 6.8 billion people
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 95 80% 5 minutes 4/5ths 2/3rds $1Tril. 84% of Millennials say social and user-generated content has an influence on what they buy. 70% 2x of Boomers agree. 57% 57% of companies in 2014 expect to devote more than 25% of their IT spending to systems of engagement. (Almost double the investment one year ago.) 9 5 IBM CONFIDENTIAL 2014 Mobile/Social: 84% of smartphone users check an app as soon as they wake up. as many people in 2013 were willing to share their geolocation data in return for personalized offers compared to the previous year. The response time users expect from a company once they have contacted them via social media. of U.S. adult smartphone users keep their phones with them 22 hours per day. of individuals are willing to trade their information for a personalized offering. of U.S. adults say they would not return to a business that lost their personal, confidential information. of upside potential in online retail sales if buyers trust more.
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 96 Observe: how fast mobile internet grows by 2014 By 2014: Mobile will be main way Of connecting to Internet
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 9797 Mobile affects all business models… Mobile = Geo-locational superfood for real-time analytics
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 98 Mobile / Social endpoints for Data Supertransformagicability TaxiWiz HousingMaps Source: http://mashable.com/2007/07/11/google-maps-mashups-2/ Weatherbug
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 9999 By 2016, how much mobile data? What kind?  2012: –Mobile-connected devices > # people • 2016: – 10 billion mobile devices – (world population: 7.3 B) http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns705/ns827/white_paper_c11-520862.html Smartphones 48% Web data, video 70%
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 100  Monetizing, innovating, creating competitive advantage out of Big Data Innovating using Big Data
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 101 Different forms of automation have had a profound impact 0
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 102 Manufacturing changes with an end of mass production.. • Growth in manufacturing capable countries • Global levelling out • Hybridised manufacturing • Micro multi-nationals clusters • Globally recognised specialisation
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 103 3D printing has the potential to drive another step change • Digitisation often leads to the freemium • Defining a sustainable position in the value chain • Really understanding what customer value is critical
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 104  “The Curve”: giving away things for free, in exchange for data?  http://www.youtube.com/watch?v=pcyzn5oiDrI Today’s changing business models
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 105 1 2 3 4 5 Augment Products Codify Services Interconnect Industries Trade Information Digitise Assets Instrument products to create new data and extend notion of client value Expand use of differentiated capabilities through ecosystems or business platforms to create additional value Use information to create new value chains that reduce waste and bridge gaps between organizations Translate data into information that is of value to adjacent industries Transform analogue into digital assets New Patterns for Innovation have emerged
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 106 Using this patterns require elastic enterprises.. Adjacency Leverage core competency Earn market permission Differentiation Maintainable advantage Serve individual needs Scaling Ecosystems Amplified innovation Co-creation of new value Dynamic Operating Model Able to share the new value Scalable business platform Source: Elastic Enterprise, Nicholas Vilatari and Haydn Shaughnessy
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 107 Interesting to look at Corning  Strong light glass for light bulbs  Dishes, plates… They are the “standard” in some cultures  Glass for LCD screens.  Now predicting the future of glass  http://www.youtube.com/watch?v=jZkHpNnXLB0
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 108 Big Data is at the heart of innovation in business
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 109  Big Data business decisions URL:  https://bda.expertise.client-conversations.com  Available on the internet Complete information on Innovating with Big Data:
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 110 Jobs replaced by Technology http://www.businessinsider.com/the-future-of-jobs-the-onrushing-wave-2014-1
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 111 1. Data + Analytics = Information 2. Information + Context = Insight 3. Insight + Actions = Desired Outcomes Quiz: The Big Data Journey to Value
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 112  Data, the new natural Resource  Data + Analytics = Information. Information + Context = Insight. Insight + Action = Outcomes  Big Data in context:  Cloud, Analytics, Mobil, Social  Innovating using Big Data:  Monetizing, innovating, creating competitive advantage out of Big Data Summary – what we covered today:
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 113 Thank You Merci Grazie Obrigado Danke Japanese Hebrew English French Russian German Italian Brazilian Portuguese Arabic Traditional Chinese Simplified Chinese Hindi Tamil Korean Thai TesekkurlerTurkish
    • © 2014 John Sing – All Rights Reserved University of South Florida - Spring 2014 114