Cloud_Big_Data_Analytics_Mobile_Social_modern_internet_scale_business_models_2014_John_Sing

© 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


University of South Florida - Spring 2014

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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)

[email protected]

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



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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



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1. Data + Analytics = Information

2. Information + Context = Insight

3. Insight + Actions = Desired Outcomes

Today’s message: The Big Data Journey to Value



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Data, the new natural resource



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Traditional IT “sensemaking” capability

Available datafor 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/



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Traditional IT “sensemaking” capability

Available datafor observation

ContextEnterpriseAmnesia

What we see in the world today ………..




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Enterprise Amnesia, definition

A defect in memory, resulting in missed opportunity, wasted resources, lower revenues, unnecessary fraud losses, and other bad news.



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Time

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Traditional IT “sensemaking” capabilities

Available ObservationSpace

Because traditional IT methods could not keep pace

WHY?




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Time

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This is the Big Data Opportunity

Add: Big Data Sensemaking Algorithms


Context Big Datacapability

New/Useful Information

DataAnalytics



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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



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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



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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!



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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 Me

ssages– Facebook's New Realtime Analytics Syst

em: 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



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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/



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Visualizing Big Data

Source: Wikibon March 2011

Goal: Analyze *all* the datareal 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

Distributedaggregation

Looselystructured

Often incomplete

Sampling not strategically competitive



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Visualizing Big Data….



PetabytesExabytes

Millions / Billions of

people

Billions /Trillions of

records

Time-stampedevents

Unknown inter-

relationships

Flat files



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Visualizing Big Data…..



Connectionsdetermined by

probability

Process entire (huge)

data set

Data generated by collective actionover the Internet

OpenSource

innovation

It’s more than the

algorithms….



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It’s also:



Its collaboration of algorithms

Combined / Collaboratedinnovative

ways

A softwareEcosystemis essentialOn a worldwide

scale

MultipleWorldwide“Pockets of

Value”



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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



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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



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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_Beyondhttp://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/



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Visualizing why Hadoop was created for Big DataTraditional 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

DataSend Consolidate result

Traditional approachApplication server and Database server are separateAnalysis Program can run on multiple Application serversNetwork is still in the middleData has to go through networkDesigned to analyze TBs of data

•Big Data Approach Analysis Program runs where the data is : on Data NodeOnly Analysis Program has to go through the networkAnalysis Program is executed on every DataNodeDesigned to analyze PBs of dataHighly Scalable :

1000s NodesPetabytes and more

Thank you to: Pascal VEZOLLE/France/IBM@IBMFR and Francois Gibello/France/IBM for the use of this slide



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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

DataSend 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



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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 outputfor final processing)

3. Reduce Phase(boil all output down toa 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();

. . .

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 maptasks 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



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What is being done

with Big Data today?



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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:



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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



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• 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



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Aerospace / defense transformation



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Automobile transformation

Ford: https://www.youtube.com/watch?v=nFUszkSv5X0



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Energy & utilities transformation



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Government transformation

Miami-Dade County: https://www.youtube.com/watch?v=toL4Yx9WYPoMiami-Dade Police: https://www.youtube.com/watch?v=1b5RiPWd-Pw



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Media and entertainment transformation



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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 advanceBlack line: actual result

Stockholm: http://www.youtube.com/watch?v=rfMylzF4lv8



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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 MapMemphis Blue CRUSH Map

Police videos: http://www.youtube.com/watch?v=8SJQtn4RO7I

Playvideo

https://www.youtube.com/watch?v=_xsffIAHY3I



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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 andEntertainment

Retail

3.5 PB in 20101 TB CT scanner → 2.5 PB/Year/Scanner

20 PB in 2011Grow 300 TB per month, every month

ExamplesIndustries

Healthcare

Provider

Claims

Processor



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How much data?



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Solution: take Big Data on the Journey to Value



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Data + Analytics = Information

Information + context = Insight

So…. What is “context”?



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Review: this is the Big Data Opportunity

Add: Big Data Sensemaking Algorithms


Context Big DataCapability“context”

New/Useful Information

DataAnalytics



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No Context

[email protected]



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Context, definition

Better understanding something by taking into account the things around it.



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Information in Context … = Insights

Top 200Customer

Job Applicant

IdentityThief

CriminalInvestigation

[email protected]



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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



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Puzzling

270 pieces90%

200 pieces66%

150 pieces50%

6 pieces2%(pure noise)

30 pieces10% (duplicates)



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First Discovery



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More Data Finds Data



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Duplicates in Front Of Your Eyes



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First Duplicate Found Here



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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!”



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150 pieces50%



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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.”



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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!



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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



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Quiz: The Big Data Journey to Value



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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.



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Data inMotion

Data atRest

Data inMany Forms

Information Ingestion and Operational Information

Information Ingestion and Operational Information

Decision Management

BI and Predictive Analytics

Navigation and Discovery

IntelligenceAnalysis,

Raw Data Structured Data Text Analytics Data Mining Entity Analytics Machine Learning

Landing Area, Analytics Zone, ArchiveLanding Area, Analytics Zone, Archive

Video/AudioNetwork/SensorEntity AnalyticsPredictive

Real-time AnalyticsReal-time Analytics

Exploration,Integrated Warehouse,

and Mart Zones

DiscoveryDeep ReflectionOperationalPredictive Stream Processing

Data Integration Master Data

StreamsStreams

Information Governance, Security and Business Continuity Information Governance, Security and Business Continuity

Batch parallel Big Data processing

Real-Time In-memory servers

Data WarehouseTraditional IT

Thus, there is a Workflow in a Big Data infrastructureThus, there is a Workflow in a Big Data infrastructure



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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



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C = cloud

A = analytics

M = Mobile

S = Social

Remember this acronym: C.A.M.S.



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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



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Cloud – today’s Delivery Model



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Where is the Big Data?

Answer: Cloud Data Centers



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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



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72 http://wikibon.org/blog/wp-content/uploads/2011/10/5-top-data-centers.html



73http://wikibon.org/blog/wp-content/uploads/2011/10/5-top-data-centers.html



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Large Cloud Data Centers

10. SUPERNAP, LAS VEGAS, 407,000 SF

9A and 9B. MICROSOFT QUINCY AND SAN ANTONIO DATA CENTERS, 470,000 S



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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



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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.



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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



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AppleHere’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, IrelandMunich, GermanyNewark, CaliforniaCupertion, Calif

Apple Data Center

FAQ

Maiden, North Carolina 500K sq ftUSD $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



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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



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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:

450,000Servers

estimated

1Q13: > 2 trillion

objects in S3

1Q13: 1.1 Mreq/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



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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



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Google: The Dalles, Oregon internet scale data center

82Google Data Center – The Dalles, Oregon



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Google Data Center Photo Gallery

http://www.google.com/about/datacenters/gallery/#/



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Google Data Centers

in 2008:



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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



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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



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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,RevenueCompetitive Advantage,Revenue



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Value delivered

IT Infrastructure Provisioning

Continuous Access to data

From traditional

Weeks

To cloud

Minutes

For usersFor users

Reduced admin costs Up to 50% savings

For ITFor 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-DeliveryCompetitive AdvantageRevenue“Time is Money”

Time-to-DeliveryCompetitive AdvantageRevenue“Time is Money”



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Growth ofThe Cloudby 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



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Visualizing Mobile and Social

C = cloud

A = analytics

M = Mobile

S = Social



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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



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Space-Time-Travel


6.8 billion people


Re-Identify

(figuring who is who) is somewhat trivial

Reveal

Where you spend timeWho with (e.g., friends)

Geo-location data

Mobile Phones600B transactions /

day(in US)

De-Identify

in volume in real-time

share with third parties



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Space-Time-Travel


6.8 billion people


Here Now

More to come

Unravel

All of one’s secretsAbsolute identification

Ultimate biometric

Reshape

Tough problemsImage classification

Identification

EnormousOpportunity

Challenge all notions of privacy



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Possible….. Like Magic …

Source - blog by: Jeff Jonas/Las Vegas/IBM, Chief Scientist, IBM SWG Entity Analyticshttp://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 off6 billion

mobile phones6.8 billion

people



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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 companiesin 2014 expect to devote more than 25% of their IT spending to systems of engagement. (Almost double the investment one year ago.)

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IBM CONFIDENTIAL 2014

Mobile/Social:

84%

of smartphone users check anapp as soon as they wake up.

as many people in 2013 werewilling 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 keeptheir phones with them 22 hours per day.

of individuals are willingto 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 onlineretail sales if buyers trust more.



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Observe: how fast mobile internet grows by 2014

By 2014:

Mobile will be main way

Of connecting to Internet



9797

Mobile affects all business models…

Mobile = Geo-locational superfood for real-time analytics



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Mobile / Social endpoints for Data Supertransformagicability

TaxiWiz

HousingMaps

Source: http://mashable.com/2007/07/11/google-maps-mashups-2/

Weatherbug



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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,video70%



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Innovating using Big Data



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Different forms of automation have had a profound impact

0



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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



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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



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“The Curve”: giving away things for free, in exchange for data?

http://www.youtube.com/watch?v=pcyzn5oiDrI

Today’s changing business models



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1

2

3

4

5

AugmentProducts

Codify Services

InterconnectIndustries

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



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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 ShaughnessySource: Elastic Enterprise, Nicholas Vilatari and Haydn Shaughnessy



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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



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Big Data is at the heart of innovation in business



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Big Data business decisions URL:

https://bda.expertise.client-conversations.com

Available on the internet

Complete information on Innovating with Big Data:



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Jobs replaced by Technology

http://www.businessinsider.com/the-future-of-jobs-the-onrushing-wave-2014-1



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Quiz: The Big Data Journey to Value



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Data + Analytics = Information. Information + Context = Insight. Insight + Action = Outcomes

Big Data in context:

Cloud, Analytics, Mobil, Social

Innovating using Big Data:


Summary – what we covered today:



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Thank You

Merci

Grazie

ObrigadoDanke

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