Transcript Lecture 2

IS3321 Information Systems Solutions
for the Digital Enterprise
Lecture 2: Big data and the Internet of
Things
Rob Gleasure
[email protected]
robgleasure.com
IS3321

Today’s session
 Introduction to big data
 The 3 V’s of big data
 The Internet of Things
Big data
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Last session
 The ‘cloud’
 Bandwidth capacity increasing year on year
 Move to pay-as-you-go web-hosted services for
 software (Saas)
 platforms (PaaS)
 infrastructures (Iaas)
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All of this interaction with one linked information system means vast
quantities of data can be captured throughout user interaction, often
in real-time
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‘big data’
Big data
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The idea is that the vast amounts of interaction data allow for
systems that are nuanced and responsive in ways that were
previously not possible
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Also a realisation that, if it can be analysed, this data is a huge
commodity, meaning new business models are possible
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So when is data ‘big data’
3 Vs of Big data
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Volume
 Facebook generates 10TB of new data daily, Twitter 7TB
 A Boeing 737 generates 240 terabytes of flight data during a flight
from one side of the US to the other
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We can use all of this data to tell us something, if we know the right
questions to ask
3 Vs of Big data
Traditional Approach
Analyzed
information
Big Data Approach
All available
information
analyzed
All available
information
Analyze small
subsets of data
Analyze all data
From http://www.slideshare.net/ibmcanada/big-dataturning-data-into-insights?qid=0b4c69bc-3db2-4e12-ae47-a362a25752eb&v=qf1&b=&from_search=3
3 Vs of Big data
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Velocity
 Clickstreams and asynchronous data transfer can capture what
millions of users are doing right now
Think back to AirBnB – make a change, then watch the response.
No guesswork required up front as to what to gather, we can induce
the interesting stuff as we see it
3 Vs of Big data
Traditional Approach
Hypothesis
Question
Answer
Data
Start with hypothesis
and test against
selected data
Big Data Approach
Data
Exploration
Insight
Correlation
Explore all data and
identify correlations
From http://www.slideshare.net/ibmcanada/big-dataturning-data-into-insights?qid=0b4c69bc-3db2-4e12-ae47-a362a25752eb&v=qf1&b=&from_search=3
3 Vs of Big data
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Variety
 Move from structured data to unstructured data, including image
recognition, text mining, etc.
 Gathered from users, applications, systems, sensors
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Increasingly comprehensive data view of our ecosystem
 The Internet of Things
The Internet of Things
From http://www.pcworld.com/article/2039413/new-intel-ceo-creates-mysterious-new-devices-division.html
The Internet of Things
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RFID sensors, bluetooth, microprocessors, wifi all becoming easier
to embed in ‘dumb’ devices
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Move to mobile also means more data streaming from us at all
times, e.g. location, call activity, net use
The Internet of Things
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Smart homes/smart cities
 Temperature, lighting, food stocks, energy, security
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Smart cars
 Diagnostics, traffic suggestions, sensors, self-driving
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Smart healthcare
 Worn and intravenous computing detects issues early and
monitors care outcomes remotely
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Smart factories, farms
 Machines coordinated efficiently, linked dynamically to
consumption models
Big data
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Success stories
 Books
 Barnes and Noble: Discovered that readers often quit
nonfiction books less than halfway through. Introduced highly
successful new series of short books on topical themes
 Amazon: originally used a panel of expert reviewers for
books. Data surplus allowed them to create increasingly
predictive recommendations. Panel has since been disbanded
and 1/3 of sales are now driven by the recommender system
Big data and the Internet of Things
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Success stories (continued)
 Transport
 Flyontime.us: used historical weather and flight delay
information to predict likelihood of flights get delayed
 Farecast: looked at ticket prices for specific flights based on
historical data, then advised users to buy or wait according to
predicted fare costing trajectory
 UPS: Uses a range of traffic data to calculate most efficient
time/fuel efficient routes according to complex algorithm
Big data and the Internet of Things
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Famous success stories (continued)
 Healthcare
 Modernizing Medicine EMA dermatology system
 https://www.youtube.com/watch?v=jMGaGtK9nzU
Big data and the Internet of Things
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Famous success stories (continued)
 Social media
 Google (data for information relevance)
 Twitter (c.f. #RescuePH)
 Facebook (social data)
Big data and privacy
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Morey et al. argue people create roughly three types of data of
increasing sensitivity
 Self-reported data
 Digital exhaust
 Profiling data
Due to the growth in biotechnologies and sensors, there’s an
argument that ‘profiling data’ could be further broken down to
differentiate between ‘digital profile’ data and ‘biometric data’
Data beneficiaries
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Companies may then use the data in three different ways
 Improve product or service
 Facilitate targeted marketing
 Sell data to third parties
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Google search is an example of a digital business that combines all
of these
 Your search behaviour becomes customised
 Ads are placed in front of you according to your history and
location
 Click-through behaviour and user overviews are provided to third
parties
Issues with big data
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Google Flu Trends
 Life imitating data, imitating life?
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No one is really average height
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Your Xbox knows you like that Katy Perry song
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Also, Target called to say your teenage daughter is pregnant.
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Icecream sales and shark attacks…
Icecream sales and shark attacks
continued (correlation, not causation)
From http://xkcd.com/552/
Target’s family monitoring continued
Readings
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Mayer-Schönberger, V. and Cukier, K. (2014). Big Data: A
Revolution That Will Transform How We Live, Work, and Think,
John Murray Publishers, UK.
Morey, T., Forbath, T. T., & Schoop, A. (2015). Customer Data:
Designing for Transparency and Trust. Harvard Business Review,
93(5), 96-105.
http://nextcity.org/daily/entry/rescuers-use-social-media-twitter-tofind-disaster-victims