DataEd Online: Demystifying Big Data

Copyright 2013 by Data Blueprint
1
Demystifying Big Data
Yes, we face a data deluge and big data seems to
be largely about how to deal with it. But much of
what has been written about big data is focused on
selling hardware and services. The truth is that
until the concept of big data can be objectively
defined, any measurements, claims of success,
quantifications, etc. must be viewed skeptically
and with suspicion. While both the need for and
approaches to these new requirements are faced
by virtually every organization, jumping into the
fray ill-prepared has (to date) reproduced the
same dismal IT project results.
Date: May 14, 2013
Time: 2:00 PM ET/11:00 AM PT
Presenter:Peter Aiken, Ph.D.
• Every century, a new technology-steam power,
electricity, atomic energy, or microprocessors-has
swept away the old world with a vision of a new one.
Today, we seem to be entering the era of Big Data
– Michael Coren
• Every century, a new technology-steam power,
electricity, atomic energy, or microprocessors-has
swept away the old world with a vision of a new one.
Today, we seem to be entering the era of Big Data
– Michael Coren

Get Social With Us!
Live Twitter Feed
Join the conversation!
Follow us:
@datablueprint
@paiken
Ask questions and submit
your comments: #dataed
2
Like Us on Facebook
www.facebook.com/
datablueprint
Post questions and comments
Find industry news, insightful
content
and event updates.
Join the Group
Data Management &
Business Intelligence
Ask questions, gain insights
and collaborate with fellow
data management
professionals

It's about separating the signal from the noise
Presented by Peter Aiken, Ph.D.

Meet Your Presenter:
Peter Aiken, Ph.D.
• 30 years of experience in data
management
– Multiple international awards
– Founder, Data Blueprint
(http://datablueprint.com)
• 8 books and dozens of articles
• Experienced w/ 500+ data management
practices in 20 countries
• Multi-year immersions with organizations
as diverse as the US DoD, Deutsche Bank,
Nokia, Wells Fargo, and the
Commonwealth of Virginia
4

• Data Analysis
- Origins
• Challenges
- Faced by virtually everyone
• Compliment
- Existing data management practices
• Pre-requisites
- Necessary to exploit big data techniques
• Prototyping
- Iterative means of practicing big data techniques
• Take Aways and Q&A
Tweeting now:
#dataed
5
• Data Analysis
- Origins
• Challenges
• Compliment
• Pre-requisites
• Prototyping
• Take Aways & Q&A
Tweeting now:
#dataed

• Data Analysis
- Origins
• Challenges
• Compliment
• Pre-requisites
• Prototyping
Tweeting now:
#dataed
6

7
Bills of Mortality by Captain John Graunt

8

9
Mortality Geocoding
Where is it happening?

10
Plague Peak
When is it happening?
("Whereas
of
the
Plague")

11
Black Rats or Rattus Rattus
Why is it happening?
Black Rats or Rattus Rattus
Why is it happening?

12
What will happen?

13
John Snow's 1854 Cholera Map of London

• Data Analysis
- Origins
• Challenges
• Compliment
• Pre-requisites
• Prototyping
Tweeting now:
#dataed
14

Data Inflation
Unit Size What
it
means
Bit
(b) 1
or
0
Short
for
“binary
digit”,
aAer
the
binary
code
(1
or
0)

computers
use
to
store
and
process
data
Byte
(B) 8
bits
Enough
informaFon
to
create
an
English
leGer
or
number
in

computer
code.
It
is
the
basic
unit
of
compuFng
Kilobyte
(KB) 1,000,
or
210,
bytes From
“thousand”
in
Greek.
One
page
of
typed
text
is
2KB
Megabyte
(MB) 1,000KB;
220
bytes
From
“large”
in
Greek.
The
complete
works
of
Shakespeare
total
5MB.
A
typical
pop
song
is

about
4MB
Gigabyte
(GB) 1,000MB;
230
bytes From
“giant”
in
Greek.
A
two-‐hour
ﬁlm
can
be
compressed
into
1-‐2GB
Terabyte
(TB) 1,000GB;
240
bytes
From
“monster”
in
Greek.
All
the
catalogued
books
in
America’s
Library
of
Congress
total

15TB
Petabyte
(PB) 1,000TB;
250
bytes
All
leGers
delivered
by
America’s
postal
service
this
year
will
amount
to
around
5PB.

Google
processes
around
1PB
every
hour
Exabyte
(EB) 1,000PB;
260
bytes Equivalent
to
10
billion
copies
of
The
Economist
ZeGabyte
(ZB) 1,000EB;
270
bytes The
total
amount
of
informaFon
in
existence
this
year
is
forecast
to
be
around
1.2ZB
YoGabyte
(YB) 1,000ZB;
280
bytes Currently
too
big
to
imagine
The preﬁxes are set by an intergovernmental group, the International Bureau of Weights and Measures. Source: The Economist Yotta and Zetta were
added in 1991; terms for larger amounts have yet to be established
15

What do they mean big?
16
"Every 2 days we create as much
information as we did up to 2003"
– Eric Schmidt
The
number
of
things
that
can
produce
data
is

rapidly
growing
(smart
phones
for
example)
IP
traﬃc
will
quadruple

by
2015
– Asigra
2012

Google Search Results for the Term "Big Data"
17
Data from Google Trends Source: Gartner (October 2012)

Number of Internet Pages Mentioning Big Data
18
Data from Google Trends Source: Gartner (October 2012)

Increasingly individuals make use of
the things data producing capabilities
to perform services for them
19

• IBM
– 100 terabytes uploaded daily
– $2.1 billion spent on mobile ads in 2011
– 4.8 trillion ad impressions/daily
• WIPRO
– 2.9 million emails sent every second
– 20 hours of video uploaded to youtube every minute
– 50 million tweets per day
• Asigra
– 2.5 quintillion bytes created daily
– 90% of data was created in the last two years
– By 2015 8 zettabytes will have been created
Examples
20

21
• 60 GB of data/second
• 200,000 hours of big data
will be generated testing
systems
• 2,000 hours media
coverage/daily
• 845 million Facebook users
averaging 15 TB/day
• 13,000 tweets/second
• 4 billion watching
• 8.5 billion devices
connected
2012 London Summer Games

22
Sloan Management Review/Harvard Business Review
Copyright 2013 by Data BlueprintMIT Sloan Management Review Fall 2012 Page 22 By Thomas H. Davenport, Paul Barth And Randy Bean

Big Data(has something to do with Vs - doesn't it?)
• Volume
– Amount of data
• Velocity
– Speed of data in and out
• Variety
– Range of data types and sources
• 2001 Doug Laney
• Variability
– Many options or variable interpretations confound analysis
• 2011 ISRC
• Vitality
– A dynamically changing Big Data environment in which analysis and predictive
models must continually be updated as changes occur to seize opportunities
as they arrive
• 2011 CIA
• Virtual
– Scoping the discussion to only include online assets
• 2012 Courtney Lambert
23

Nanex 1/2 Second Trading Data(May 2, 2013 Johnson and Johnson)
24
http://www.youtube.com/watch?v=LrWfXn_mvK8
The European Union last year approved a new rule mandating that all trades must exist for at
least a half-second in this instance that is 1,200 orders and 215 actual trades

25
Historyflow-Wikipedia entry for the word “Islam”

26
Spatial Information Flow-New York Talk Exchange

Some Far-out Thoughts
on Computers by Orrin
Clotworthy
• Predicted use of
not just
computing in the
intelligence
community
• Also forecast
predictive
analytics
• Accompanying
privacy
challenges
27

• Big Data are high-volume, high-velocity, and/or high-variety
information assets that require new forms of processing to
enable enhanced decision making, insight discovery
and process optimization.
– Gartner 2012
• Big data refers to datasets whose size is beyond the ability of
typical database software tools to capture, store, manage, and analyze.
– IBM 2012
• Big data usually includes data sets with sizes beyond the ability of
commonly-used software tools to capture, curate, manage, and process the
data within a tolerable elapsed time.
– Wikipedia
• Shorthand for advancing trends in technology that open the door to a new
approach to understanding the world and making decisions.
– NY Times 2012
• Big data is about putting the "I" back into IT.
– Peter Aiken 2007
• We have no objective definition of big data!
– Any measurements, claims of success, quantifications, etc. must be viewed
skeptically and with suspicion!
• Question: "Would it be more useful to refer to "big data techniques?"
Defining Big Data
28

Big Data Techniques
• New techniques available to impact the productivity (order of
magnitude) of any analytical insight cycle that compliment,
enhance, or replace conventional (existing) analysis methods
• Big data techniques are currently characterized by:
– Continuous, instantaneously
available data sources
– Non-von Neumann
Processing (defined later in the presentation)
– Capabilities approaching
or past human comprehension
– Architecturally enhanceable
identity/security capabilities
– Other tradeoff-focused data processing
• So a good question becomes "where in our existing architecture
can we most effectively apply Big Data Techniques?"
29

Computers
Human resources
Communication facilities
Software
Management
responsibilities
Policies,
directives,
and rules
Data
What Questions Can Architectures Address?
30
• How and why do the
components interact?
• Where do they go?
• When are they needed?
• Why and how will the
changes be implemented?
• What should be managed
organization-wide and what
should be managed
locally?
• What standards should be
adopted?
• What vendors should be
chosen?
• What rules should govern
the decisions?
• What policies should guide
the process?

!

!

!

!

Copyright 2013 by Data Blueprint 31
Organizational Needs
become instantiated
and integrated into an
Data/Information
Architecture
Informa(on)System)
Requirements
authorizes and
articulates
satisfyspecificorganizationalneeds
Data Architectures produce and are made up of information models that are
developed in response to organizational needs

Enterprises Spend $38 Million a Year on Data
32
• Worldwide spending on
business information is
now $1.1 trillion/year
• Enterprises spend an
average of $38 million
on information/year
• Small and Medium
sized Businesses on
average spend
$332,000
– http://www.cio.com.au/article/429681/
five_steps_how_better_manage_your_data/

• Data Analysis
- Origins
• Challenges
• Compliment
• Pre-requisites
• Prototyping
Tweeting now:
#dataed
33

Gartner Five-phase Hype Cycle
http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp
34
Technology Trigger: A potential technology breakthrough kicks things off. Early proof-of-concept stories and media interest
trigger significant publicity. Often no usable products exist and commercial viability is unproven.
Trough of Disillusionment: Interest wanes as experiments and implementations fail to deliver. Producers of the
technology shake out or fail. Investments continue only if the surviving providers improve their products to the
satisfaction of early adopters.
Peak of Inflated Expectations: Early publicity produces a number of
success stories—often accompanied by scores of failures. Some
companies take action; many do not.
Slope of Enlightenment: More instances of how the technology can benefit the
enterprise start to crystallize and become more widely understood. Second- and third-
generation products appear from technology providers. More enterprises fund pilots;
conservative companies remain cautious.
Plateau of Productivity: Mainstream adoption starts to
take off. Criteria for assessing provider viability are more
clearly defined. The technology’s broad market
applicability and relevance are clearly paying off.

Gartner Big Data Hype Cycle
35
"A focus on big data is not a substitute for the
fundamentals of information management."

36
Big Data in Gartner’s Hype Cycle

Technology Continues to Advance
37
• (Gordon) Moore's law
– Over time, the number of transistors on
integrated circuits doubles approximately
every two years

Pick any two!
and there are
still tradeoffs
to be made!
38

• Faster processors outstripped
not only the hard disk, but main
memory
– Hard disk too slow
– Memory too small
• Flash drives remove both
bottlenecks
– Combined Apple and Yahoo
have spend more than $500
million to date
• Make it look like traditional
storage or more system
memory
– Minimum 10x improvements
– Dragonstone server is 3.2 tb
flash memory (Facebook)
• Bottom line - new capabilities!
"There’s now a blurring between the storage world and the memory world"
39

• von Neumann
bottleneck
(computer science)
– "An inefficiency inherent in
the design of any von
Neumann machine that
arises from the fact that
most computer time is
spent in moving
information between
storage and the central
processing unit rather than
operating on it"
[http://encyclopedia2.thefreedictionary.com/von+Neumann+bottleneck]
• Michael Stonebraker
– Ingres (Berkeley/MIT)
– Modern database
processing is
approximately 4%
efficient
• Many "big data
architectures are
attempts to address
this, but:
– Zero sum game
– Trade characteristics
against each other
• Reliability
• Predictability
– Google/MapReduce/
Bigtable
– Amazon/Dynamo
– Netflix/Chaos Monkey
– Hadoop
– McDipper
• Big data exploits
non-von Neumann
processing
Non-von Neumann Processing/Efficiencies
40

Potential Tradeoffs:
CAP theorem: consistency, availability and partition-tolerance
41
Partition
(Fault)
Tolerance
AvailabilityConsistency
RDBMS NoSQL
Small datasets can be both consistent & available
Atomicity
Consistency
Isolation
Durability
Basic
Availability
Soft-state
Eventual consistency

Potential either/or Tradeoffs
42
SQL Big Data
Privacy Big Data
Security Big Data
?
Massive High-
speed Flexible

• Patterns/objects,
hypotheses emerge
– What can be
observed?
• Operationalizing
– The dots can be
repeatedly connected
• Things are
happening
– Sensemaking
techniques address
"what" is happening?
• Patterns/objects,
hypotheses emerge
– What can be
observed?
• Operationalizing
– The dots can be
repeatedly connected
– "Big Data"
contributions are
shown in orange

<-‐Feedback
"Sensemaking"

Techniques
!
Exis&ng!
Knowledge
/base
Analytics Insight Cycle
43
Volume
Velocity
Variety
PotenFal/
actual
insights
Discernm
ent
Exploitable
Insight
PaGern/Object

Emergence
Com
bined/
inform
ed

insights
AnalyDcal

boEleneck

• Data analysis struggles with the social
– Your brain is excellent at social cognition - people can
• Mirror each other’s emotional states
• Detect uncooperative behavior
• Assign value to things through emotion
– Data analysis measures the quantity of social
interactions but not the quality
• Map interactions with co-workers you see during work days
• Can't capture devotion to childhood friends seen annually
– When making (personal) decisions about social
relationships, it’s foolish to swap the amazing machine
in your skull for the crude machine on your desk
• Data struggles with context
– Decisions are embedded in sequences and contexts
– Brains think in stories - weaving together multiple
causes and multiple contexts
– Data analysis is pretty bad at
• Narratives / Emergent thinking / Explaining
• Data creates bigger haystacks
– More data leads to more statistically significant
correlations
– Most are spurious and deceive us
– Falsity grows exponentially greater amounts of data
we collect
• Big data has trouble with big problems
– For example: the economic stimulus debate
– No one has been persuaded by data to switch sides
• Data favors memes over masterpieces
– Detect when large numbers of people take an instant
liking to some cultural product
– Products are hated initially because they are unfamiliar
• Data obscures values
– Data is never raw; it’s always structured according to
somebody’s predispositions and values
44
Some Big Data Limitations

Savings come from a variety
of agreed upon categories
and values:
• Reduced hospital re-
admissions
• Patient Monitoring:
Inpatient, out-patient,
emergency visits and ICU
• Preventive care for ACO
• Epidemiology
• Patient care quality and
program analysis
$300 billion is the potential annual value to health care
45
$165
$108
$47
$9$5
Transparency in clinical data and clinical decision support
Research & Development
Advanced fraud detection-performance based drug pricing
Public health surveillance/response systems
Aggregation of patient records, online platforms, & communities

0
25
50
75
100
Current Improved
Reversing The Measures
• Currently:
– Analysts spend 80% of their time manipulating data and 20% of their time
analyzing data
– Hidden productivity bottlenecks
• After rearchitecting:
– Analysts spend less time manipulating data and more of their time analyzing data
– Significant improvements in knowledge worker productivity
46
Manipulation Analysis

• Data Analysis
- Origins
• Challenges
• Compliment
• Pre-requisites
• Prototyping
Tweeting now:
#dataed
47

What do we teach business people about data?
48
What percentage of the deal with it daily?

What do we teach IT professionals about data?
• 1 course
– How to build a new
database
– 80% if UT expenses
are used to improve
existing IT assets
• What impressions do
IT professionals get
from this education?
– Data is a technical skill
that is used to develop
new databases
• This is not the best
way to educate IT and
business
professionals - every
organization's
– Sole, non-depletable,
non-degrading,
durable, strategic
asset
49

Application-Centric Development
Original articulation from Doug Bagley @ Walmart
50
t
t
Strategy
Goals/Objectives
Systems/Applications
Network/Infrastructure
Data/Information
t
• In support of strategy, the organization
develops specific goals/objectives
• The goals/objectives drive the
development of specific systems/
applications
• Development of systems/applications
leads to network/infrastructure
requirements
• Data/information are typically
considered after the systems/
applications and network/
infrastructure have been articulated
• Problems with this approach:
– Ensures that data is formed
around the application and not
the information requirements
– Process are narrowly
formed around applications
– Very little data reuse is possible

Einstein Quote
51
"The significant
problems we face
cannot be solved
at the same level
of thinking we
were at when we
created them."
- Albert Einstein

What does it mean to treat data as an organizational asset?
• Assets are economic resources
– Must own or control
– Must use to produce value
– Value can be converted into cash
• An asset is a resource controlled by
the organization as a result of past
events or transactions and from which
future economic benefits are expected
to flow to the organization [Wikipedia]
• With assets:
– Formalize the care and feeding of data
• Cash management - HR planning
– Put data to work in unique/significant
ways
• Identify data the organization will need
[Redman 2008]
52

Data-Centric Development Flow
Original articulation from Doug Bagley @ Walmart
53
t
t
Strategy
Goals/Objectives
Data/Information
Network/Infrastructure
Systems/Applications
t
• In support of strategy, the organization
develops specific goals/objectives
• The goals/objectives drive the
development of specific data/
information assets with an eye to
organization-wide usage
• Network/infrastructure components are
developed to support organization-
wide use of data
• Development of systems/applications
is derived from the
data/network architecture
• Advantages of this approach:
– Data/information assets are
developed from an
organization-wide perspective
– Systems support organizational
data needs and compliment
organizational process flows
– Maximum data/information reuse

Top
Operations
Job
CDO Reporting
54
Top Job
Top
Finance
Job
Top
Information
Technology
Job
Top
Marketing
Job
• There is enough work to justify the function
• There is not much talent
• The CDO provides significant input to the Top Information Technology Job
Data Governance Organization
Chief
Data
Officer

• Data Analysis
- Origins
• Challenges
• Compliment
• Pre-requisites
• Prototyping
Tweeting now:
#dataed
55

56
Traditional Systems Life Cycle Challenges
projectcartoon.com
• Original business concept
• As the consultant described it
• As the customer explained it
• How the project leader understood it
• How the programmer wrote it
• What the beta testers received
• What operations installed
• As accredited for operation
• When it was delivered
• How the project was documented
• How the help desk supported it
• How the customer was billed
• After patches were applied
• What the customer wanted

"Waterfall" model of
Systems Development
57

"Waterfall" model (with iteration possible)
58

Spiral Model of
Systems
Development
59
Barry W. Boehm "A Spiral Model of
Software Development and Enhancement"
ACM SIGSOFT Software Engineering Notes
August 1986, 11(4):14-24
"The major
distinguishing
feature of the
spiral model is
that it creates a
risk-driven
approach ...
rather than a
primarily
document-driven
or code-driven
process"

Prototyping & Big Data Technologies
60

Advanced
Data
Practices
• Cloud
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
• 5 Data
management
practices areas /
data management
basics ...
• ... are necessary
but insufficient
prerequisites to
organizational data
leveraging
applications that is
self actualizing data
or advanced data
practices
Hierarchy of Data Management Practices (after Maslow)
Basic Data Management Practices
– Data Program Management
– Organizational Data Integration
– Data Stewardship
– Data Development
– Data Support Operations
http://3.bp.blogspot.com/-ptl-9mAieuQ/T-idBt1YFmI/AAAAAAAABgw/Ib-nVkMmMEQ/s1600/maslows_hierarchy_of_needs.png

Experience Pebbles
62

• Data Analysis
- Origins
• Challenges
• Compliment
• Pre-requisites
• Prototyping
Tweeting now:
#dataed
63

Which Source of Data Represents the Most Immediate Opportunity?
64
Guidance
• The real change: cost-effectiveness
and timely delivery
• Business process optimization — not
technology
• High fidelity and quality information
• Big data technology, all is not new
• Keep your focus, develop skills
(Source: Gartner (January 2013)

Gartner Recommendations
65
Impacts Top Recommendations
Some of the new analytics that are made
possible by big data have no precedence,
so innovative thinking will be required to
achieve value
Treat big data projects as innovation
projects that will require change
management efforts. The business will
take time to trust new data sources and
new analytics
Creative thinking can unearth valuable
information sources already inside the
enterprise that are underused
Work with the business to conduct an
inventory of internal data sources outside
of IT's direct control, and consider
augmenting existing data that is IT
'controlled.' With an innovation mindset,
explore the potential insight that can be
gained from each of these sources
Big data technologies often create the
ability to analyze faster, but getting value
from faster analytics requires business
changes
Ensure that big data projects that improve
analytical speed always include a process
redesign effort that aims at getting
maximum benefit from that speed
Gartner 2012

Results: It is not always about money
• Solution:
– Integrate multiple databases into
one to create holistic view of data
– Automation of manual process
• Results:
– Data is passed safely and effectively
– Eliminate inconsistencies,
redundancies, and corruption
– Ability to cross-analyze
– Significantly reduced turnaround
time for matching patients with
potential donor -> increased
potential to make life-saving
connection in a manner that is
faster, safer and more reliable
– Increased safe matches from 3 out
of 10 to 6 out of 10
66

Data versus Tools-A History Lesson
• Sophisticated tool without data are useless
• Mediocre tools with the data are frustrating
• Analysts will always opt for frustration over futility, if
that is their only option
– Ira "Gus" Hunt CIA/CTO
• http://www.huffingtonpost.com/2013/03/20/cia-gus-hunt-big-data_n_2917842.html
67

Questions?
68
+ =

Unlock Business Value through
Data Quality Engineering
June 11, 2013 @ 2:00 PM ET/11:00 AM PT
Data Systems Integration & Business Value Pt. 1:
Metadata
July 9, 2013 @ 2:00 PM ET/11:00 AM PT
Sign up here:
www.datablueprint.com/webinar-schedule
or www.dataversity.net
Upcoming Events
69

10124 W. Broad Street, Suite C
Glen Allen, Virginia 23060
804.521.4056

DataEd Online: Demystifying Big Data

More Related Content

What's hot (20)

Viewers also liked (14)

Similar to DataEd Online: Demystifying Big Data (20)

More from DATAVERSITY (20)

Recently uploaded (20)

DataEd Online: Demystifying Big Data