Literacy in the Age of Big Data

1. #IMDAYS
//
@michael_smit
Literacy
in
the
Age
of
Big
Data
Mike
Smit
School
of
Informa9on
Management
Faculty
of
Management

2. #IMDAYS
//
@michael_smit
What
is
Big
Data?
• Volume
/
Variety
/
Velocity,
or
• Anything
more
than
I
can
handle,
or
• Data
too
large
to
be
contained
by
a
single
computer,
or
• Data
beyond
human
scale,
or
• Data
measured
in
TB
or
bigger,
or
• Anything
I
have
a
beFer
chance
of
selling
you
by
claiming
it
is
Big
Data.

3. #IMDAYS
//
@michael_smit

4. #IMDAYS
//
@michael_smit
Twi;er
Example
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At
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academically
rigorous.
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5. #IMDAYS
//
@michael_smit
What
is
Big
Data?
• Volume
/
Variety
/
Velocity,
or
• Anything
more
than
I
can
handle,
or
• Data
too
large
to
be
contained
by
a
single
computer,
or
• Data
beyond
human
scale,
or
• Data
measured
in
TB
or
bigger,
or
• Anything
I
have
a
beFer
chance
of
selling
you
by
claiming
it
is
Big
Data.
WHY
is
Big
Data?

6. Reason
#1:
Web
2.0
turned
everyone
into
content
creators

8. Reason
#2:
Internet
of
Things
turns
everything
into
data
creators
Image:
GE
press
release

10. #IMDAYS
//
@michael_smit
Reason
#3:
Data
accumulaOon
is
less
visible

11. #IMDAYS
//
@michael_smit

12. #IMDAYS
//
@michael_smit
Reason
#4:
Declining
Price
of
Storage

14. #IMDAYS
//
@michael_smit
1.
How
much
would
it
cost
to
buy
enough
hard
drives
to
store
all
the
music
in
the
iTunes
store?
2.
Same
quesOon,
but
pretend
it
is
10
years
ago.

15. #IMDAYS
//
@michael_smit
Reason
#5:
Cloud
storage
(and
pay-­‐as-­‐you-­‐go
pricing)

16. #IMDAYS
//
@michael_smit
Pre-­‐Cloud
0
1
Price
Time

17. #IMDAYS
//
@michael_smit
Cloud
Era
0
1
Price
Time

18. #IMDAYS
//
@michael_smit
Ds1fFtZx4olD5acndKSToGizuuj2D9Ut9prJlDLPpq35mNVHQghsD
pGo13qZKpgF8Qe1xQnjKU0VEDwn3aXNTe4miEwbAq2WqkjWx
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AfpRwmUZnBLYWJcjGIgjpD5237WhfZMFSEaMfOSi5SFD1aAq12
Reason
#6:
The
lingering
hope
of
finding
valuable
informaOon

19. #IMDAYS
//
@michael_smit
Why
Do
We
Have
Big
Data?
…
because
we
can.

20. #IMDAYS
//
@michael_smit
What
is
Big
Data?
A
Problem,
not
a
Solu8on.

21. #IMDAYS
//
@michael_smit
So,
uh,
thanks
for
having
me!
(Just
Kidding)

22. #IMDAYS
//
@michael_smit
Where
Do
We
Start?
• Admit
you
have
a
problem
• S9ck
together
• Remain
Calm!
We
fear
what
we
don’t
understand:
data
literacy
educa9on.
• Analy9cs
(self-­‐serve
business
intelligence)
• There
is
no
subs9tute
for
human
aFen9on…
but
when
that’s
not
feasible,
what
else
you
got?
– Idea:
Cogni9ve
Compu9ng
for
improved
automa9on
– Idea:
Knowledge
Graph
for
RM
• Records
Management

23. #IMDAYS
//
@michael_smit
Admit
you
have
a
problem

24. #IMDAYS
//
@michael_smit
SOck
Together

26. 26
Ascend
the
Pyramid
(AnalyOcs,
self-­‐service
business
intelligence,
etc.)

27. #IMDAYS
//
@michael_smit
Historic
Flood
Database:
A
Big
Data
Approach
• Automa9cally
processing
newspaper
ar9cles
to
produce
open
datasets
describing
geo-­‐
located
floods
in
Nova
Sco9a.
• Visual
interface

29. #IMDAYS
//
@michael_smit
Remain
Calm:
Strength
through
EducaOon
(Data
Literacy)

30. Skills
Gap
• Predicted
for
US
in
2018
by
McKinsey
Global
Ins9tute
Posi%ons:(
465k(
Workforce:(
300k(
Deep$Analy*cs$Skills$

31. Posi%ons:(
4m(
Workforce:(
2.5m(
Deep$Analy*cs$Skills$ Data1savvy$

32. Data
Literacy
• The
ability
to
create,
comprehend,
and
communicate
data.
• The
ability
to
collect,
manage,
evaluate,
and
apply
data,
in
a
cri9cal
manner.
• Spans
disciplines,
sectors,
universi9es,
…

33. !

34. #IMDAYS
//
@michael_smit
Data
Literacy
EducaOon
Conceptual
Framework
Introduction to Data
Knowledge and understanding of data
Knowledge and understanding of the uses and
applications of data
Data Collection
Data Discovery and
Collection Performs data exploration Identifies useful data Collects data
Evaluating and Ensuring
Quality of Data and Sources
Crtically assesses sources of data for
trustworthiness
Critically evaluates quality of datasets for errors
or problems
Data Organization Knowledge of basic data organization methods
and tools Asesses data organization requirements Organizes data
Data Manipulation
Asesses methods to clean data Identifies outliers and anomalies Cleans data
Data Management
Data Conversion (from
format to format)
Knowledge of different data types and
conversion methods
Converts data from one format or file type to
another
Metadata Creation and Use
Creates metadata descriptors
Assigns appropriate metadata descriptors to
original data sets
Data Curation, Security, and
Re-Use
Assesses data curation requirements (e.g.
retention schedule, storage, accessibility,
sharing requirements, etc.)
Assess data security requirements (e.g.
restricted access, protected drives, etc.) Curates data
Data Preservation
Assesses requirements for preservation Asseses methods and tools for data preservation Preserves data
Data Tools Knowledge of data analysis tools and
techniques
Selects appropriate data analysis tool or
technique
Applies data analysis tools and
techniques
Basic Data Analysis
Develops analysis plans Applies analysis methods and tools Conducts exploratory analysis Evaluates results of analysis
Compares results of analysis with other
findings
Data Interpretation
(Understanding Data) Reads and understands charts, tables, and
graphs
Identifies key take-away points, and integrates
this with other important information
Identifies discrepancies within
the data
Data Evaluation
Identifying Problems Using
Data
Uses data to identify problems in practical
situations (e.g. workplace efficiency)
Uses data to identify higher level problems (e.g.
policy, environment, scientific experimentation,
marketing, economics, etc.)
Data Visualization Creates meaningful tables to organize and
visually present data
Creates meaningful graphical representations of
data
Evaluates effectiveness of
graphical representations
Critically assesses graphical representations
for accuracy and misrepresentation of data
Presenting Data (Verbally) Asssess the desired outcome(s) for presenting
the data
Assesses audience needs and familiarity with
subject(s)
Plans the appropriate meeting or
presentation type
Utilizes meaningful tables and
visualizationsto communicate data
Presents arguments and/or outcomes
clealy and coherently
Data Driven Decisions
Making (DDDM) (Making
decisions based on data) Prioritizes information garnered from data Converts data into actionable information
Weighs the merit and impacts of
possible solutions/decisions Implements decisions/solutions
Critical Thinking Aware of high level issues and challlenges
associated with data Thinks critically when working with data
Data Culture
Recognizes the importance of data
Supports an environment that fosters critical use
of data for learning, research, and decision-
making
Data Application Data Ethics
Aware of legal and ethical issues associated
with data Applies and works with data in an ethical manner
Data Citation Knowledge of widely-accepted data citation
methods Creates correct citations for secondary data sets
Data Sharing Assesses methods and platforms for sharing
data Shares data legally, and ethically
Evaluating Decisions Based
on Data
Collects follow-up data to assess effectiveness
of decisions or solutions based upon data Conducts analysis of follow-up data
Compares results of analysis
with other findings
Evaluates decisions or solutions based on
data
Retains original conclusions or decisiosn,
or implements new decisions/solutions

35. #IMDAYS
//
@michael_smit
There
is
no
subsOtute
for
human
a;enOon
But
some9mes
we
have
too
much
data
and
not
enough
humans!

37. Google’s
Knowledge
Graph

39. 39
CogniOve
CompuOng

40. 40

41. #IMDAYS
//
@michael_smit
Discussion
• Mike.Smit@dal.ca
• @michael_smit
• I’m
here
all
day!

42. #IMDAYS
//
@michael_smit
Image
Credits
(1)
• hFp://www.scien9ficamerican.com/media/inline/blog/Image/wisdom.jpg
• hFp://rudyloans.com/wp-­‐content/uploads/2013/11/Arrow-­‐Up-­‐4.jpg
• hFp://www.mrwallpaper.com/cat-­‐and-­‐dog-­‐cuddle-­‐wallpaper/
• hFp://poFermore.wikia.com/wiki/Category:Gryffindor
• hFp://poFermore.wikia.com/wiki/File:Slytherin_mark.png
• hFp://daverobertsfilm.wordpress.com/2011/02/02/media-­‐studies-­‐key-­‐debates/
• hFp://www.themobilityresource.com/wearable-­‐technology-­‐and-­‐how-­‐it-­‐affects-­‐
people-­‐with-­‐disabili9es/
• Original
source
unknown;
available
• hFp://adpaascu.wordpress.com/tag/global-­‐ci9zens/
• hFps://www.torproject.org/
• hFp://www.gnupg.org/
• hFp://www.iden9tyfinder.com/
42

43. #IMDAYS
//
@michael_smit
Image
Credits
(2)
• hFp://www.gartner.com/technology/research/hype-­‐cycles/
• hFp://blog.udacity.com/2013/07/new-­‐course-­‐design-­‐of-­‐everyday-­‐things.html
• Screenshot
from
hFp://pennystocks.la/internet-­‐in-­‐real-­‐9me/
• hFp://www.officeimaging.com/
• hFp://www.clipartbest.com/gradua9on-­‐caps-­‐clip-­‐art
• Cost
per
GB
from
hFp://www.mkomo.com/cost-­‐per-­‐gigabyte-­‐update
• Images
on
slides
47,
57,
58
are
©
Mike
Smit,
2014.
• Slide
35:
screenshot
of
personal
laptop
&
cell
phone
• Slide
37:
Vancouver
Archives,
hFp://searcharchives.vancouver.ca/power-­‐lines-­‐
and-­‐suppor9ng-­‐structure-­‐in-­‐lane-­‐west-­‐of-­‐main-­‐street-­‐at-­‐pender-­‐street
• Slide
43:
Screenshot
of
Watson
User
Modeling.
Made
from
my
own
copy
of
their
demo
applica9on,
but
also
available
publicly
at
hFp://watson-­‐um-­‐
demo.mybluemix.net/
43

44. #IMDAYS
//
@michael_smit
Image
Credits
(3)
• All
graphs
were
created
for
the
purpose
of
this
presenta9on
• Logos
on
slide
38
are
from
the
respec9ve
websites
• Images
on
slide
39:
– BoFom
lei:
Thalmic
Labs
via
TechCrunch
hFp://techcrunch.com/2013/06/05/thalmic-­‐labs-­‐
raises-­‐14-­‐5m-­‐to-­‐make-­‐the-­‐myo-­‐armband-­‐the-­‐next-­‐big-­‐thing-­‐in-­‐gesture-­‐control/
– Top
lei:
Apple.com
– Top
right:
fitbit.com
– BoFom
right:
hFps://www.google.ca/glass/start/
• Slide
41:
hFp://www.geekwire.com/2013/ibm-­‐takes-­‐watson-­‐cloud/
44