The document discusses the concept of big data and its implications for higher education institutions. It highlights the challenges and strategies of implementing data-driven decision making, emphasizing the importance of metrics, analytics maturity, and governance. Various aspects of data management, including its volume, velocity, and variety, are explored alongside references to business intelligence and the evolving landscape of analytics.

1. BIG DATA
DOUNIVERSITIES
dream of
BIG DATA

2. 30definitions
OPENTRACKER.NET

3. in excel
i don’t
FIT

4. Big data is like teenage sex:
everyone talks about it,
nobody really knows how to do it,
everyone thinks everyone else is
doing it, so everyone claims they
are doing it...
DAN ARIELY

5. consensus

6. DATA DRIVEN ENTERPRISE
UNIVERSITY DEVELOPMENTS

7. data driven
ENTERPRISE

8. volume, velocity, and variety

9. “There were 5 exabytes of
information created between the
dawn of civilization through 2003,
but that much information is now
created every 2 days.”
ERIC SCHMIDT

10. decision making
INFORM

11. business
STRATEGIES

12. culture
RIGHT

13. 40% 43%ENTERPRISE
INITIATIVE
TECHNICAL
INITIATIVE
NOT SEEING EYE-TO-EYE
EAB

14. where is the value?

15. STRATEGIC
use metrics with the high
level objectives and look
at aggregated results
with long time frames.

16. TACTICAL
use measures focused
on shorter time periods
(days or weeks) and are
used by managers to
help meet business
targets.

17. OPERATIONAL
measures are focused
on real-time or
transaction data with
attention on exception
alerting.

18. ANALYTICSis the
new INTELLIGENCE

19. highlow
low
high
information and analytical
maturity
businessvalue
IBM
INFORMATION ENABLED ENTERPRISE
HISTORICAL
REPORTING
SENSE AND
REPORT
ANTICIPATE
AND SHAPE

20. barriers
constraints
&

21. role-based
access
data
governance
data quality education

22. dedicated
leadership
support
dedicated
leadership
support

23. university
DEVELOPMENTS

24. weare
here

25. we are building...

26. ANALYTICS HERO

27. ready set
DATA

28. EXPANDING ON 3 FRONTS AT AN INCREASING RATE
mb gb tb pb
real time
near real time
periodic
batch
table
database
photo, web, audio
social, video, mobile
VOLUME
VELOCITY
VARIETY

29. 2000 2005 2011 2013 2014
PEOPLESOFT
MOODLE
EDRMS
GOOGLE
DATA
WAREHOUSE
future

30. V
olume

31. VELOCITY

32. reporting and analysis lead
to insightful action

33. Data Decision
Decision Support
Action
Decision Automation
Predictive
What will happen?
Diagnostic
Why did it happen?
Prescriptive
What should happen?
Descriptive
What happened?
Business
Intelligence
Business
Analytics
Human Input
Reporting or
Analytics
GARTNER

34. an
STRATEGIC
TACTICAL
OPERATIONAL

35. Strategic Analysis and
Data Warehouse Office

36. 2012
2015
2014
data books
via: website
source: peoplesoft
excel powerpivot
via: sharepoint
source: acorn
tableau workbooks
(student)
via: tableau server
source: acorn
via: reporting site
add: tableau workbooks
(hr & financial

39. FALL HEADCOUNT BY NATIONAL STATUS

41. real-time
machine data
splunk traverse
OPERATIONAL
INTELLIGENCE

42. intelligence
TACTICAL

43. are
we
heading
W
H
ERE

44. not all the pieces
are put together.

45. integration

46. SELF
SERVICE

47. ANALYTICS
MATURITY
OF

48. “There are known knowns. These are
things we know that we know. There
are known unknowns. That is to say,
there are things that we know we don't
know. But there are also unknown
unknowns. There are things we don't
know we don't know.”
DONALD RUMSFELD

49. DATA
MANAGEMENT
ANALYSIS
governance

50. DATA DRIVEN ENTERPRISE
UNIVERSITY DEVELOPMENTS

51. CREDITS
https://www.flickr.com/photos/sniegowski/8355161895/
https://www.flickr.com/photos/nichodesign/10429930073/
http://www.freepik.com/free-vector/
https://www.flickr.com/photos/officialgdc/6235163921/
https://www.flickr.com/photos/julochka/12205021584/
http://www.analyticshero.com/
https://www.flickr.com/photos/orangegreenblue/9217944381/