Slide deck I used to discuss some ideas about cloud computing, business intelligence, and big data at TDWI's Solution Summit on Cloud BI. Feel free to quote but please attribute to me (Pano Xinos, 2013) or the original sources if sources are provided. All proprietary information is their respective owners and/or publishers. This presentation is Copyright

1. BI
and
The
Cloud:
What
does
it
all
mean?
Pano
M.
Xinos

2. For
the
sake
of
clarity…

3. Necessary
evil
On-­‐demand,
self-­‐service
Broad
network
access
Resource
pooling
Rapid
elasLcity
Measured
service
hMp://csrc.nist.gov/publicaLons/nistpubs/800-­‐145/SP800-­‐145.pdf
Private
cloud
Public
cloud
Hybrid
cloud
IaaS
PaaS
SaaS

4. PredicLve
AnalyLcs
=
Big
Data
Business
Intelligence
≠
Big
Data

5. THREE
IDEAS

6. One
CHEAP,
ACTIONABLE
INFORMATION
Two
AS
“BIG
DATA”
à
MAINSTREAM
BUSINESS
INTELLIGENCE
=
BIG
DATA
Three
GARBAGE
IN,
GARBAGE
OUT
(“IT’S
NOT
INFORMATION
OVERLOAD;
IT’S
FILTER
FAILURE.”)

7. One
CHEAP,
ACTIONABLE
INFORMATION

8. hMp://aws.amazon.com/ec2/pricing/
(Oct.
2013)
hMp://www.google.com/trends/
(Oct.
2013)

9. hMp://aws.amazon.com/ec2/pricing/
(Oct.
2013)

10. $3B+
of
revenue
in
2013
can’t
be
wrong

11. Big
Data
<3’s
Cloud
Large
Workloads
+
Cloud
CompuLng
XOXOXOX

12. CommodiLzed
compute
combined
with
predicLve
analyLc
tools
yields
acLonable
informaLon

13. Cloud
+
PredicLve
Data
AnalyLcs
Cheap,
AcLonable
InformaLon

14. “Man
Spends
266
Hours
Awake
For
World
Record,
Now
Feels
Stupid”
hMp://www.wired.com/table_of_malcontents/2007/05/man-­‐spends-­‐266-­‐hours-­‐awake-­‐for-­‐world-­‐record-­‐now-­‐feels-­‐stupid/

15. Two
AS
“BIG
DATA”
à
MAINSTREAM
BIG
DATA
=
BUSINESS
INTELLIGENCE

16. hMp://www.infoincog.com/the-­‐big-­‐data-­‐hype-­‐cycle/

17. It’s
not
all
doom
and
gloom

18. hMp://bytesizebio.net/2013/04/13/terrible-­‐advice-­‐from-­‐a-­‐great-­‐scienLst/

19. Future
success
is
measured
in
terms
of
opportunity
cost
Image:
hMp://thecareerblog.wordpress.com/

20. Companies
that
already
have
a
BI
pracLce
will
more
easily
integrate
predicLve
analyLcs

21. Open
source
is
a
low(er)
cost
soluLon
that
will
help
drive
adopLon

22. As
predicLve
analyLcs
is
more
widely
adopted,
it
will
merge
with
BI
as
a
new
pracLce

23. hMp://www.woldfitness.com

24. Three
GARBAGE
IN,
GARBAGE
OUT
(“IT’S
NOT
INFORMATION
OVERLOAD;
IT’S
FILTER
FAILURE.”1)

25. “It’s
not
informaLon
overload;
it’s
filter
failure.”
-­‐-­‐Clay
Shirky
(2008)
1
hMp://www.youtube.com/watch?v=LabqeJEOQyI

26. hMp://blog.poMerzot.com/2007/09/25/garbage-­‐in-­‐garbage-­‐out-­‐and-­‐the-­‐desire-­‐to-­‐cover-­‐our-­‐own-­‐ass-­‐is-­‐ruining-­‐the-­‐world/

27. Flickr,
Copyright
ShieldkiMen

28. PoliLcs
hMp://www.threehundredeight.com/

29. So,
what
does
it
all
mean?

30. One
CLOUD
IS
CHEAP.
INFORMATION
IS
PRICELESS.
Two
GET
ON
BOARD
WHILE
THE
GETTING
IS
GOOD.
Three
EXPERIENCE
AND
INDUSTRY
EXPERTISE
SOLVES
PROBLEMS.

31. Thank
you!
@pmxinos
PanoXinos.TDWI@gmail.com