1.
Wake Up and
Smell the Data
February, 2013
Mark Madsen
www.ThirdNature.net
@markmadsen

2.
Caveat
The focus of this talk is on information processing
and delivery, leaving out many aspects of big data
in the automation / execution sense.

3.
Big Data, Big Hype
$876 Gajillion (analyst estimates of the big data market)

4.
We’ve been here before
Bill Schmarzo, EMC

5.
Big Data, Big Nonsense
Big data is subjective, based on bigness at a point in time?
McKinsey focused on the least interesting aspect of big data.
Source: McKinsey

6.
Data volume is the oldest, easiest problem
Image courtesy of Teradata

7.
Technology Capability and Data Volume
Source: Noumenal, Inc.

8.
Origin of BI and data warehouse concepts
The general concept of a
separate architecture for BI
has been around longer, but
this paper by Devlin and
Murphy is the first formal
data warehouse architecture
and definition published.
8
“An architecture for a business and
information system”, B. A. Devlin,
P. T. Murphy, IBM Systems Journal,
Vol.27, No. 1, (1988)
Slide 8Copyright Third Nature, Inc.

9.
Our ideas about
information and
how it’s used are
outdated.

10.
Metadata catalog

11.
Report

12.
Report library

13.
BI is using broken metaphors
We think of BI as publishing, which it isn’t.

14.
When you first give people access to information
that was unavailable…
OH GOD
I can see into forever

15.
After a while the response is more measured

16.
User autonomy is a tradeoff
Autonomy is a tradeoff in
most data warehouses:
control at the expense of
complexity.
Complexity for casual users
can lead to messes.
So we err on the side of
simplifying user access in
three ways…

17.
Centralize: that solves all problems!
Creates bottlenecks
Causes scale problems
Enforces a single model
In some organizations and areas of business “data warehouse” is a bad word.

18.
Standardize: it’s simpler for everyone

19.
The “E” in EDW
was a lie…

20.
Measurement started with the convenient data
The convenient data is
transactional data.
▪ Goes in the DW and is used, even
if it isn’t the right measurement.
The difficult and misleading data
is declarative data.
▪ What people say and what they
do require ground truth.
The inconvenient data is
observational data.
▪ It’s not neat, clean, or designed
into most systems of operation.
We need to build data systems
that integrate all three.

21.
Value: There’s a pony in there somewhere

22.
Many current views miss the point
Using Big Data

23.
It’s not about “big”
Using Big Data
And “big” is often not as big as you think it is.

24.
It’s not really about data, either
Using Big Data
If there’s no process for applying information in a specific
context then you are producing expensive trivia.

25.
Two keys to making big data worthwhile
Value:
Goal solution
not
Solution goal
Actionability:
Simple “value” isn’t enough.
Information has to be actionable, somehow.

26.
Planning data strategy means understanding the
context of data use so we can provide infrastructure
Monitor
Analyze
Exceptions
Analyze
Causes
Decide Act
No problem No idea Do nothing
We need to focus on what people do with data as the
primary task, not on the data or the technology.
Copyright Third Nature, Inc.

27.
General model for organizational use of data
Collect
new data
Monitor
Analyze
Exceptions
Analyze
Causes
Decide Act
No problem No idea Do nothing
Act on the process
Usually days/longer timeframe
Act within the process
Usually real-time to daily

28.
You need to be able to support both paths
Collect
new data
Monitor
Analyze
Exceptions
Analyze
Causes
Decide Act
Act on the process
Act within the process
Conventional BI
Causal analysis, i.e. “data science”

29.
How do you manage the business in today’s environment?
Our simplistic notions of BI with stable models, ordered data
and predictability are being replaced by concepts from
decision support and complex adaptive systems (CAS).
Simple Complicated Complex
Assumption: Order Assumption: Unorder Assumption: Disorder
Cause and effect is repeatable
& predictable
Cause and effect is separated
in time & space, repeatable,
learnable
Cause and effect is coherent
in retrospect only, modelable
but changing
Known Knowable Unpredictable
Standard processes, clear
metrics, best practice
Analytical techniques to
determine options, effects
Experiment to create possible
options
Sense, categorize, respond Sense, analyze, respond Test, sense, respond
Reporting, dashboards Ad‐hoc, OLAP, exploration Data science, casual analysis
Situational context governs data useCopyright Third Nature, Inc.

30.
BI/DW environment support varies for these contexts
Handles this really well
(most of the time).
Basic BI Analysis Data science, analytics
Assumption: Order Assumption: Unorder Assumption: Disorder
Cause and effect is repeatable
& predictable
Cause and effect is separated
in time & space, repeatable,
learnable
Cause and effect is coherent
in retrospect only, modelable
but changing
Known Knowable Unpredictable
Standard processes, clear
metrics, best practice
Analytical techniques to
determine options, effects
Experiment to create possible
options, test hypotheses
Sense, categorize, respond Sense, analyze, respond Test, sense, respond
Reporting, dashboards Ad‐hoc, OLAP, data discovery Casual analysis, simulation
Handles this sort of
ok, sometimes.
This, not so much.
Copyright Third Nature, Inc.

31.
TANSTAAFL
Technologies are not
perfect replacements for
one another.
When replacing the old
with the new (or ignoring
the new over the old) you
always make tradeoffs,
and usually you won’t
see them for a long time.

32.
The usage models for conventional BI
Collect
new data
Monitor
Analyze
Exceptions
Analyze
Causes
Decide Act
No problem No idea Do nothing
Act on the process
Usually days/longer timeframe
Act within the process
Usually real-time to daily
This is what we’ve been
doing with BI so far: static
reporting, dashboards,
ad-hoc query, OLAP

33.
The usage models for analytics and “big data”
Collect
new data
Monitor
Analyze
Exceptions
Analyze
Causes
Decide Act
No problem No idea Do nothing
Act on the process
Usually days/longer timeframe
Act within the process
Usually real-time to daily
Analytics and big data is
focused on new use
cases: deeper analysis,
causes, prediction,
optimizing decisions
This isn’t ad-hoc,
reporting, or OLAP.

34.
Analytics embiggens the data volume problem
Many of the processing problems are O(n2) or worse, so
moderate data can be a problem for DB‐based platforms

35.
New and growing use cases drive the need to expand
The use cases are now interactive applications, lower latency
data, complex analytics and discovery rather than reporting.

36.
Big Data Shift in a Nutshell
The old model for data
▪ Centralized publishing
▪ Read only
▪ Integrate before use
▪ Record only important data
▪ Retrieval‐focused
▪ Single method of access
▪ Human‐level latency
The new model for data
▪ Community creation
▪ Read‐write
▪ Integrate at time of use
▪ Record all the data
▪ Processing‐focused
▪ Multiple methods of access
▪ Machine‐level latency
It’s an architectural reconfiguration, just like web 2.0

37.
“The future, according to some scientists, will be exactly like
the past, only far more expensive.” ~ John Sladek

38.
About the Presenter
Mark Madsen is president of Third
Nature, a research and advisory firm
focused on analytics, business
intelligence and data management.
Mark is an award‐winning author,
architect and CTO whose work has been
featured in numerous industry
publications. Over the past ten years
Mark received awards for his work from
the American Productivity & Quality
Center, TDWI, and the Smithsonian
Institute. He is an international speaker,
a contributor at Forbes Online and
Information Management. For more
information or to contact Mark, follow
@markmadsen on Twitter or visit
http://ThirdNature.net

39.
About Third Nature
Third Nature is a research and consulting firm focused on new and
emerging technology and practices in analytics, business intelligence, and
performance management. If your question is related to data, analytics,
information strategy and technology infrastructure then you‘re at the right
place.
Our goal is to help companies take advantage of information-driven
management practices and applications. We offer education, consulting
and research services to support business and IT organizations as well as
technology vendors.
We fill the gap between what the industry analyst firms cover and what IT
needs. We specialize in product and technology analysis, so we look at
emerging technologies and markets, evaluating technology and hw it is
applied rather than vendor market positions.

40.
CC Image Attributions
Thanks to the people who supplied the creative commons licensed images used in this presentation:
Outdated gumshoe.jpg – http://flickr.com/photos/olivander/372385317/
Card catalog – http://flickr.com/photos/deborahfitchett/2372385317/
book of hours manuscript2.jpg ‐ http://flickr.com/photos/jeffrey/89461374/
royal library san lorenzo.jpg ‐ http://flickr.com/photos/cuellar/370663920/
uniform_umbrellas.jpg ‐ http://www.flickr.com/photos/mortimer/221051561/
ponies in field.jpg ‐ http://www.flickr.com/photos/bulle_de/352732514/
caged_tower_melbourne.jpg ‐ http://www.flickr.com/photos/vermininc/2227512763