Dr. Robin Bloor and Philip Howard with Infobright Live Webcast on May 29, 2013 Getting to the bottom of serious situations quickly can separate success from failure in the information economy. Whether you're dealing with customer attrition or dropped phone calls, lost sales or failed machinery, the ability to perform effective root cause analysis can offer tremendous value, especially within critical time windows. This is the domain of investigative analytics – using insights gleaned from complex data sets to identify behavioral patterns, then building predictive models that send the business on a better course. Register for this episode of Hot Technologies to hear veteran Analysts Dr. Robin Bloor of The Bloor Group and Philip Howard of Bloor Research, as they articulate their vision of what you need to utilize investigative analytics. They'll be briefed by Don DeLoach, who will discuss the Infobright solution's ability to analyze large amounts of data quickly and flexibly, thus enabling the kind of root cause analysis that can solve business issues as they arise. Infobright will focus on how their technology is designed to help businesses harness and gain insight from their machine-generated data, which is increasingly generated by instrumentation, aka the Internet of Things.

1. H T
Technologies
2013

2. HOST:
Eric
Kavanagh

3.
THIS
YEAR
is…

4. INVESTIGATIVE
ANALYTICS
=
ž Seeking
previously
unknown
patterns
in
data
ž Extracting
real-­‐time
insight
from
machine
generated
data
ž Being
able
to
query
data
streams,
i.e.,
mobile
data,
web
logs,
geospatial
data,
social
media
data,
etc.

5. ANALYST:
Philip
Howard
Research
Director,
Bloor
Research
ANALYST:
Robin
Bloor
Chief
Analyst,
The
Bloor
Group
GUEST:
Don
DeLoach
CEO
&
President,
Infobright
THE
LINE
UP

6. INTRODUCING
Philip
Howard

7. Exploiting the Internet of Things with
Investigative Analytics
Philip Howard
Research Director, Bloor Research

8. telling the right storyConfidential © Bloor Research 2013
Internet of Things
+ trains, golf courses, icebergs, ATMs, pipeline networks ….

9. telling the right storyConfidential © Bloor Research 2013
Investigative Analytics
" What happened?
" Why did it happen? Is this part of a pattern that indicates that it
might happen again?
" How are we going to react? If it is part of a pattern how can we
can leverage this for business purposes in the future?

10. telling the right storyConfidential © Bloor Research 2013
Some use cases
IBM X-Force survey

11. telling the right storyConfidential © Bloor Research 2013
Requirements

12. telling the right storyConfidential © Bloor Research 2013

13. INTRODUCING
Robin
Bloor

14. INVESTIGATIVE ANALYTICS
+
THE INTERNET OF THINGS

16. It Begins With State…
People, objects,
systems, system
components, etc.
Things can report
state
RFID tags, sensors,
log files, tweets, etc.
Such snippets of data
are events
EVERYTHING HAS
STATE

17. Transactional Event Based
— Corresponds to a system
change
— Process heavy/data light
— Analysis happens
downstream
— Flows as part of a
business process
— Fast
— Corresponds to a state
change
— Process light/data heavy
— Analysis can happen
pre-transaction
— Can be a trigger in a
business process
— Faster
Transactions v Events

18. The Technology March

19. The Three Latencies
Time to develop
Time to deploy
User experience

20. Boiling It Down
It is all about TIME TO INSIGHT – as
long as that is followed by ACTION

21. INTRODUCING
Don
DeLoach

22. Infobright: Investigative Analytics
for The Internet of Things
Don DeLoach, CEO, Infobright
don@infobright.com

23. Internet of Things
Graphic from Sensor Mania! The Internet of Things, Wearable Computing,
Objective Metrics, and the Quantified Self 2.0,JSAN, Nov. 2102

24. Requirements for Practical Investigative Analytics
LOW TOUCHHIGH AVAILABILITY
AFFORDABILITY
TCO
AD HOC
PERFORMANCE SCALABILITY
COMPRESSION
LOAD SPEEDS

25. § Data management
§ Hadoop transforming this area
§ Transparent analytic stack
§ Operational, investigative, predictive
§ Machine-generated, text
§ User consumption:
§ Real-time, interactive visualization & query creation
Emerging Data Analytics Stack:
Days of One-Size-Fits All Are Gone
“Yesterday’s
BI-­‐ETL-­‐EDW
stack
is
wrong-­‐sided
for
tomorrow’s
needs,
and
quickly
becoming
irrelevant.”
Gigamon

26. Intelligence Not Hardware: Knowledge Grid
• Stores
it
in
the
Knowledge
Grid
(KG)
• KG
is
loaded
into
memory
• Less
than
1%
of
total
compressed
data
size
Creates
informa?on
(metadata)
about
the
data
upon
load,
automa?cally
• The
less
data
that
needs
to
be
accessed,
the
faster
the
response
• Sub-­‐second
responses
when
answered
by
the
KG
Uses
the
metadata
when
processing
a
query
to
eliminate
/
reduce
need
to
access
data
• No
need
to
par??on
data,
create/maintain
indexes,
projec?ons
or
tune
for
performance
• Ad-­‐hoc
queries
are
as
fast
as
sta?c
queries,
so
users
have
total
flexibility
Architecture
Benefits

27. Infobright Analytic Suite
Investigative Analytics for Machine-generated Data:
§ High performance ad-hoc query capabilities—enabling real-time information insights at
the speed of business
§ Extremely efficient (footprint, compression, data load) analytic engine designed for
enterprise software deployments, OEM/embedded configurations and enterprise-ready
appliance configurations proven in production
§ Install to analytics in hours: Infobright is designed for time to value
§ Integrated with the leading Hadoop, BI and ETL players
Operational
Simplicity
High
Performance
Efficient
Form Factor
Infobright sets the bar for
query performance, form
factor, and analytics
business impact

28. AFTERBEFORE
What is needed today (and tomorrow)?
MACHINE
DATA
MACHINE
DATA
DATABASE
ADMINISTRATORS
HARDWARE
HARDWARE
APPLICATION
APPLICATION

29. Embedded Database for M2M/Internet of Things
Low Admin: Do not want to
force users to require DBAs to
keep solution running
Load Speeds: Ingestion rates
continue to increase, placing
heavy burden on solutions
High Compression: Want to
keep longer histories in less
space
Lower TCO: Resulting in
better value for customers,
better margins for providers
Stripped Away “DBA” tax
requirement required by
previous versions
Ingesting over 1TB/Hour,
with significant headroom
beyond that
Over 3X the retention period
and a 5X simultaneous
reduction in storage
requirement
Lower TCO for users,
higher margins for JDSU
Little to No
Admin
Fast Load
Speeds
20:1+
Compression
Exceptional Ad
Hoc Query
Performance
Very Low TCO
REQUIREMENTS EXAMPLE: JDSU

30. Embedded Database for M2M/Internet of Things
Low Admin: Looking for
would ensure customers have
fast access to data
Load Speeds: Handle
projected 70% growth rate in
mobile messaging
High Compression: Need to
increase data stored without
increase in storage
requirements
Lower TCO: Competitive
flexibility of lower cost with
higher value-add services
No indexes, data partitioning
or manual tuning. No need for
dedicated DBAs.
100,000 records per second
at peak making data available
for analysis within minutes
Increased to 90 days of data
stored in less hardware due
to drastic compression
TCO only 20% of the cost of
competitors. Major wireless
carrier wins with this solution
Little to No
Admin
Fast Load
Speeds
20:1+
Compression
Exceptional Ad
Hoc Query
Performance
Very Low TCO
REQUIREMENTS EXAMPLE: MAVENIR

31. Embedded Database for M2M/Internet of Things
Low Admin: Do not want to
force users to require DBAs to
keep solution running
Fast Query Performance:
Customers depend on this
analysis to tune networks
High Compression and Fast
Load Speeds: Need to meet
business growth projections
Lower TCO: Resulting in
better value for customers,
better margins for providers
Low touch administration
reduces friction and latency
for queries
Sub-second web-based
queries critical to customers
to tune the network
High data compression
rates and load speed allow
for projected growth rate of
data volume
Low OPEX = better margins
and more confidence planning
capacity to meet growth
Little to No
Admin
Fast Load
Speeds
20:1+
Compression
Exceptional Ad
Hoc Query
Performance
Very Low TCO
REQUIREMENTS EXAMPLE: POLYSTAR

32. Embedded Database for M2M/Internet of Things
High Compression:
Projected data growth
outpacing storage capacity
Ad hoc Query: Utilities want
to drive customer participation
in efficiency-related programs
Fast Load Speeds: Need to
integrate several data streams
quickly
Lower TCO: Solution needs
to affordably meet business
needs
No additional hardware or
manual set-up in the form of
data indexing or partitioning
Fast flexible reporting (20K
reports in first 3 months) help
utilities better drive business
Better business answers
due to combined analysis of
behavioral, demographic and
log data
Low TCO translates to better
pricing and stronger
competitive positioning
Little to No
Admin
Fast Load
Speeds
20:1+
Compression
Exceptional Ad
Hoc Query
Performance
Very Low TCO
REQUIREMENTS EXAMPLE: OPOWER

33. Momentum in the M2M/Internet of Things
Applications in the Internet of Things will all require Low Touch, High
Capacity and High Density; and Low Cost deployments
Smart Grids
Smart
Vehicles,
Smart Cities
Mobile Health
Others..
BEFORE
MACHINE
DATA
DATABASE
ADMINISTRATORS
HARDWARE
APPLICATION
AFTER
MACHINE
DATA
HARDWARE
APPLICATION

34. Thank
You

36. The
Archive
Trifecta:
• Inside
Analysis
www.insideanalysis.com
• SlideShare
www.slideshare.net/InsideAnalysis
• YouTube
www.youtube.com/user/BloorGroup
THANK
YOU!