The Briefing Room with Robin Bloor and Actian
Slides from the Live Webcast on May 8, 2012
Getting into an analytical rhythm requires more than just data and time. One of the key ingredients is speed, specifically, the ability to ask questions of your data in a conversational fashion. This has traditionally been the domain of Operational Data Stores or OLAP cubes, but there are new options today that don't rely so heavily on the kind of maintenance necessary to keep those solutions humming. Today, there are specialized database technologies that can fuel conversational analytics.
Check out this episode of The Briefing Room to learn from 30-year IT Analyst Robin Bloor, Ph.D., who will chart the evolution of specialty database technology. He'll explain how vector-based processing in particular changes the game of analytics. Bloor will be briefed by Fred Gallagher of Actian, who will discuss the technical advantages of the Actian Vectorwise database, including vector-based processing, on-chip memory optimization, parallel execution, column-based storage and self-optimizing compression.
For more information visit: http://www.insideanalysis.com
Watch us on YouTube: http://www.youtube.com/playlist?list=PL5EE76E2EEEC8CF9E
3. Reveal the essential characteristics of enterprise
software, good and bad
Provide a forum for detailed analysis of today’s
innovative technologies
Give vendors a chance to explain their product to
savvy analysts
Allow audience members to pose serious questions...
and get answers!
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Tuesday, May 8, 12
5. Ultimately analytics is about businesses making optimal
decisions, although the range of technologies that inhabit
this area is wide: statistical analysis, data mining, process
mining, predictive analytics, predictive modeling, business
process modeling and additionally complex event
processing.
With the advent of big data, analytics has become “big
analytics” with organizations diving into large heaps of data
that previously was not available or usable.
When you combine analytics with Big Data response times
can become barriers to meeting business goals in the
needed time frame. Database performance become a key
determining factor.
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Tuesday, May 8, 12
6. Robin Bloor is Chief
Analyst at The
Bloor Group.
Robin.Bloor@Bloorgroup.com
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Tuesday, May 8, 12
7. Actian Corporation (formerly Ingres Corp.) is a database and
software development company. Its premier database
platform is Vectorwise a new, innovative and highly
performant database.
Vectorwise is the only database platform we are aware of that
implements parallelism at every level from the processor core
to data storage and is thus both scalable and highly
performant.
Actian also boasts a cloud-based app development platform
tailored for building mobile applications (actian Apps) which
are lightweight consumer-style applications that automate
business actions triggered by real-time changes in data.
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Tuesday, May 8, 12
8. Fred Gallagher GM, Vectorwise at
Actian Corporation is responsible
for managing the business
activities for Actian’s
breakthrough product. He joined
Actian in 2006 as vice president
of business development. Prior to
joining Actian, Fred worked for
Qlusters, where he was
responsible for worldwide sales,
marketing, and business
development. Previously, he was
at Seagate Technology. Fred holds
Bachelor of Arts and an MBA from
Stanford University.
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Tuesday, May 8, 12
9. Vectorwise
Enabling Conversational Analytics
Fred Gallagher
General Manager of Vectorwise, Actian Corporation
Tuesday, May 8, 12
11. Vectorwise
Enabling Conversational Analytics
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12. Conversational Analytics:
What is it?
The ability to interact with your data in real-time
and ask follow up questions in the moment
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13. Conversational Analytics:
The Need for Speed
Empower your employees to get insight quickly
Be faster than your competition
Optimize your business
Discover new opportunities first
13
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14. Vectorwise Optimizes the Modern Technology
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16. Vectorwise Optimizes the Modern Technology
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17. Vectorwise Optimizes the Modern Technology
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Tuesday, May 8, 12
18. Vectorwise:
What Makes it So Fast?
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compression
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21. What Makes Data Useful?
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analyze granular data quickly!
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27. Summary
Most DBMS’s designed for 1980’s hardware
Vectorwise exploits 30 years of hardware advances
Processors, cache and memory
Solves the problem instead of throwing money at the problem
Several proof-points with customers and benchmarks
Vectorwise speed creates value from data
Brings data to solve business problems and take action in real-time
Optimize data infrastructure and your business
Competitive advantage
OQ
Tuesday, May 8, 12
32. Most of the Big Data opportunity is really a Big Analytics
opportunity. Companies rarely think in terms of “better
dashboards” when they embark on Big Data projects
Big Analytics can be thought of as involving 2 distinctly
different latencies:
Acceptable latency
Truly actionable latency
The second of these is an imperative in some business
areas e.g. credit card fraud, telco customer churn, risk
analysis, etc.
Tuesday, May 8, 12
33. Analytics needs to be a precursor to action. There are
very few new analytical techniques. Indeed most
analytical techniques are old and well understood and
well established.
The challenge with analytics is whether analytical activity
is well integrated within a business process, so that when
a trend changes, a business can respond quickly (if it is a
contextual trend) and quickly enough (if it is a
competitive trend).
Sometimes businesses perform the analytics but fail to
act. It’s more common than it should be.
Tuesday, May 8, 12
35. Big Analytics is here to stay
In some analytical application areas
speed is desirable, in others speed is
critical
Analytic speed depends upon the
database engine, but also data flow
architecture
Business effectiveness depends upon
integration with the business process
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Tuesday, May 8, 12
36. Questions
What specific application areas do you regard as sweet
spots for VectorWise
Benchmarks may be impressive, but in the end they are
synthetic. So the question is how well does Vectorwise
perform:
With mixed query workloads (short and long queries) on
large volume data?
With heavy duty analytic workloads? (what difference
does vector processing make in this area?
Can it handle fast ingest with simultaneous query
workloads.
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Tuesday, May 8, 12
37. Questions
In what way is Vectrowise available as a cloud service. Is
there anythign other than speed which differentiates you?
Which vendors do you tend to meet in competition?
Who have been the early adopters of this kind of capability
and what kind of business problems are they trying to solve?
Which vertical business sectors have shown most interest in
Vectorwise and which have shown the least
How important is Vectorwise as a complement to your
mobile App platform?
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Tuesday, May 8, 12