The Briefing Room with Analyst Dr. Robin Bloor and SkyTree
Live Webcast on June 24, 2014
Watch the archive:
https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=1da2b498fc39b8b331a5bbb8dea2660f
With data growing more complex these days, many organizations are looking for ways to make sense of new information sources. The goal? Sprint ahead of the competition by exploiting fast-moving opportunities. The challenge? The data volumes, variety and velocity call for significantly greater horsepower than ever before. That’s where machine learning comes into play, and it’s already fundamentally changing the Big Data Analytics landscape.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor as he explains how advanced analytics technology can transform the enterprise. He’ll be briefed by Martin Hack, CEO of Skytree, who will tout his company’s machine learning solution for big data. Hack will discuss the critical challenges facing today’s data professionals, and present use cases to show how machine learning can help organizations leverage big data as a capital asset. He’ll specifically address the power of predictive analytics, which can help companies seize opportunities and prevent serious problems.
Visit InsideAnlaysis.com for more information.
1. Grab some coffee and enjoy
the pre-show banter before
the top of the hour!
2. The Sky’s the Limit – The Rise of Machine Learning
The Briefing Room
3. Twitter Tag: #briefr
The Briefing Room
Welcome
Host:
Eric Kavanagh
eric.kavanagh@bloorgroup.com
@eric_kavanagh
4. ! 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!
Twitter Tag: #briefr
The Briefing Room
Mission
5. This Month: ANALYTICS & MACHINE LEARNING
July: INNOVATIVE TECHNOLOGY
August: BIG DATA ECOSYSTEM
Twitter Tag: #briefr
The Briefing Room
Topics
2014 Editorial Calendar at
www.insideanalysis.com/webcasts/the-briefing-room
6.
7. Twitter Tag: #briefr
The Briefing Room
Analyst: Robin Bloor
Robin Bloor is
Chief Analyst at
The Bloor Group
robin.bloor@bloorgroup.com
@robinbloor
8. Twitter Tag: #briefr
The Briefing Room
Skytree
! Skytree offers a machine learning platform that focuses on
advanced analytics
! Skytree Server includes data setup and predictive analytics
modeling, and it can be installed across a cluster, locally or
in the cloud
! Skytree Server is Hadoop-ready and certified on Cloudera 5
9. Twitter Tag: #briefr
The Briefing Room
Guest: Martin Hack
Martin Hack is President, CEO & Co-Founder of
Skytree. Mr. Hack has 20 years of experience
creating new technology products, services and
strategies. He has launched game changing
products, driven worldwide strategies and helped
set de-facto industry standards. As an expert on
Trusted Computing, Virtualization and High
Performance environments he became a sought-after
advisor to Fortune 500 companies and
government organizations. He has been involved in
all aspects of the product life cycle including
engineering, product management, marketing,
business development and sales. He also developed
and introduced products and services into
commercial and government segments for
organizations such as Sun Microsystems, SonicWALL
and GreenBorder (acquired by Google).
11. SAME DATA.
BETTER RESULTS.
MARTIN HACK
CEO
martin@skytree.net
THE MACHINE LE A R N I N G COMPANY ®
!
11
12. Our Vision
THE DATA DRIVEN ENTERPRISE
Machine learning: !
The modern science of finding patterns and making predictions from data:!
!
multivariate statistics, data mining, pattern recognition, advanced/predictive analytics!
THE MACHINE LE A R N I N G COMPANY ®
12
POWERED BY MACHINE LEARNING
13. Machine Learning is a Strategic Imperative!
50’s-70s 80’s-90’s Mid 90’s - Today
THE MACHINE LE A R N I N G COMPANY ®
13
1st Wave:
Artificial Intelligence
Pattern Recognition
Universities
Technology
Evolution!
Application
Evolution!
2nd Wave:
Neural Networks
Data Mining
Science
Credit scoring
OCR
Now: Machine Learning on Big Data
3rd Wave:
Machine Learning:
Convergence
Sales / Marketing
Finance
Biotech
Retail
Telco
Government
14. What Has Changed? !
Arrival of Big Data!
o Leading companies no longer have small data !
!!
Companies now compete on predictive
analytics !
o Accuracy = $ !
o Speed = $ !
• BUT these are computationally limited (in current
systems) !!
THE MACHINE LE A R N I N G COMPANY ®
14
15. Most Popular
Machine Learning Applications!
THE MACHINE LE A R N I N G COMPANY ®
15
Real-time!
16. 15,000% increase in
Data Scientists job
postings. (source: FICO)!
140,000 – 190,000
open Data Scientists
positions. (source: VentureBeat)!
! 16
THE MACHINE LE A R N I N G COMPANY ®
17. The War Of The Algorithms!
!
q Best Algorithm wins!
q Zero Sum Game, Winner Takes all!
q There is no second place!
17 THE MACHINE LE A R N I N G COMPANY ®
18. Global Leaders Select Skytree
WORLD’S
LEADING:
Consumer
Electronics
Content recommendation
Logis3cs
&
Shipping
Anomaly detection
Automobile
Web
Portal
On-board destination recommendation
Ad targeting
Financial
Services
&
Credit
Card
Customer lead scoring, fraud, credit risk scoring
THE MACHINE LE A R N I N G COMPANY ®
19. Operations Optimization !
• Business Challenges:!
– Healthcare organizations often staff!
for peak load.!
• Very expensive, not driven by data &!
predictive analytics.!
• Business Opportunities: !
– Several leading healthcare organizations are working with Skytree to apply Machine
Learning & Predictive Analytics to staffing schedules.!
» Will optimize utilization and minimize cost.!
» Will allow for the redeployment of resources to other locations (related
hospitals/clinics/labs) for better utilization.!
THE MACHINE LE A R N I N G COMPANY ®
!
19
20. Lead Scoring!
• Business Challenges:!
– Leading businesses need to improve!
their lead targeting methods.!
• At present, businesses are approaching many !
attractive customers w/ campaigns that do not resonate !
with the target, and !
• businesses are approaching customer prospects!
who are less attractive than their analysis indicates.!
– Result: They often miss on truly attractive customer leads and win
customers who are not the most profitable. !
• Business Opportunities: !
– Skytree is enabling next generation lead generation for a leading financial services
organization.!
THE MACHINE LE A R N I N G COMPANY ®
20
21. Speed = Accuracy = Revenue
Global Financial Institution!
Use Case: Micro Targeting!
THE MACHINE LE A R N I N G COMPANY ®
21
1,250x Speedup
BEFORE:!
1,200 Cores @100 Nodes
!
Runtime: 100 Minutes!
Accuracy: 57%!
100!
Runtime
(minutes)!
NOW: SKYTREE!
12 Cores @1 Node!
!
Runtime: 8 Minutes!
Accuracy: 60%!
8!
22. Fraud Detection!
• Business Challenges:!
– Fraud perpetrators continue to get more sophisticated.!
• The issue has exceeded the ability of investigators & !
analysts to manage the issue.!
• Business Opportunities: !
– Skytree is working with customers to apply Machine Learning & Predictive Analytics
to:!
» Better anticipate & detect fraud,!
» Mitigate impact,!
» Improve detection systems over time.!
THE MACHINE LE A R N I N G COMPANY ®
22
23. Ease of Use – Smart Machine Learning
Automation through AI!
Graphical user interface
Self-Aware Algorithm Selection
Examines
the context of your data and selects the best
fitting algorithm from our extensive algorithm library.
Information Auto Sensing
Automatically
detects whether your data is text, table,
series (time series, forecasting) or network data, just
point
and click.
Instant Interactive Reports
Generates
science-based, interactive reports that are
defensible and "Boss Ready".
23 THE MACHINE LE A R N I N G COMPANY ®
24. Machine Learning = Faster Time to Insight!
THE MACHINE LE A R N I N G COMPANY ®
24
Skytree Machine Learning
Better, Faster Results
25. Company Overview!
Martin Hack, CEO & Co-Founder
Sun, GreenBorder (Google)!
Prof. Alexander Gray, PhD, CTO & Co-Founder
National Expert on Large-Scale, Fast ML Algorithms!
Prof. Leland Wilkinson, PhD, VP Data Visualization
Creator of Grammar of Graphics, SYSTAT (SPSS/IBM)!
Burke Kaltenberger, VP Worldwide Field Operations
Infochimps/CSC, MapR, ParAccel!
Jin H. Kim, PhD, VP Marketing
Tom Sawyer Software, Vitria, Mentor Graphics!
Jon Skogland, VP Finance
Silicon Graphics, Kontagent, Intuitive Surgical!
!
!
!
Prof. Michael Jordan, UC Berkeley: machine learning ‘godfather’!
Prof. David Patterson, UC Berkeley: systems (inventor RISC, RAID)!
Prof. Pat Hanrahan, Stanford: data visualization (Tableau, Pixar)!
Prof. James Demmel, UC Berkeley: high-performance computing (LAPACK)!
EXECUTIVE
TEAM!
TECHNICAL !
ADVISORY!
BOARD!
25 THE MACHINE LE A R N I N G COMPANY ®
26. Skytree Hadoop Distribution Architecture!
Web Services Graphical User Interface
1.x
Spark
HDFS and MAPR Data Platform
THE MACHINE LE A R N I N G COMPANY ®
2.x/
YARN
ZooKeeper
Data Sources / Targets
OLTP / EDW
27. Skytree Server
Enterprise ready and easy to deploy!
THE MACHINE LE A R N I N G COMPANY ®
Big Data Sources
• Flat files
• Data Warehouse
• RDBMS
• NoSQL
• Hadoop
27
Outputs
• Business
reports
• Systems
monitoring
• Client
application
32. Big Data and Analytics
There MAY be some Big Data
applications that are not about
data analytics.
If so,
nobody is talking about them…
33. The Driving Force: Insight
Insight comes from
DATA
EXPLORATION
Foresight
INSIGHT
Hindsight Oversight
34. The Latency Issue
u In data analytics latency
is time to value…
u Traditional analytic
latencies:
• Data access
• Data preparation
• Model development
• Execution
• Implementation
• Model audit & update
35. Computer vs. Human
First they beat us
at chess.
Then they beat us
at Jeopardy.
Now they’re gonna
beat us at running
a business!
36. The Impact of Machine Learning
First you need to organize for it
Then you need to architect for it
Then you need to test and experiment
Then you need to DEPLOY!
37. The Future of Analytics
There is no question that machine learning
will dominate data analytics
The question is:
Who will implement it EARLY and
implement it WELL?
38. u Some people recommend a gradual approach to
increasing BI sophistication. They suggest that
the next step for most organizations is predictive
analytics. What do you think?
u Do you need “Data Scientist” skills to properly
exploit machine learning?
u What is the typical design and implementation
process?
u Why not just use the open source code that is
available?
39. u What is your performance advantage? Please
provide details.
u Are there areas of data analytics where
machine learning is not applicable?
u How does this capability integrate with existing
BI solutions?
