The Briefing Room with Dean Abbott and Tableau Software Live Webcast July 23, 2013 http://www.insideanalysis.com Today’s desire for analytics extends well beyond the traditional domain of Business Intelligence. That’s partly because business users are realizing the value of mixing and matching all kinds of data, from all kinds of sources. One emerging market driver is Cloud-based data, and the desire companies have to analyze this data cohesively with their on-premise data sets. Register for this episode of The Briefing Room to learn from Analyst Dean Abbott, who will explain how the ability to access data in the cloud can play a critical role for generating business value from analytics. He’ll be briefed by Ellie Fields of Tableau Software who will tout Tableau’s latest release, which includes native connectors to cloud-based applications like Salesforce.com, Amazon Redshift, Google Analytics and BigQuery. She’ll also demonstrate how Tableau can combine cloud data with other data sources, including spreadsheets, databases, cubes and even Big Data.

1. The Briefing Room
Bridging the Gap: Analyzing Data In and Below the Cloud

2. Twitter Tag: #briefr The Briefing Room
Welcome
Host:
Eric Kavanagh
eric.kavanagh@bloorgroup.com

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! 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!
Mission

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Topics
This Month: CLOUD
August: ANALYTIC PLATFORMS
September: ANALYTICS

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Cloud

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Analyst: Dean Abbott
Dean Abbott is President of Abbott Analytics, Inc.
Dean has more than 21 years of experience
applying advanced data mining, data preparation
and data visualization methods in real-world
data-intensive problems, including fraud
detection, response modeling, survey analysis,
planned giving, predictive toxicology, signal
process and missile guidance. He has developed
and evaluated algorithms for use in commercial
data mining and pattern recognition products,
including polynomial networks, neural networks,
radial basis functions and clustering algorithms.
He is a seasoned instructor, having taught a wide
range of data mining tutorials and seminars.

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! Tableau builds software for data visualization, business
intelligence and analytics
! Its latest release, Tableau 8.0, offers new capabilities such
as native connectors to cloud-based applications
(Salesforce.com, Google Analytics and BigQuery, Amazon
Redshift) and Tableau Online, a hosted version of Tableau
Server
! These added features enable access to BI and analytics in
the cloud using both on-premise and cloud-based data
Tableau Software

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Ellie Fields
Ellie Fields is the Director of Product
Marketing at Tableau Software. She
has spoken at numerous industry
events for business intelligence as
well as for data journalism. Prior to
Tableau, Ellie worked at Microsoft
and in late-stage venture capital.
Ellie is a graduate of Rice University
and the Stanford Graduate School of
Business.

11. Tableau Desktop
For Anyone
• Explore and visualize data
• Self-service analytics for everyone
• Blazing speed against massive data

12. Tableau Server
For Organizations
• Complete business intelligence system
• Web dashboards and applications
• Secure information management
• Enterprise scalability

13. Fast, easy, beautiful
self-service analytics
for everyone

16. Tableau Server
running
in the Cloud

17. Start fast
Stay secure
Work anywhere
Grow smart

18. Fast,
Flexible
Deployment
76%
Choose
Cloud
for
Speed
to
Deploy
IDC

19. Sandeep
Varma,
Herring
Creek
Capital

20. Easy
Mobile
Access
No
VPN
or
DMZ
Required
Easy
to
Secure
Easy
sharing

21. Greg
Sheldon,
Chief
InformaKon
Officer
at
Elite
Brands.
Greg
Sheldon,
Chief
InformaKon
Officer
at
Elite
Brands.

23. Tableau
Online

24. Tableau
Online
On-­‐Premise
Data
(push)

25. Tableau
Online
Cloud
Data
(pull)
On-­‐Premise
Data
(push)

26. Usable
BY
EVERYONE
Accessible
EVERYWHERE
Easily
EVERYPLACE
Applicable
TO
ALL
DATA

27. Simple
Pricing
$500
per
user
per
year

28. 200

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Perceptions & Questions
Analyst:
Dean Abbott

31. Analyzing Data
in the Cloud
Dean Abbott
Abbott Analytics, Inc.
July 23, 2013
Email: dean@abbottanalytics.com
Blog: http://abbottanalytics.blogspot.com
Twitter: @deanabb
© Abbott Analytics, Inc. 2001-201331

32. What is a Cloud?
• Hardware that isn’t “here”
• Flexible hardware and virtual machines
• Don’t worry about size—grow as needed
• Don’t worry about time—only pay for what you use
32

33. How Big is Big?
• Most analytics projects use small data
• 10Ks to 1Ms of records; < 1GB; desktop tools
• Some analytics projects use medium data
• > k GB; too big for comfortable laptop/standard desktop
• Fits into server -> client – server architecture
• A few analytics projects use true big data
• 10s GB active processing
• Leverage specialty software / hardware
• Column stores; high performance database
• Cloud (Google File System, Hadoop)
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34. Predictive Analytics in the Cloud
• Analytics (BI, BA, PA, DM): Data-Driven Decisions
• Predictive Analytics: Data-Driven Decisions using
machine learning / statistics / AI
• Automated discovery of which variables relate to target
variable
• Building potentially complex, multi-dimensional, nonlinear
relationships between inputs and target
• Visualization of PA: Data Insight
• Show how variables relate to target
• Informs decision-makers why key variables are important in
predictions

35. Obstacles
• Setup costs
• Not IT setup costs, but there are still infrastructure costs
• Integration with operational systems
• The cloud environment is a silo.
• Connectivity
• Real-time operational systems vs. the analyst sandbox
• Security
• Or perception of security issues
• Public/secure cloud vs. private servers
© Abbott Analytics, Inc. 2001-201335

36. Questions
1. How flexible is the query environment for visualization?
How easy is it to change the variables and slices one
needs to visualize? (cloud vs. local OLAP cubes)
2. How would an analyst interact with the cloud
environment to do Exploratory Data Analysis (EDA)?
1. What is the framework to connect with data mining /
predictive analytics software?
3. What strategies do you recommend for reducing data
flow to / from the cloud environment?

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July: CLOUD
August: ANALYTIC PLATFORMS
September: ANALYTICS
Upcoming Topics
www.insideanalysis.com

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Thank You
for Your
Attention
Image credit for Slide 5: yukipon / 123RF Stock Photo