The Briefing Room with Dr. Robin Bloor and Tableau
Live Webcast Feb. 24, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=bdd2466b23e1546c79230fbfd374f348
The path to analysis can take many forms, but the mantra in today's competitive world is speed. No longer can companies take months to roll out analytical applications. Self-service is the standard call from business analysts looking to optimize their operations. Designing an intuitive self-service environment can be a serious challenge, however. That's why many companies are employing proven methodologies for rolling out valuable analytical solutions.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor, as he explains why a methodology can help ensure the success of analytical applications. He'll be briefed by Ted Wasserman of Tableau Software, who will discuss his company's Drive initiative, which was designed to help companies foster an analytic culture. He'll explain how Drive provides a roadmap for analytic success that focuses on securing quick wins, then building momentum with an interactive, business-focused approach.
Visit InsideAnalysis.com for more information.
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Welcome
Host:
Eric Kavanagh
eric.kavanagh@bloorgroup.com
@eric_kavanagh
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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
February: DATA IN MOTION
March: BI/ANALYTICS
April: BIG DATA
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Analyst: Robin Bloor
Robin Bloor is
Chief Analyst at
The Bloor Group
robin.bloor@bloorgroup.com
@robinbloor
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Tableau
Tableau builds software for data visualization, business
intelligence and analytics
Its products include Tableau Desktop, Tableau Public,
Tableau Online and Tableau Drive
Tableau Drive is a methodology designed to create an
analytic culture throughout an organization
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Guest: Ted Wasserman
Ted Wasserman is Senior Product Manager at Tableau
Software. Ted leads Tableau's data strategy and execution.
This includes product planning, setting the strategic vision,
and leading the execution of new features and integration
with technology vendors. Prior to joining Tableau, Ted
worked at IBM for seven years in the Information
Management division in a number of different roles
including technical consulting, channel marketing, and
business development. Ted holds a bachelor's degree and
master's degree in computer science from Queen's
University.
12. Business IT
Requirements Planning Development Test
User
Experience
Production Analytics
The traditional model fails business
users and IT
Users must wait… and are rarely satisfied.
13. The Agile model improves waterfall
Production Development
Planning
User
Acceptance
Test
Subject Matter
Expertise
(requirements)
Keeping business
closer to development
improves quality,
speed and flexibility.
14. How do you provide the programmatic
support needed to scale self-service
analytics?
15. What is Drive?
Tableau Drive is a business intelligence
deployment methodology that draws from agile
methods and is informed by the most analytically-
minded companies in the world.WithTableau
Drive, business and IT jointly own the analytics
platform.And there is the opportunity to
continuously evolve and grow, even at the scale
of an enterprise deployment.
16. IT Role
• Security
• Data architecture
• Scalability
• Training
• Center of Operations
• Enablement intranet
A partnership that works
Business Role
• Creative analytic work
• Driving data acquisition
• Sharing expertise
• Seeking help when needed
• Evangelism
• Catalyzing action
ExecutionEnablement
19. Foundation
building
Foundation: technical
• Sandbox and production environments
• Governance security policies
• Data dictionary
• Clean, managed published data sources
• Processes: promotion, escalation, licensing
• Online help desk
25. The Old Waterfall
u Feasibility
u Requirements
u Design
u Development
u Testing
u User testing
u Production
u Review
In the beginning it was the only
method for organizing projects.
And there were problems…
Requirements
Feasibility
Design
Development
Testing
User
Testing
Deployment
Review
Cost Multiple
For Correcting
Errors
2-3
2-3
2-3
2-3
2-3
2-3
Cumulative
Multiple
4-9
2-3
8-27
16-81
32-243
64-729
26. A Fundamental Error
u The engineering project
flow…
u IT never included the
prototyping stage
u This was never realized
until the early 1990s when
RAD tools emerged
u It includes a feedback loop
u This was the birth of the
“Agile Development”
movement
2-3
4-9 or 2-3
Planning
Requirements
Design
Prototyping
Deployment
Integration
Testing
27. The Fundamental Mismatch
§ Knows the technology
capabilities
§ Has poor knowledge of
the real business
process
§ Does not know for sure
what the business
process will become
§ Knows the business
process
§ Has poor knowledge of
the technology
capabilities
§ Does not know for sure
what the business
process will become
The Developer The End User
This mismatch will never be truly resolved until the
developer and the end-user are the same person
28. For BI/Analytics…
u The major difference: The
development tools are
business oriented
u Instead of design we have
exploration discovery
u Prototyping and iteration
are still key
u Deployment may require
technicians
u This has a right to claim
agility
Business
Process
Design
Exploration
Discovery
Prototyping
Deployment
Integration
Testing
29. The Self Service Trend
The self-service trend in Analytics
and BI is an effort to resolve the
fundamental mismatch
30. u How often do you encounter “old
methodologies?” How often are they a barrier to
progress?
u Drive seems to require executive sponsorship.
Does that mean you have to sell directly to
executives?
u How onerous is the technical side of Foundation?
31. u How much help do businesses/IT need to build
and deploy Foundation?
u Are we short of skilled staff for BI and Analytics
(clearly, the evidence suggests we are currently
short of data scientists…)?
u Do we want business analysts to become ersatz
data scientists?