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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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JANUARY: Big Data
February: Analytics
March: Open Source
April: Intelligence
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Big Data
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COMBINING DATA
GLEANING INSIGHTS FINDING MEANINGFUL DATA SETS
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Analyst: Lyndsay Wise
Lyndsay Wise is the president and founder of WiseAnalytics, a boutique analyst and consultancy firm. Lyndsay has ten years of IT experience in business systems analysis, software selection, and implementation of enterprise applications. She provides consulting services for small and mid-sized companies and conducts research into leading technologies, market trends, BI products and vendors, mid-market needs, and data visualization.
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! Tableau builds software for data visualization, business intelligence and analytics
! Their stated mission is to help people see and understand data
! Its platform is designed for ease of use by both power and casual users
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.
Then: Big data everywhere, various data stores, data stuck and no insight – billions of rows of data.
Now: Self service reporting for everyone. 800+ people using multiple sources of data to answer questions.
The Problem: Old School Software Traditional technologies are complicated, inflexible and slow moving
From Management To Insight
• Managing big data infrastructures are no longer enough
• Organiza6ons require a way to get value out of their data
• Evalua6ng BI on its merit in rela6on to big data management is becoming a must have for many businesses
Ques6ons
• Big data is defined a variety of ways in the industry today – what is Tableau’s sweet spot within big data analy6cs? Does it extend beyond diversity and volume?
• The two examples you used focus on data consolida6on and self-‐service access for hundreds (if not thousands) of people. How are other companies using Tableau to address their big data challenges?
Ques6ons
• What is the real differen6ator/value proposi6on in comparison to how other similar vendors address big data challenges?
• When would a company not want to use Tableau to manage their big data?
• How does Tableau provide faster data access (slide 11 reference)? Do limita6ons exist, i.e., does this depend on the plaZorm, etc.?
Ques6ons
• What limita6ons exist in rela6on to data volumes or added complexi6es?
• What trends do you see in rela6ons to customer use and the transi6on from big data storage to big data analy6cs?
• For organiza6ons evalua6ng Tableau for big data access, what do they need to know or prepare in advance to get the system up and running?
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Upcoming Topics
This month: Big Data
February: Analytics
March: Open Source
April: Intelligence
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