The Briefing Room with Mike Ferguson and Alteryx Live Webcast on Feb. 12, 2013 Today's savvy organizations know that a streamlined approach to data and applications can put the power of predictive analytics right where it needs to be: in the hands of the user. Sure, training is still required, but a real revolution is underway for the graphic design of such user interfaces. Central to this overhaul of design is the concept of intelligent, simple workflow, which enables users to get things done in an orderly fashion. Check out the slides for this episode of The Briefing Room to hear analyst Mike Ferguson of Intelligent Business Strategies as he explains why interface design and workflow must go hand-in-hand. He will be briefed by Matt Madden of Alteryx, who will tout his company’s predictive platform, a solution that leverages an array of traditional and Big Data analytics applications, designed for problem solvers and decision makers. Madden will also provide several customer use cases that demonstrate the new normal in predictive analytics.

1. The Briefing Room

2. Welcome
Host:
Eric Kavanagh
eric.kavanagh@bloorgroup.com
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3. Mission
! 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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4. FEBRUARY: Analytics
March: OPERATIONAL INTELLIGENCE
April: INTELLIGENCE
May: INTEGRATION
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5. Analytics
Hindsight and Insight are fairly common
© Cristian Farcas | Dreamstime.com
PREDICTIVE CAN BE ELUSIVE
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6. Analyst: Mike Ferguson
Mike Ferguson is Managing Director of
Intelligent Business Strategies Limited. As an
independent analyst and consultant, he
specializes in business intelligence, data
management and enterprise business
integration. With more than 30 years of IT
experience, Mike has consulted for dozens of
companies, spoken at events all over the
world and written numerous articles.
Formerly he was a principal and co-founder
of Codd and Date Europe Limited – the
inventors of the Relational Model, a Chief
Architect at Teradata on the Teradata DBMS
and European Managing Director of DataBase
Associates where he was a partner with Colin
White.
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7. Alteryx
! Alteryx provides an enterprise-class analytics platform
which enables users to combine Big Data with information
assets across the organization
! Analysts can perform predictive and spatial analytics, as
well as produce sharable apps
! Alteryx’s Strategic Analytics Software is a desktop-to-cloud
solution that combines business data, industry content and
spatial processing
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8. Matt Madden
Matt Madden is Senior Product
Marketing Manager at Alteryx. He has
over 13 years of experience helping
organizations realize the power and
benefits of analytics in the roles of
Sales and Marketing.
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9. The New Normal:
Predictive Power on the Front
Lines
Matt Madden- Sr. Product Marketing Manager
© 2012 Alteryx, Inc. 9

10. Decisions start with data
• New sources creating huge volumes of “Big Data”
• Social Media
• Sensors
• Radio Frequency ID (RFID)
• Log files
Big Data
• More Data= More Questions= More Decisions
Value
• Predictive analytics provides tremendous potential
for high-value analysis & decisions
• Customer Analytics Predictive
• Marketing Optimization
• Market Basket Analysis
• Inventory Analysis
• Reducing Churn
© 2012 Alteryx, Inc. 10

11. Predictive Analytics: A Competitive Imperative
• Traditional Business Intelligence (BI) platforms are backward-looking
• Predictive Analytics represents the highest value
• Historically, Predictive Analytics have also been the most complex to
implement
Prediction
Monitoring
Reporting- What happened?
• Query, reporting tools
Analysis
Analysis- Why did it happen?
• OLAP & visualization tools
Reporting
Monitoring- What’s happening now?
• Dashboard, scorecards
Prediction- What might happen?
• Predictive analytics
*Source: The Data Warehousing Institute (TDWI) www.tdwi.org
© 2012 Alteryx, Inc. 11

12. The “Old Way” Doesn’t Work Any More
• Time-consuming • Expensive
• Requires specialized expertise • Hard to rapidly iterate
© 2012 Alteryx, Inc. 12

13. Alteryx Strategic Analytics: A New Approach
• Faster time from question to insight • Lower cost
• No specialized skills required • Iteration-friendly
© 2012 Alteryx, Inc. 13

14. Alteryx Big Data Architecture – App Design
Analytic Apps 3rd Party Multiple Format
Consumption
Analytics Output
Publish
Output
Statistical Predictive Spatial
Analytics Understand and Drive foresight with Deep understanding of
build models R based tools location intelligence
Personal ETL Agile Database Data Quality Data Management
access & large scale, rapid keep your data define and map
Integration integrate any data processing clean and relationships
data credible between data
Output &
Analytics
Upload
In DB
Ingest
Structured | Semi-structured | Unstructured
Local & Data Big Data
NoSQL Hadoop
Productivity Warehouse Discovery
IT / DW
Team
(Coming soon)
Inter Source Data Integration
© 2012 Alteryx, Inc. 14

15. Alteryx Analytic Workflow – Step 1
All Relevant Data
App & Data
Un-Structured
Content
© 2012 Alteryx, Inc. 15

16. Alteryx Analytic Workflow – Step 1
All Relevant Data
App & Data
Integrate
Un-Structured
Content Integrate any data
source
© 2012 Alteryx, Inc. 16

17. Alteryx Analytic Workflow – Step 1
All Relevant Data
Packaged Market
& Customer Data
Enrich
App & Data
Integrate
Un-Structured
Content Integrate any data
source
© 2012 Alteryx, Inc. 17

18. Alteryx Analytic Workflow – Step 1
All Relevant Data
Packaged Market
& Customer Data
Enrich
App & Data
Integrate
Analyze
Un-Structured
Rapid design of
Content Integrate any data
predictive analytics
source
© 2012 Alteryx, Inc. 18

19. Alteryx Analytic Workflow – Step 1
All Relevant Data
Packaged Market
& Customer Data
Enrich
App & Data
Integrate
Analyze
Un-Structured
Rapid design of
Content Integrate any data
predictive analytics
source
© 2012 Alteryx, Inc. 19

20. Create & Share Analytic Apps in Cloud – Step 2
Assemble App
© 2012 Alteryx, Inc. 20

21. Create & Share Analytic Apps in Cloud – Step 2
Assemble App
Publish
Private or Public Cloud
© 2012 Alteryx, Inc. 21

22. Create & Share Analytic Apps in Cloud – Step 2
Assemble App
Publish
Private or Public Cloud
Run
© 2012 Alteryx, Inc. 22

23. Integrate “Three-V” Data
• Emerging data sources
• High volume
• High velocity
• High variability
• New data platforms
• Hadoop
• NoSQL
• Alteryx advantage
• Easily integrate non-traditional Un-Structured
data Content
• Leverage technology and cost
advantages of next-gen
platforms
© 2012 Alteryx, Inc. 23

24. Include Third-Party Data for a Complete Picture
Harness third-party data
sources to provide data on:
• Consumers
• Age, income, education, etc.
• Locations
• i.e. Local business and
residential spending
projections
• Competitors
• Employees, revenue,
locations, etc.
• Drive times
• Traffic patterns, typical
weather, road types, etc.
© 2012 Alteryx, Inc. 24

25. Move Predictive Analytics to the Front Lines
• Take the value of Predictive
Analytics beyond the “Ivory
Tower”
• Empower front-line employees
• Clerks, customer service
agents, field service personnel
• Harness the power of the R
Analytical language
• Over 20 Prepackaged analytic
techniques
• No coding required
• Drag-and-drop
• Tightly integrated
© 2012 Alteryx, Inc. 25

26. Southern States Cooperative Continues Success With
Increased Campaign Response and Revenue
Key Requirements:
• Unify customer data
“My number one responsibility is to make sure across multiple sources
we understand our customers’ needs and wants, • Improve direct mail
campaign execution and
I use Alteryx every single day to do just that ” results
• Maximize revenue
Greg Bucko, Manager of Customer Insights. generation from
catalogue business
• Enhance ROI from
mailings
• Customer focused analytics improve response rates by 63%
• More targeted mailings improving gross margin for each campaign
• Extending insights to full range of customer channels including retail
© 2012 Alteryx, Inc. 26

27. Demonstration
Richard Snow
© 2012 Alteryx, Inc. 27

28. Conclusion
• Organizations must adapt:
• From backward-looking to
forward-looking
• Beyond traditional data sources
“My number one responsibility is to
• To deliver the value of make sure we understand our
Predictive Analytics to the
customers’ needs and wants, I use
front line
Alteryx every single day to do just
that”
Greg Bucko, Manager of Customer
Insights.
© 2012 Alteryx, Inc. 28

29. Conclusion
• Organizations must adapt:
• From backward-looking to
forward-looking
• Beyond traditional data sources
“My number one responsibility is to
• To deliver the value of make sure we understand our
Predictive Analytics to the
customers’ needs and wants, I use
front line
Alteryx every single day to do just
• Alteryx’s unique approach that”
delivers:
• Far broader accessibility for
Predictive Analytics Greg Bucko, Manager of Customer
• Much lower cost and Insights.
complexity
• Deeper insight into data (Social
Media & Big Data, Third-party
Data, Traditional sources)
© 2012 Alteryx, Inc. 29

30. Contact Info
Contact us: 1-888-836-4274
www.alteryx.com/contact-alteryx
Learn More or
Get the 30-Day Trial:
www.alteryx.com
Visit the Analytics Gallery:
gallery.alteryx.com
@alteryx
© 2012 Alteryx, Inc. 30

31. Inspire 2013
Seize the Power of Strategic Analytics
Sheraton Phoenix
Downtown Hotel
March 5-7, 2013
Learn More!
www.alteryx.com/inspire
Follow Us on Twitter!
@alteryx #Inspire13
“Overall the best single company sponsored conference I've ever attended.”
─Antoinette Bowen, Sr. Marketing Manager, AT&T Mobility
© 2012 Alteryx, Inc. 31

32. Perceptions & Questions
Analyst:
Mike Ferguson
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33. Alteryx In The Briefing Room
Mike Ferguson
Managing Director
Intelligent Business Strategies
February 2013
www.intelligentbusiness.biz
Twitter: @mikeferguson1

34. Traditional Data Warehousing and Business
Intelligence
Data Warehousing Business Intelligence
Integration / DQ P
o
BI
r
Data
Tools web
DW t
Platform
a Reports &
l analytics
Operational Data warehouse &
data data marts
What is Data Warehousing? What is Business Intelligence?
Data warehousing is the process of building Business Intelligence is actionable
an analytical system by cleaning and business insight that is produced by
integrating data from multiple data sources querying and analysing data in a data
warehouse or a data mart using BI tools
The analytical system can consist of 1 or
more databases A typical organisation has information
producers and information consumers.
34

35. What Is Self Service BI?
“ The creation of a BI environment whereby business users
can create and access BI reports, queries, and analytics
without the need for IT involvement”
§ Business users need to be able to:
• Be more self-sufficient
• Collaborate with others to share insights and make decisions
• Access personalised business insight
§ Self-service BI options
• Data discovery and visualisation tools
• Analytical workflow and visualisation tools
§ Self-service BI is NOT about self-service data warehousing
• Data governance and common data definitions are critical to
maximising the use of trusted data and facilitating common
understanding
35

36. Self-Service BI Data Discovery and Visualisation Tools
Allow Users to Quickly Produce Insight – e.g. Insurance
e.g. Calculate Net Premiums and Claims even when re-insurance data is not in
the DW
community
Data discovery
and Publish / Share
visualisation tool Consume /
insights Enhance /
Re-publish /
Data Act
In-memory data
visualisation
server with
in-memory
columnar
storage
Predictive model
Underwriting DW Ultimates Re-insurance
system data data
36

37. Self-Service Analytical Workflow Development & Visualisation
Tools Allow Users to Quickly Produce Insight – e.g. Insurance
e.g. Calculate Net Premiums and Claims even when re-insurance data is not in
the DW
community
Analytical workflow
development and Publish / Share
visualisation tool Consume /
insights Enhance /
Re-publish /
Analytical Act
Workflow execution
Workflow
Execution
Server
Predictive model
Underwriting DW Ultimates Re-insurance
system data data
37

38. Predictive Analytics Are Now Becoming Available In Self-
Service BI Tools – But Do Users Know How to Use Them
Business Analyst community
Publish / Share
Consume /
insights Enhance /
Re-publish /
Predictive models Data Discovery & Act
Visualisation OR
Analytical workflow server
The challenge is making it
easy for non-statistically
trained business analysts to
select the right algorithms
for the business questions
Predictive model they are trying to answer
Underwriting DW Ultimates Re-insurance
system data data
38

39. Impact of Self-Service BI/Analytical Tools on Data
Management
§ Business users needing data from multiple sources are using
front end tools for data integration rather than for data
analysis and visualisation
§ Potentially inconsistent data definitions and calculations for
the same data created by every user doing their own data
integration
§ Potentially a major increase in the proliferation of overlapping
data sets created by self-service BI business users not
connecting to data via a BI platform semantic layer
§ Potential for multiple versions of unmanaged data scattered
throughout the enterprise
• Potential for multiple versions of reference data
§ Potential for inconsistent data everywhere and not just
created by Excel users
39

40. Simplifying And Governing Data Access to Improve Self-
Self-Service BI
Service BI – One Approach is Via Data Virtualisation
community
Business Analyst Publish / Share
Consume /
Enhance /
Re-publish
Data Discovery &
Visualisation OR
Analytical workflow server
Data Virtualization
personal
Transaction
& office
systems
data
DW
Predictive
models
Data Management
40

41. Governing Information Distribution Is Also Important
- Information Producers and Information Consumers
Information Producers Information Consumers
Govern who can Govern what they
produce, what data can access and what
they can access and devices they can use Business
how they name data glossary
Information Distribution
Business
glossary
Business & Financial Analysts,
IT Developers, Some Managers
Executives, Managers, Frontline workers,
Govern Customers, Partners, Suppliers
distribution
41

42. New Data Sources Have Emerged Inside And Outside
The Enterprise That Business Now Wants To Analyse
Data volume
Data variety E.g. RFID tag
sensor
networks
Front Office Product/ BackOffice
service line 1
Service Finance
Customers
Product line 2
Supply Chain
Suppliers
Credit
Sales Product line 3 Procurement
Verification
Product line 4
Marketing HR
Product line n
Planning
Operations
Data volume
Data variety
weather data
Number of sources 42

43. Big Data Has Taken Us Beyond The Traditional Data
Warehouse – New Big Data Analytical Workloads
1. Complex analysis of structured data
2. Analysis of data in motion
3. Exploratory analysis of un-modeled multi-structured data
4. Graph analytics
5. Accelerating ETL and analytical processing of un-
modeled data to enrich data in a data warehouse or
analytical appliance
6. The storage and re-processing of archived data
43

44. The Changing Landscape – We Now Have Different
Platforms Optimised For Different Analytical Workloads
Big Data workloads result in multiple platforms now being needed for
analytical processing
Advanced Analytic DW & marts Advanced Analytics
(multi-structured data) (structured data)
NoSQL DB EDW DW
e.g. graph DB mart Appliance
Streaming NoSQL Hadoop Data Warehouse Analytical
data DBMS data store RDBMS RDBMS
44

45. Hadoop ‘Sandboxes’ Are Common for Data Scientist
Led Investigative Analysis of Multi-structured Data
sandbox sandbox
Un-modelled data
ETL new
MapReduce insights
Applications
(batch analysis)
Seismic Web
data logs
sensor
data
45

46. ETL Acceleration Is Also A Popular Big Data Use Case
For Bringing Additional Insights Into Data Warehouses
Hundreds of Cloud Data e.g. Deriving insight from huge
terabytes up volumes of social web content on
to petabytes sites like Twitter, Facebook. Digg,
MySpace, TripAdvisor, Linkedin….for
sentiment analytics
Operational
systems
Extract
D
Transform
DW
Cloud Data
Map/ Reduce I
analytical
applications
HDFS e.g. PIG, JAQL relevant
insight
46

47. This Requires Parsing & Extraction From Multi-Structured
Data While Integrating Data In A Big Data Environment
E-mail (semi-structured)
Load Parse Extract Transform …
Text (unstructured) 47

48. Data Deluge – Need To Accelerate And Automate Data Filtering To
Consume Data That Is Arriving Faster Than We Can Consume It
Enterprise
F
DI
A L Enterprise
systems
TT
AE
R
48

49. Data Management Tools Are Being Extended To Embrace
And Exploit MPP Hadoop Clusters AND Embed Analytics
Approaches:
• Custom code
• Data Management tools suites
• Self-service analytical workflow development tools???
Extract Data from Hadoop
Invoke Custom Analytics on Hadoop
Transform & Cleanse Data in Hadoop (MapReduce)
Data Parse & Prepare Data in Hadoop (MapReduce)
management Discover data in Hadoop
tools
Load Data into Hadoop
Trends: Expect MUCH more from data management
tool vendors including generation of MapReduce code
to clean and transform data
49

50. New Analytical Platforms Breed New Requirements
– Cross Silo Analytics for Harder Business Questions
Analyse?
RT Analytics Advanced Analytics DW & marts Advanced Analytics
(multi-structured data) (structured data)
NoSQL DB
EDW DW e.g. graph DB
mart Appliance
Streaming
data
50

51. Cross Silo Analytics Option - Multi-Platform Analytical
Workflows Need Analytics Embedded in ETL Processing
• Support parsing and extract of data from multi-structured data sources
• Help automate analysis and consumption of data
• Move the data to the best platform to do the analytics
• Support analytical processing across multiple analytical platforms
NoSQL DB
e.g. graph DB EDW
Step 1 Step 2 Step 3
Extract Load Parse Clean Transform Analyse Insights
51

52. Discussion Points
§ Competitive positioning
• Where does Alteryx fit in the analytical competitive landscape?
§ Product positioning
• Is Alteryx for Data Warehousing, Self-service BI or both?
§ Data Governance
• How does Alteryx facilitate support for data consistency and reuse
§ Analytical workloads
• What kinds of analytical workload is Alteryx providing solutions for?
• Big Data – How does Alteryx work with Big Data and NoSQL Platforms?
§ Performance
• How does Alteryx scale to handle concurrent users analysing and
consuming business insights
• How does Alteryx exploit underlying analytical platforms to get
performance with high volume multi-structured data?
52

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54. Upcoming Topics
This month: Analytics
March: Operational
Intelligence
April: Intelligence
May: Integration
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55. Thank You
for Your
Attention
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