The New Normal: Predictive Power on the Front Lines
 

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The Briefing Room with Mike Ferguson and Alteryx ...

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.

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The New Normal: Predictive Power on the Front Lines Presentation Transcript

  • 1. The Briefing Room
  • 2. Welcome Host: Eric Kavanagh eric.kavanagh@bloorgroup.comTwitter Tag: #briefr The Briefing Room
  • 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!Twitter Tag: #briefr The Briefing Room
  • 4. FEBRUARY: Analytics March: OPERATIONAL INTELLIGENCE April: INTELLIGENCE May: INTEGRATIONTwitter Tag: #briefr The Briefing Room
  • 5. Analytics Hindsight and Insight are fairly common © Cristian Farcas | Dreamstime.com PREDICTIVE CAN BE ELUSIVETwitter Tag: #briefr The Briefing Room
  • 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.Twitter Tag: #briefr The Briefing Room
  • 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 processingTwitter Tag: #briefr The Briefing Room
  • 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.Twitter Tag: #briefr The Briefing Room
  • 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 DiscoveryIT / 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 Ive ever attended.” ─Antoinette Bowen, Sr. Marketing Manager, AT&T Mobility© 2012 Alteryx, Inc. 31
  • 32. Perceptions & Questions Analyst: Mike FergusonTwitter Tag: #briefr The Briefing Room
  • 33. Alteryx In The Briefing RoomMike FergusonManaging DirectorIntelligent Business StrategiesFebruary 2013www.intelligentbusiness.bizTwitter: @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 analyticsOperational Data warehouse & data data martsWhat is Data Warehousing? What is Business Intelligence?Data warehousing is the process of building Business Intelligence is actionablean analytical system by cleaning and business insight that is produced byintegrating data from multiple data sources querying and analysing data in a data warehouse or a data mart using BI toolsThe analytical system can consist of 1 ormore 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 datavisualisation server with in-memory columnar storage Predictive model Underwriting DW Ultimates Re-insurance system data data 36
  • 37. Self-Service Analytical Workflow Development & VisualisationTools Allow Users to Quickly Produce Insight – e.g. Insurancee.g. Calculate Net Premiums and Claims even when re-insurance data is not inthe 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 DataManagement §  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 personalTransaction & 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 DistributionBusinessglossaryBusiness & 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 OperationsData volumeData variety weather dataNumber of sources 42
  • 43. Big Data Has Taken Us Beyond The Traditional DataWarehouse – 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 DifferentPlatforms 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 ScientistLed 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 WarehousesHundreds of Cloud Data e.g. Deriving insight from hugeterabytes up volumes of social web content onto 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-StructuredData 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 ToConsume 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 EmbraceAnd 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 AnalyticalWorkflows 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
  • 53. Twitter Tag: #briefr The Briefing Room
  • 54. Upcoming Topics This month: Analytics March: Operational Intelligence April: Intelligence May: Integration www.insideanalysis.comTwitter Tag: #briefr The Briefing Room
  • 55. Thank You for Your AttentionCertain images and/or photos on this page are the copyrighted property of 123RF Limited, their Contributors or Licensed Partners and are being used withpermission under license. These images and/or photos may not be copied or downloaded without permission from 123RF Limited.Twitter Tag: #briefr The Briefing Room