Inspire 2013 - Alteryx and the Teradata Unified Data Architecture


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With so much to be gained from slicing and dicing the vast amount of data being collected, why aren’t more companies further along in their efforts to exploit all of their available data? In a word: complexity. The volume, velocity and variety of data coming from transactional systems, web, text, social media, and machine generated data make it too complex to be analyzed in an ad-hoc manner. And with the variety of environments like data warehouses, Hadoop and other analytical platforms available, what tools should be brought to bear to take advantage of it all? The Teradata Unified Data Architecture™ helps make sense of these massive, unruly data sets so organizations spend time analyzing information rather than gathering and managing data, letting users leverage these powerful resources transparently to unlock new and valuable business insights. The net result: higher productivity, lower costs, and a broadening of opportunities. See how Alteryx provides a visual workflow to blend and move data plus create and publish analytics within and across the different environments supported in the Teradata Unified Data Architecture™, and learn how you can get started immediately using Alteryx with Teradata.

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Inspire 2013 - Alteryx and the Teradata Unified Data Architecture

  1. 1. Getting your data to “play nice with others” usingAlteryx and the Teradata Unified Data ArchitectureBruce Johnson, TeradataJim Schattin, AlteryxTechnology trackMarch 7, 20131:15 – 2:00 pm
  2. 2. Which one are you?Person with the hose?Person trying to get the hose?Dog waiting for the bath? ORThe DataThe AnalystThe Problem to be solved
  3. 3. Data (Big & Small) Provides Sources For Insight
  4. 4. Why? • Organizations compete on analytics • Narrowly focused analytics against narrow data sets produce narrow insights • Enriching and putting all data to work makes for smarter decisions and data- driven business models • Opportunities for organizations to benefit from analytics across more of their data is greater than ever before… So are the challenges to figure out how to best enable this capability
  5. 5. Challenge = Opportunity• Organizations are not making use of all the data they possess NOW!• Undiscovered nuggets of information about customers, products and performance hidden in hard to reach places – e.g., ERP, legacy systems, web logs, social mediaData should be easily accessible and usablePlatforms and tools should be flexible and scalableBusiness should be able to “ask any question at any time”Need to understand the role of new technologies (e.g., Hadoop, MapReduce)Need to understand the value in Big Data, how it can improve what we know today!
  6. 6. Take advantage of it! • Teradata’s response to this opportunity is the Unified Data Architecture™ (UDA) to deploy available technologies to unleash the value of data • Creates a strong analytic foundation by embracing existing, new and emerging technologies in a cohesive manner • Manage all the data with workload specific engines and a consistent set of tools > Apply the right technology to the right analytical opportunities > Isolate intelligent signals in a world of noise > Turn invisible opportunities into actionable decisions
  7. 7. Teradata Unified Data Architecture™ Three capabilities working in conjunction with one another… • Teradata Data Warehousing: Integrated and shared data environment serves as the foundation for any analytic environment, provides a single source of centralized data for reuse, delivers strategic and operational analytics to the extended organization • Aster Data Discovery: Pre-packaged SQL-MapReduce capabilities for data-driven discovery, helping unlock insights from big data, performed with a technology with rapid exploration abilities via a variety of analytic techniques and accessible by mainstream business analysts • Hadoop Data Staging: Preparation for analytics in a low-cost technology proven to be very effective for loading, storing and refining data  Data, answer-sets, and insights passed seamlessly among the architecture capabilities  Synchronized components with transparent management, access and analysis of all of the data
  8. 8. Teradata Unified Data Architecture™ ADVANCED ANALYTICS Enable any analysis against any type or volume of data at any time…Discovery and Data warehouse forexploration platform insight deploymentthat enables agility into a reliablewith limited productionconstraints environment…
  9. 9. So you’ve got big and little data, a discoveryplatform and a flexible architecture…. Now what?
  10. 10. Analytics Ecosystem Strategy That Fits High Opportunity Strategic Goals and EDW Insight Initiatives Guided Analytics COMPLEXITY Analytics Lab KPIs Feedback Executives Business Users Analysts Data Scientists Guided Analysis Advanced Analytics Low High NUMBER OF USERS Low
  11. 11. Develop Analytics to Solve Business Problems… Augment traditional analytic approaches with new approaches Statistics Forecasting Graph Analysis Geospatial Text Analysis
  12. 12. … And Make Your New Insights Operational
  13. 13. Marketing Service ProviderDigital Marketing Attribution • Segmentation: Custom Analytic Tools Data by Custom SQL-MR algorithms to match Client and create centralized identifiers • Sessionize by clientMedia Data • nPath identifies segment path analysis Teradata Aster (behavior after ads)(Aggregated) • Benefits: level data Archival Cookie- Raw Web Logs - Marketing analysts more productive with Aster Ad Server Hadoop (on AWS) - Lower cost - storage and Logs (Storage, aggregations, batch refining done on cleansing)
  14. 14. Big Box Grocer – Initial Use Case Affinity Analysis What is the customer buying in key categories? Does this affect other categories and how?
  15. 15. Analytics Architecture Reports Mobile Social Analyst Data Scientist Layer Guided Analytics Data Application Layer Prototyping Layer Layer Database Layer Teradata Aster Lab/Test Teradata EDW Hadoop Prod Store/Cleanse • Reporting • Prototyping • Aggregations / ETL /API • 2 – 3yrs data • Fail Fast • Data Hub – Active Archive • Cleansing Multi-Structured • Productionization • Analytics Lab Documents Transactional Data IT/OT Images Audio Social/Text/Log Video Enterprise Systems
  16. 16. Affinity Analysis Current Method UDA MethodTool SQL SQL-MapReduce (Collaborative Filter operator)Dataset Time Span 13 weeks 8 years (32X time span)Affinity Calculation One category against All Categories vs. all others othersCalculation Time 4 hours 48 Minutes 2.4 Minutes - same calculations against same data
  17. 17. Affinity Analysis:Shelf Stable Juice Seasonal Affinity with Other Categories 0.025 0.02 Alcohol Cereal 0.015 Frozen - Ice Cream Collab. Laundry Detergent Score 0.01 Other Cheese Paper Towels Pizza 0.005 Shredded Cheese Sliced Cheese 0 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 String Cheese 2004 2005 2006 2007 2008 2009 2010 2011 Year/Month
  18. 18. Retail Use CasesAffinity Analysis: Analyzing Affinity of items over a long duration (6-10yrs) will provide key insights into running better promotions, planogram and price planning using affinity of items.  Affinity Analysis on 8 Years of Data for All Categories against All Other CategoriesConsumer Migration: Analyzing declines in consumer segments over large timeframes.  Determine the items missing from declining baskets and why  How much time best (Platinum, Gold) consumer is spending in different segments before becoming unengaged?Pricing Affinity: Analyzing item price movement and its impact on basket size and affinity of items over a long duration (6 years).  Determine individual and multiple item their price movement impact on total basketCompetitor Impact: Analysis of various competitor impacts over time  Understand impact of competitor store opening on basket size and consumer loyalty (trips per month)  Determine if the effects are temporary or permanentSocial Media : Integrating consumer online data (Social Media - Facebook) with existing transaction data to understand loyalty  Understand the number of fans by demographic  Understand social media behaviors of best consumers (Platinum and Gold)  Differences in behavior of consumers in categories who are Facebook fans versus non-Facebook fans
  19. 19. Financial Services Use Cases • Understanding Customer Service Interactions key to product ‘fit’ • Churn, Adoption, Attrition, Path to product cross sell • Combine Check Image, Voice, Social, Web, Transaction data • Web Analytics (90% of data) • Combine and Sessionize HTTP raw, HTTPS and XML Application logs • Find Golden path to Application Submit • Execution requires discovery across multiple channels to see patterns and paths in data to use as new variables for propensity scoring • 4 steps 1) Customerization (ID the custom in data) 2) Sessionization 3) Sequencing Analytics (Discover behavior across a period of time and all channels) 4) Productionize in Models, Events and Campaigns
  20. 20. Financial Services - Churn Prevention Hadoop captures, SOCIAL CLICKSTREAM stores and FEEDS DATA Aster does path and sentiment analysis transforms social, with multi-structured images and call data records Multi-Structured Data Call Center Voice Call Data Records Sentiment Aster Discovery Hadoop Scores Platform Email/Survey Check Data Analysis + Data Marketing Dimensional Analytic Results Check Images Automation Data Capture, Retain and Traditional Data Flow Refine Layer (Customer Data Sources Retention Campaign) ETL Tools Teradata Integrated DWBRANCH, ATM DATA
  21. 21. Events Preceding Account Closure
  22. 22. Events Preceding Account ClosureSELECT * FROM npath ( ON ( SELECT … WHERE u.event_description IN ( SELECT aper.event FROM attrition_paths_event_rank aper ORDER BY aper.count DESC LIMIT 10) Interactive Analytics Reducing the “Noise” ) … PATTERN ((OTHER|EVENT){1,20}$) SYMBOLS (…) RESULT (…) )) n; to find the “Signal”
  23. 23. Events Preceding Account ClosureSELECT *FROM nPath ( ON (…) PARTITION BY sba_id ORDER BY datestamp MODE (NONOVERLAPPING) Closed Accounts Fee Reversal Seems PATTERN ((OTHER_EVENT|FEE_EVENT)+) SYMBOLS ( event LIKE %REVERSE FEE% AS FEE_EVENT, event NOT LIKE %REVERSE FEE% AS OTHER_EVENT) RESULT (…)) n; to Be a “Signal”
  24. 24. Paths to Attrition (Version 2) Multiple Fee Reversal and Viewing Product/Rates and Offers happens in the last mile for Account Closure
  25. 25. Telco Use Case with Teradata UDA and AlteryxProblemA Global Communication Service Provider is interested in preventing customer churn by identifying at-risk customers and thenproviding special offers that reduce the likelihood of churn in a profitable way. This requires use of predictive analytics. Perform predictive analytics to identify customers most likely to churnJoint SolutionAlteryx loads call records of customers that churned over the last 5 years into Aster to identify a golden pathAlteryx moves output into a Teradata Data Lab to combine with customer data from Teradata DW to drive in-database analyticsAlteryx performs detailed geospatial engineering/network analysis and then provides to Business Analysts for reviewResults • Ability to identify key customers that are likely churn candidates • Visualization of problem spots on the network (cell sites, network elements, ...) that are driving churn • Understanding of other key reasons for churn – performance, competitive offers, ... • See what offers have avoided churn by similar customers in the past • Able to identify which offers will work and to evaluate a least cost offer to prevent churn • The ability to make offers to keep customers from churning • Deeper understanding of customer behavior
  26. 26. Retail Use Case with Teradata UDA and AlteryxProblemImproving customer insight by identifying customers most likely to shop at a competitor, to drive better marketing campaigns,to bring social media analytics into decision making, and to get a 360 degree view of product demand requires use of a stronganalytics solution that is accessible to business users. Enable business users to access a single analytics platform for a variety of requirementsJoint SolutionAlteryx loads customer purchase history into Aster to identify golden paths for purchase and churnAlteryx moves output into a Teradata Data Lab to combine with customer data from Teradata DW to drive in-database analyticsResults enhanced by applying Alteryx Drive Time analysis to understand which customers drive by competitors to shop themResults • Ability to identify key customers that are likely churn candidates • Gain customer insight and identify key attributes for purchase • Combine ratings, reviews, mobile, and interaction data, and apply predictive model clustering, to determine products with the most and least demand • Integrate social media content to enable business units to understand market perception and to analyze sentiment values in decision making • Integrate drive time geospatial with data such as weather, sensor, economic, competitive, traffic, logistics and other data sources to improve labor costs and maximize customer service
  27. 27. TERADATA UNIFIED DATA ARCHITECTURE Data Scientists Quants Customers / Partners Front-Line Workers Engineers Business Analysts Executives Operational Systems LANGUAGES MATH & STATS DATA MINING BUSINESS INTELLIGENCE APPLICATIONS Productionize insights Affinity analysis Dashboards and reporting Consumer migration Vendor managed inventory Price elasticity Assortment optimization Competitor Incursion Aggregations / ETL /API Analytical ScoringX-promotion affinity analysis Data Hub –Archive DISCOVERY Cleansing Multi-Structured Event Triggers Influencer analysis INTEGRATED PLATFORM Consumerize DATA WAREHOUSE Customer Behavior Analysis Sessionize Voice to text; ID keyword Spend Analysis Golden Path Determination Image Performance Analysis Fraud Sentiment Analysis X-Platform Aggregation Customer Segmentation Channel Hopping Risk Analysis Attrition Paths Customer Profitability Fraudulent Paths Portfolio Analysis CAPTURE | STORE | REFINE E-MAIL CUSTOMER ON-LINE STORE DATA IRI / NIELSEN PRICING COMPETITOR SEGMENTS
  28. 28. Demo
  29. 29. Retail Analytics • Leverage Alteryx to direct analytics across Teradata and Teradata Aster to gain consumer insight for your retail brand. • Append 3rd party content to your customer records and load into Teradata • Execute statistical algorithms in database via the Teradata R package • Run nPath MapReduce function in Teradata Aster to reveal how online customers navigate your web site to make a purchase • Perform product correlation calculations in Teradata to understand purchasing behaviors at brick and mortar locations >>> Merchandise e-Commerce and Physical locations for optimal results
  30. 30. Ready to Play? Let’s get started!
  31. 31. Thank You!