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Big Data Needs Big Analytics
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Big Data Needs Big Analytics

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  • "Big data" is a popular term generally used to acknowledge the exponential growth, availability and use of information (structured and unstructured). A lot has been written lately on big data trend and how it will become a key basis of competition, innovation, and growth.How does SAS define or view the term “Big Data”?Big data is a relative term (and not an absolute term) - when an organization’s ability to handle, store and analyze data (from a volume, variety and velocity perspective) exceeds its current capacity (i.e. beyond your comfort zone) then it would qualify of having a “big data” problem.
  • SAS High-Performance Analytics is delivered as a pre-configured analytics appliance. It includes analytical capabilities spanning data exploration, modeling and scoring from SAS delivered on either Teradata or EMC Greenplum database appliance to solve complex problems in a highly scalable, distributed environment using in-memory analytics processing. It will let customers develop and deploy analytical models using complete data – not just a subset or aggregate – to get accurate and timely insights and take well-informed decisions. It does not limit analytic professionals to using simplified analytical methods for solving complex problems. Compresses or shrinks the time from model inception to model deployment and derive rapid insights to make well-informed decisions or before the questions become obsolete!SAS High-Performance Analytics will include a select set of procedures from following SAS products: Base SAS, SAS/STAT, SAS/ETS, and SAS Enterprise Miner. A SAS 9.3 client interface manages the submission of high performance enabled problems to the compute grid (appliance) for execution.
  • Use all the entire suite together? Adding our highly optimized advanced analytics to the process can help you generate answers to quesitons you never thought you could ask. (Note: at this point you should tie back to the “so what if you could” story you started before.Our suite allows an organization to move to “now you can”!

Big Data Needs Big Analytics Big Data Needs Big Analytics Presentation Transcript

  • Big Data Needs BigAnalyticsDeepak RamanathanInformation Management and AnalyticsAsia Pacific (North) Copyright © 2012, SAS Institute Inc. All rights reserved.
  • OURPERSPECTIVE Big Data is RELATIVE not ABSOLUTE Big Data (Noun) When volume, velocity and variety of data exceeds an organization’s storage or compute capacity for accurate and timely decision-making Copyright © 2012, SAS Institute Inc. All rights reserved.
  • THE FUTURE OF ANALYTICS IS HERE Copyright © 2012, SAS Institute Inc. All rights reserved. View slide
  • Copyright © 2012, SAS Institute Inc. All rights reserved. View slide
  • THE ANALYTICS LIFECYCLE IDENTIFY / FORMULATEBUSINESS EVALUATE / PROBLEM BUSINESSMANAGER MONITOR DATA ANALYST RESULTS PREPARATIONDomain Expert Data ExplorationMakes Decisions Data VisualizationEvaluates Processes and ROI Report Creation DEPLOY MODEL DATA EXPLORATIONIT SYSTEMS / DATA MINER /MANAGEMENT VALIDATE STATISTICIAN MODEL TRANSFORMModel Validation & SELECT Exploratory AnalysisModel Deployment BUILD Descriptive SegmentationModel Monitoring MODEL Predictive ModelingData Preparation Copyright © 2012, SAS Institute Inc. All rights reserved.
  • CURRENT BIG DATA MEETSTRENDS IN BIG ANALYTICSANALYTICS Copyright © 2012, SAS Institute Inc. All rights reserved.
  • • A flexible architecture that supports many data types and usage patternsTechnology • Upstream use of analytics to optimize data relevanceChecklist for • Real-time visualization and advanced Big Data analytics to accelerate understanding Analytics and action • Collaborative approaches to align Business and IT executives Copyright © 2012, SAS Institute Inc. All rights reserved.
  • • Leverage in-memory architecture via a dedicated software and hardware appliance Big Data • Drive high-performance capabilities Meets across the analytical lifecycleBig Analytics • Achieve insights at breakthrough speed before questions become obsolete • Offer a consistent interface for current SAS analytic users Copyright © 2012, SAS Institute Inc. All rights reserved.
  • SAS HIGH-PEFORMANCE ANALYTICS Copyright © 2012, SAS Institute Inc. All rights reserved.
  • HIGH-PERFORMANCE SAS® GRID COMPUTING ANALYTICS Copyright © 2012, SAS Institute Inc. All rights reserved.
  • HIGH-PERFORMANCE SAS® IN-DATABASE ANALYTICS Copyright © 2012, SAS Institute Inc. All rights reserved.
  • IN-MEMORY ACCELERATES UNDERSTANDING AND ACTIONARCHITECTURE Hadoop Teradata Aster Copyright © 2012, SAS Institute Inc. All rights reserved.
  • SAS® VISUAL BUSINESS VISUALIZATION: SAS® VISUAL ANALYTICS ANALYTICS SAS LASR Analytic Server Copyright © 2012, SAS Institute Inc. All rights reserved.
  • SAS HIGH-PERFORMANCE ANALYTICS – HOW IT WORKS DB Appliance Node SAS High-Performance Analytics Appliance • Processing request parsed into multiple, proc hplogistic data=GPLib.MyTable; parallel requests distributed to the nodes of class A B C D ; model y = a b c b*d x1-x100; the environment output out=GPlib.logout pred=p; • Each node operates ‘independently’ run; • Results consolidated and returned to client Copyright © 2012, SAS Institute Inc. All rights reserved.
  • OUR OUR HIGH-PERFORMANCE ANALYTICS PERSPECTIVE DRIVES HIGH IMPACT RESULTS PERSPECTIVERetention Campaigns 15% improvement Increase coupon redemption rate from(SAS® Grid Manager) 10% to 25% (SAS® Scoring Accelerator (In-DB))Regression analysis from167 hours (1 week) to 84 seconds! Recalculate entire risk portfolio from 18(SAS® High-Performance Analytics) hours to 12 minutes (SAS® High-Performance Risk) 270 million price points analyzed in 2 hrs. (from 30 hrs.) (SAS® High-Performance Markdown Optimization) Copyright © 2012, SAS Institute Inc. All rights reserved.
  • 16Copyright © 2012, SAS Institute Inc. All rights reserved.
  • Deepak.Ramanathan@sas.com Copyright © 2012, SAS Institute Inc. All rights reserved.