Mass tlc presentation menninger

Data ScienceA Practitioner’s PerspectiveMass Technology Leadership Council Panel DiscussionDavid Menninger, Formerly VP & Research Director, Ventana ResearchDavid.Menninger@emc.com ©2012, Ventana Research

David MenningerFormer Vice President – Ventana ResearchNow head of business development and strategy for EMC Greenplum.Until last week, covered analytics, business intelligence and informationmanagement for Ventana Research. Over two decades of experiencedeveloping and bringing to market some of the leading edgetechnologies for helping organizations analyze data to support a rangeof action-taking and decision-making processes.Prior to joining Ventana Research, served as VP of Marketing andProduct Management at Vertica Systems, Oracle, Applix, InforSenseand IRI Software. Helped create over three quarter billion dollars ofshareholder value while serving in these roles.Email: david.menninger@emc.com 2 ©2011, Ventana Research, Inc.

Some Recent Relevant Research

Volume and Velocity of Data Are Most Important In Evaluating Big Data Technology less than 1 TB 10% 1-10 TB 29% 11-100 TB 31% 101 TB-1 PB 13% more than 1 PB 11% Dont know 7% 0% 10% 20% 30% 40%less than 10 GB per day 26% 11-100 GB per day 33% 101 GB-1 TB per day 20% 1-10 TB per day 4% More than 10 TB per… 6% Dont know 12% 0% 10% 20% 30% 40% Source: Ventana Research The Challenge of Big Data Benchmark Research 4 ©2012, Ventana Research

Hadoop Is Being Adopted or Considered by 54% of EnterprisesProduction 22% Planned 15%Evaluating 17% Source: Ventana Research Hadoop Information Management Analytics Research 5 ©2011, Ventana Research, Inc.

…but the Vast Majority Use a Variety of Big Data Technologies An RDBMS (for example, IBM DB2, Microsoft SQLServer, MySQL, Oracle) on 89% 2% 3% 2% 3% standard hardware Flat files 70% 7%1% 4% 18% A DW appliance (for example , Netezza, Exadata, EMC 34% 11% 3% 21% 31% Greenplum, Teradata) In-memory databases 33% 13% 4% 17% 33% Hadoop 22% 12% 3% 17% 45% Other 26% 4%4% 10% 57% A specialized DBMS (for example, AsterData, Infobright, Kognitio, Parac 15% 9% 5% 19% 51% cel, SybaseIQ, Vertica) Currently in production Plan to use within 12 months Plan to use in 12-24 months Still evaluating No plans to use Source: Ventana Research The Challenge of Big Data Benchmark Research 6 ©2012, Ventana Research

What Types of Applications? What types of large-scale data applications is your organization running? 60% Query and reporting 89%Consolidation of multiple 63% Hadoop is most oftendata sources for analysis 71% used for advanced Custom/production 65% analyses and is more application 68% likely to be used to 56% analyze unstructured Data preparation 60% data and for data 69% sandboxing than other Advanced analyses 47% technologies. It is less Analysis or indexing 46% likely to be used for of unstructured data 32% query and reporting. Hadoop Data sandbox/ 44% Data experimentation 32% Non-Hadoop Source: Ventana Research Hadoop Information Management Analytics Research 7 ©2011, Ventana Research, Inc.

Predictive Analytics Still Emerging Despite its potential, predictive analytics remain a specialist tool, ranking 10th among BI capabilities with only 13% using them Spreadsheets 60% Business Intelligence 49% Analytic Databases 41% Custom-built systems 34% Data warehouse 28% Planning and forecasting 26% Application server 20% LOB analytics 18% RDB 14% … yet 80% ranked predictive analytics Predictive Analytics 13% capabilities as important or very important Source: Ventana Research Business Analytics Benchmark Research 8 ©2012, Ventana Research

Forecasting and Marketing are the MostCommon Uses of Predictive Analytics Forecasting… 72% 24% Marketing analyses… 70% 22% Customer service or support… 45% 34%Product recommendations or offers 43% 22% Fraud detection 34% 31%Intelligence or surveillance analysis 28% 28% Social network analysis 27% 38% Logistics analysis 26% 27% Predicting product development … 18% 34%Predicting prices in the supply chain 17% 36% Scientific or clinical research 17% 27% Healthcare decisions 16% 29% Current Predicting mechanical failures 9% 33% Future Other 17% 24% Source: Ventana Research Predictive Analytics Benchmark Research 9 ©2012, Ventana Research

Organizations Employ a Variety of PredictiveAnalytics Algorithms Classification and regression trees /… 69% 25% 6% Linear Regression 66% 33% Logistic regression or other discrete choice… 61% 29% 10% Association rules 49% 37% 14% K-nearest neighbors 36% 42% 21% Neural networks 30% 36% 34% Box Jenkins, Autoregressive… 30% 35% 35%Exponential smoothing / double exponential… 22% 43% 34% Naïve Bayes 21% 43% 36%Support vector machines 20% 23% 57% Survival analysis 15% 41% 44%Monte Carlo Simulations 13% 47% 40% Frequently Occasionally Not at allClassification and regression trees / decision trees and LinearRegression are the most popular predictive analytics techniques used. Source: Ventana Research Predictive Analytics Benchmark Research 10 ©2012, Ventana Research

Who Designs and Deploys Predictive Analytics? Data Scientist / Bus. Intelligence / Line-of- Data Mining Data Warehouse Business Resources Team Analysts 32% 31% 19% … but who should be performing these tasks? Source: Ventana Research Predictive Analytics Benchmark Research 11 Q18 ©2012, Ventana Research

Who Does the Best Job? Satisfaction vs. Project Team Specialized data scientist, statistical 70% or data mining resources Line of business analysts 65% Business intelligence and data 59% warehouse team 50% 55% 60% 65% 70% 75% Overall Average Source: Ventana Research Predictive Analytics Benchmark Research 12 ©2012, Ventana Research

Real-Time Scoring of New RecordsNot at all Regularly 30% 30% More than half the organizations perform real-time scoring infrequently or not at all. Occasionally Infrequently 18% 22% Source: Ventana Research Predictive Analytics Benchmark Research 13 Q26 ©2012, Ventana Research

Organizations Need More Timely Resultsfrom Predictive Analytics Satisfaction vs. Use of Real-time Scoring Regularly 88% Occasionally 73% Infrequently 47% or Not at all 0% 20% 40% 60% 80% 100% Overall Average Source: Ventana Research Predictive Analytics Benchmark Research 14 ©2012, Ventana Research

Frequency of Updating Predictive Models Dont know Constantly 16% 12% Hourly 2%Most organizations Dailydon’t update their 6% Less oftenanalytic models thanfrequently enough. quarterly Weekly 17% 11%Nearly four in 10 updatetheir models quarterly orless frequently. Monthly Quarterly 14% 22% Source: Ventana Research Predictive Analytics Benchmark Research 15 Q27 ©2012, Ventana Research

Organizations that Update Models MoreFrequently Have Higher Satisfaction Satisfaction vs. Model Updates At Least Daily 81% At least Monthly 74% Less Frequently 48% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% Overall Average Source: Ventana Research Predictive Analytics Benchmark Research 16 ©2011, Ventana Research

Most Organizations Are Not ProvidingAdequate Support and Training Training in Predictive analytics 44% 32% 24% concepts and techniques Product training 42% 33% 26% Training in the application of predictive analytics to business 39% 38% 23% problems Specialized consulting resources 31% 39% 31% (internal or external) Help desk resources 24% 34% 42% Adequately Only somewhat adequately Inadequately Source: Ventana Research Predictive Analytics Benchmark Research 17 ©2012, Ventana Research

What Types of Training and Support AreMost Effective? Satisfaction vs. Training and Support Training in Predictive analytics 89% concepts and techniques Help desk resources 89% Training in the application of predictive 86% analytics to business problems Product training 79% Specialized consulting resources 77% (internal or external) 60% 65% 70% 75% 80% 85% 90% 95% Overall Average Source: Ventana Research Predictive Analytics Benchmark Research 18 ©2012, Ventana Research

Data ScienceA Practitioner’s PerspectiveMass Technology Leadership Council Panel DiscussionDavid Menninger, Formerly VP & Research Director, Ventana ResearchDavid.Menninger@emc.com ©2012, Ventana Research

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