Anwendungsbereiche für Big Data & Analytics

Size: px
Start display at page:

Download "Anwendungsbereiche für Big Data & Analytics"

Transcription

1 Anwendungsbereiche für Big Data & Analytics Dr. Thomas Keil, Program Marketing Manager Business Analytics, SAS Institute GmbH

2 SAS Institute SAS is the first company to call when you need to solve complex business problems. Dr. James H. Goodnight, CEO und Gründer von SAS Gegründet 1976 in Cary, North Carolina Mitarbeiter weltweit in 400 SAS Offices in 52 Ländern Seit 1982, mit 470 Mitarbeitern in 6 deutschen Niederlassungen Etwa Unternehmen und Organisationen aus allen Branchen setzen auf SAS Umsatz (D): 114 Millionen Umsatz (Int.): 2,43 Mrd. US$ Investition in R&D > 20 % 2

3 Gartner Hype Cyle Big Data Hype oder Trend? 3

4 Big Data Worum geht es? Source: An IDC White Paper - sponsored by EMC. As the Economy Contracts, the Digital Universe Expands. May

5 Wie entsteht Big Data? Mobile transactions Sensor Data Pervasive Computing Weiter fortschreitende Digitalisierung Nutzer generieren Inhalte, teils automatisiert Maschinen generieren Daten 5

6 Big Data Eine Herausforderung Volumen Geschwindigkeit BIG DATA Heterogenität Es kommt darauf an, den Wert der Daten zu erkennen und zu nutzen! 6

7 SAS liefert Lösungen Erfassen Big Data Mehr Variablen Datenmanagement Analysieren Komplexe Berechnungen Varianten Explorative Analyse Umsetzen Schnelle Ergebnisse Monitoring Integration in Geschäftsprozesse Unlösbare Probleme / Neue Möglichkeiten / Unbekannte Risiken 7 Copyright 2011, SAS Institute Inc. All rights reserved.

8 8

9 High-Performance Computing SAS Analytics SAS High-Performance Computing SAS Grid Computing SAS In- Database SAS In- Memory Analytics Architecture Flexibility Deployment Flexibility Desktop, SMP, MPP, Grid On Premise, Cloud, Appliance 9

10 McKinsey-Studie zeigt Big Value Quelle: McKinsey Global Institute, May 2011: Big data: The next frontier for innovation, competition and productivity 10

11 Beispiel: Handel Marketingoptimierung Visa Classic / Direct Mail Visa Classic / Call Center Visa Classic / Branch Kunden Visa Gold / Direct Mail Visa Gold / Call Center Visa Gold / Branch Home Equity Loan / Direct Mail Home Equity Loan / Call Center Home Equity Loan / Branch Millionen von Kunden Hunderte Angebote Bisher SAS High Performance Analytics Beschleunigung 5 h 29 min 2 min 164x 11

12 Beispiel: Bank Risikoberechnung Finanzinstrumente Marktparameter Zwei Zeitreihen 8,8 Mrd. Value at Risk- Berechnungen Bisher SAS High Performance Analytics Beschleunigung 18 h < 3 min 360x 12

13 Beispiel: Handel Preisfindung 73 Millionen Artikel Mehr als 800 Geschäfte Jede Woche hunderte Millionen von Preisentscheidungen Zwei bis drei Jahre historische Informationen ~ 3 Terabyte Daten 80% Hardware-Einsparungen! Bisher SAS High Performance Analytics Beschleunigung 27 h 15 min 1 h 15 min 22x 13

14 Beispiel: Gesundheitswesen Risikostrukturausgleich Quelle: GKV-Spitzenverband (Vortrag Mathias Kleinschmidt, , Big-Data-Workshop, Fraunhofer-Inst, St. Augustin) 14

15 Beispiel: Geodaten Kundensegmentierung Quelle: infas geodaten (Vortrag Ludger Hertig, , Big-Data-Workshop, Fraunhofer-Insitut, St. Augustin) 15

16 Anwendungsszenarien für jede Branche 16

17 Zusammengefasst... Immer mehr Daten mit SAS Kernkompetenz immer besseren Analysen in Business Analytics immer kürzerer Zeit für High Performance Computing Entscheidungsträger aufbereiten und Business Analytics Framework überall verfügbar machen. Mobile BI Strategie: SAS High Performance Analytics 17

18 18

19 Vielen Dank für Ihre Aufmerksamkeit. Dr. Thomas Keil Program Marketing Manager Business Analytics SAS Institute GmbH Tel Mobil: Im Gespräch bleiben XING: Business Analytics mit SAS

20 Backup: Weitere Beispiele

21 SAS Grid Customer Churn and Cross-sell/Up-sell Telecommunications Challenges Grow market share Improve marketing efficacy Solution Grid-based deployment Better analytic processes and controls Benefits 15% improvement in marketing campaigns Reduced processing from 11 hrs. to 10 seconds We have jobs that use to take 11 hours to run. In the new analytics environment, they are running in around 10 seconds. Sandra Hogan, Director of Customer Intelligence 21

22 SAS In-Database Customer Buying Behaviors Retail Marketing Services Challenges Quickly profile customer behavior in real-time Better targeting of customer offers Analyze and gain insight on 2.5 petabytes of data Solution In-database processing Managed analytical environment Benefits Increase coupon redemption rate from 10 % to 25% Reduced model scoring from 4.5 hours to 60 seconds Catalina is now able to create more flexible, robust models that take advantage of complex analytic data preparation steps and methods without the need for manual recoding of our custom in-database scoring routines. Eric Williams, Chief Technology Officer for Catalina Marketing 22

23 SAS In-Database Customer Success Story Propensity to Pay Telecom Company: Providing Performance gains by Refactoring Past Approach Daily process begins with flat file creation at 6:30am SLA delivered at ~9:30am. File transferred to SQL Server, limited to ~350K customer records based on specific criteria. In-Database Approach Daily process begins at 4:00am with EDW load. All operational data loaded directly to EDW. No flat file or intermediate processing is needed. Business Issue: Improve collection of unpaid accounts Technical Limitation: 3 hour SLA Solution: SAS In-Database Scoring 300 step process to support data mining life cycle. 10 step process Scoring and customer selection done in-database against ALL customer rows Result: Business Process change leading to BETTER targeting and $1M to $3M extra collections a month. 30 MINUTES TO SCORE ~350k customers Runs in ~ 3 HOURS 4 MINUTES TO SCORE ~40M customers Runs in 12 MINUTES 23

24 Backup: Technische Umsetzung

25 Key Business Challenges Underutilized resources Support incremental growth Unnecessary data movement Guarantee uptime & continuity Increasing costs Growth in data and user volumes; complexity Slow time to results Slow response time Limited analysis due to lack of resources Low productivity SAS High Performance Computing SAS Grid Computing SAS In-Database SAS In-Memory Analytics Event Stream Processing 25

26 SAS High Performance Computing SAS Grid Computing SAS In- Database SAS In- Memory Analytics Event Stream Processing WHY IT Value Provide a centrally managed SAS environment to the enterprise Scale-out server infrastructure Business Reduce time to results Guaranteed uptime and continuity of services Increased user flexibility WHAT Functionality High availability Workload management Distributed enterprise job scheduling Scalability for a multi-user environment HOW Technology SAS Grid Manager Software to manage a SAS distributed (grid) environment SAS jobs/parts of a job (step boundaries) are split up to run in parallel across multiple servers in a managed grid environment. Shared physical storage is used within the environment Partner: Platform Computing 26

27 SAS High Performance Computing SAS Grid Computing SAS In-Database SAS In-Memory Analytics Event Stream Processing IT Business WHY WHAT Value Integrate with the current IT infrastructure, leveraging the current data warehouse investment Functionality Enable Data Governance Reduce Data Redundancy Reduce Information Latency Increase Hardware Utilization Streamlined Analytic Processes for Better Decisions Reduced time to results Minimize data preparation Accelerate data discovery Increase no. of models generated Develop complex models to improve outcomes HOW Technology SAS Scoring Accelerator, SAS Analytics Accelerator, SAS/ACCESS Ability to provide select Data Preparation, Data Exploration, Predictive Analytics and Scoring capabilities inside the data warehouse. The analytical computations within a SAS step boundary run in parallel leveraging the MPP (Massive Parallel Processing) and partitioned shared nothing capabilities of the database. Partners: Aster Data, EMC (GreenPlum), IBM DB2, Netezza, Oracle, and Teradata 27

28 SAS Scoring Accelerator and SAS Model Manager Model Management and Deployment Analytic Models SAS Model Manager SAS Scoring Accelerator Model Monitoring Database SAS Functions SAS Formats Enterprise Miner Models Adds Business Value Consistent model development and validation Understanding of model strategy and lifetime value Improves Production Process Efficient deployment of models in a timely manner Enforces Governance Process Audit trails for regulatory compliance Monitor the performance of models Provide qualitative overlay on test results Create reports detailing model performance Acceptable Recalibration Redevelopment Reduced data movement and latency Eliminate model score code rewrite and model revalidation efforts Achieve higher modelscoring performance and faster time to results Consolidate data to improve regulatory compliance Better manage, provision and govern data 28

29 SAS High Performance Computing SAS Grid Computing SAS In-Database SAS In-Memory Analytics Event Stream Processing IT Business WHY Value Provide a dedicated high performance, scalable, managed appliance for high-end analytics Solve complex and time-critical business problems involving big data in near real-time WHAT HOW Functionality Dedicated managed system (includes hardware and software) Designed and scoped for high performance computing and analytics Technology Near real-time results Handle large volumes of data and complex calculations Optimized for specific customer business issues SAS High Performance Markdown Optimization, SAS High Performance Risk, SAS High Performance Analytics, SAS High Performance Merchandise Planning Analytical computations and data are co-located in a distributed framework. In-memory analytics for rapid execution are able to read from and persist to distributed storage. Partners: Hewlett Packard (HP) for High Performance Solutions; Teradata and EMC/Greenplum for High Performance Analytics 29

30 Backup: Stufen von Analytics

31 31

32 1 STANDARD REPORTS Answer the questions: What happened? When did it happen? Example: Monthly or quarterly financial reports. We all know about these. They re generated on a regular basis and describe just what happened in a particular area. They re useful to some extent, but not for making long-term decisions AD HOC REPORTS Answer the questions: How many? How often? Where? Example: Custom reports that describe the number of hospital patients for every diagnosis code for each day of the week. At their best, ad hoc reports let you ask the questions and request a couple of custom reports to find the answers QUERY DRILLDOWN (OR OLAP) Answer the questions: Where exactly is the problem? How do I find the answers? Example: Sort and explore data about different types of cell phone users and their calling behaviors. Query drilldown allows for a little bit of discovery. OLAP lets you manipulate the data yourself to find out how many, what color and where. ALERTS Answer the questions: When should I react? What actions are needed now? Example: Sales executives receive alerts when sales targets are falling behind. With alerts, you can learn when you have a problem and be notified when something similar happens again in the future. Alerts can appear via , RSS feeds or as red dials on a scorecard or dashboard. 32

33 5 STATISTICAL ANALYSIS Answer the questions: Why is it happening? What opportunities am I missing? Example: Banks can discover why an increasing number of customers are refinancing their homes. Here we can begin to run some complex analytics, like frequency models and regression analysis. We can begin to look at why things are happening using the stored data and then begin to answer questions based on the data FORECASTING Answer the questions: What if these trends continue? How much is needed? When will it be needed? Example: Retailers can predict how demand for individual products will vary from store to store. Forecasting is one of the hottest markets and hottest analytical applications right now. It applies everywhere. In particular, forecasting demand helps supply just enough inventory, so you don t run out or have too much. PREDICTIVE MODELING Answer the questions: What will happen next? How will it affect my business? Example: Hotels and casinos can predict which VIP customers will be more interested in particular vacation packages. If you have 10 million customers and want to do a marketing campaign, who s most likely to respond? How do you segment that group? And how do you determine who s most likely to leave your organization? Predictive modeling provides the answers. OPTIMIZATION Answer the questions: How do we do things better? What is the best decision for a complex problem? Example: Given business priorities, resource constraints and available technology, determine the best way to optimize your IT platform to satisfy the needs of every user. Optimization supports innovation. It takes your resources and needs into consideration and helps you find the best possible way to accomplish your goals. 33

High-Performance Analytics

High-Performance Analytics High-Performance Analytics David Pope January 2012 Principal Solutions Architect High Performance Analytics Practice Saturday, April 21, 2012 Agenda Who Is SAS / SAS Technology Evolution Current Trends

More information

High-Performance Business Analytics: SAS and IBM Netezza Data Warehouse Appliances

High-Performance Business Analytics: SAS and IBM Netezza Data Warehouse Appliances High-Performance Business Analytics: SAS and IBM Netezza Data Warehouse Appliances Highlights IBM Netezza and SAS together provide appliances and analytic software solutions that help organizations improve

More information

In-Memory Analytics for Big Data

In-Memory Analytics for Big Data In-Memory Analytics for Big Data Game-changing technology for faster, better insights WHITE PAPER SAS White Paper Table of Contents Introduction: A New Breed of Analytics... 1 SAS In-Memory Overview...

More information

EMC/Greenplum Driving the Future of Data Warehousing and Analytics

EMC/Greenplum Driving the Future of Data Warehousing and Analytics EMC/Greenplum Driving the Future of Data Warehousing and Analytics EMC 2010 Forum Series 1 Greenplum Becomes the Foundation of EMC s Data Computing Division E M C A CQ U I R E S G R E E N P L U M Greenplum,

More information

Collaborative Big Data Analytics. Copyright 2012 EMC Corporation. All rights reserved.

Collaborative Big Data Analytics. Copyright 2012 EMC Corporation. All rights reserved. Collaborative Big Data Analytics 1 Big Data Is Less About Size, And More About Freedom TechCrunch!!!!!!!!! Total data: bigger than big data 451 Group Findings: Big Data Is More Extreme Than Volume Gartner!!!!!!!!!!!!!!!

More information

Advanced In-Database Analytics

Advanced In-Database Analytics Advanced In-Database Analytics Tallinn, Sept. 25th, 2012 Mikko-Pekka Bertling, BDM Greenplum EMEA 1 That sounds complicated? 2 Who can tell me how best to solve this 3 What are the main mathematical functions??

More information

EMC Greenplum Driving the Future of Data Warehousing and Analytics. Tools and Technologies for Big Data

EMC Greenplum Driving the Future of Data Warehousing and Analytics. Tools and Technologies for Big Data EMC Greenplum Driving the Future of Data Warehousing and Analytics Tools and Technologies for Big Data Steven Hillion V.P. Analytics EMC Data Computing Division 1 Big Data Size: The Volume Of Data Continues

More information

IBM Data Warehousing and Analytics Portfolio Summary

IBM Data Warehousing and Analytics Portfolio Summary IBM Information Management IBM Data Warehousing and Analytics Portfolio Summary Information Management Mike McCarthy IBM Corporation mmccart1@us.ibm.com IBM Information Management Portfolio Current Data

More information

IBM Cognos 10: Enhancing query processing performance for IBM Netezza appliances

IBM Cognos 10: Enhancing query processing performance for IBM Netezza appliances IBM Software Business Analytics Cognos Business Intelligence IBM Cognos 10: Enhancing query processing performance for IBM Netezza appliances 2 IBM Cognos 10: Enhancing query processing performance for

More information

SAS. Predictive Analytics Suite. Overview. Derive useful insights to make evidence-based decisions. Challenges SOLUTION OVERVIEW

SAS. Predictive Analytics Suite. Overview. Derive useful insights to make evidence-based decisions. Challenges SOLUTION OVERVIEW SOLUTION OVERVIEW SAS Predictive Analytics Suite Derive useful insights to make evidence-based decisions Overview Turning increasingly large amounts of data into useful insights and finding how to better

More information

Focus on the business, not the business of data warehousing!

Focus on the business, not the business of data warehousing! Focus on the business, not the business of data warehousing! Adam M. Ronthal Technical Product Marketing and Strategy Big Data, Cloud, and Appliances @ARonthal 1 Disclaimer Copyright IBM Corporation 2014.

More information

MUK-IT 63. Roundtable. Herzlich Willkommen bei der Software AG. Anton Hofmeier VP Sales Terracotta DACH / MdGL

MUK-IT 63. Roundtable. Herzlich Willkommen bei der Software AG. Anton Hofmeier VP Sales Terracotta DACH / MdGL MUK-IT 63. Roundtable Herzlich Willkommen bei der Software AG Anton Hofmeier VP Sales Terracotta DACH / MdGL Überblick February 15, 2013 2 Software AG www.softwareag.com 5.500 Mitarbeiter >1Mrd Umsatz

More information

III JORNADAS DE DATA MINING

III JORNADAS DE DATA MINING III JORNADAS DE DATA MINING EN EL MARCO DE LA MAESTRÍA EN DATA MINING DE LA UNIVERSIDAD AUSTRAL PRESENTACIÓN TECNOLÓGICA IBM Alan Schcolnik, Cognos Technical Sales Team Leader, IBM Software Group. IAE

More information

Mike Maxey. Senior Director Product Marketing Greenplum A Division of EMC. Copyright 2011 EMC Corporation. All rights reserved.

Mike Maxey. Senior Director Product Marketing Greenplum A Division of EMC. Copyright 2011 EMC Corporation. All rights reserved. Mike Maxey Senior Director Product Marketing Greenplum A Division of EMC 1 Greenplum Becomes the Foundation of EMC s Big Data Analytics (July 2010) E M C A C Q U I R E S G R E E N P L U M For three years,

More information

SAP BusinessObjects Mobile So gelangen Ihre Informationen auf mobile Geräte. Jörg Diekkämper 24. April 2015

SAP BusinessObjects Mobile So gelangen Ihre Informationen auf mobile Geräte. Jörg Diekkämper 24. April 2015 SAP BusinessObjects Mobile So gelangen Ihre Informationen auf mobile Geräte Jörg Diekkämper 24. April 2015 2005 2013 Quelle 2014 SAP AG or an SAP affiliate company. All rights reserved. 2 2014 SAP AG or

More information

ANALYTICS IN BIG DATA ERA

ANALYTICS IN BIG DATA ERA ANALYTICS IN BIG DATA ERA ANALYTICS TECHNOLOGY AND ARCHITECTURE TO MANAGE VELOCITY AND VARIETY, DISCOVER RELATIONSHIPS AND CLASSIFY HUGE AMOUNT OF DATA MAURIZIO SALUSTI SAS Copyr i g ht 2012, SAS Ins titut

More information

Tableau Visual Intelligence Platform Rapid Fire Analytics for Everyone Everywhere

Tableau Visual Intelligence Platform Rapid Fire Analytics for Everyone Everywhere Tableau Visual Intelligence Platform Rapid Fire Analytics for Everyone Everywhere Agenda 1. Introductions & Objectives 2. Tableau Overview 3. Tableau Products 4. Tableau Architecture 5. Why Tableau? 6.

More information

Greenplum Database. Getting Started with Big Data Analytics. Ofir Manor Pre Sales Technical Architect, EMC Greenplum

Greenplum Database. Getting Started with Big Data Analytics. Ofir Manor Pre Sales Technical Architect, EMC Greenplum Greenplum Database Getting Started with Big Data Analytics Ofir Manor Pre Sales Technical Architect, EMC Greenplum 1 Agenda Introduction to Greenplum Greenplum Database Architecture Flexible Database Configuration

More information

Big Data and Trusted Information

Big Data and Trusted Information Dr. Oliver Adamczak Big Data and Trusted Information CAS Single Point of Truth 7. Mai 2012 The Hype Big Data: The next frontier for innovation, competition and productivity McKinsey Global Institute 2012

More information

SAS and Oracle: Big Data and Cloud Partnering Innovation Targets the Third Platform

SAS and Oracle: Big Data and Cloud Partnering Innovation Targets the Third Platform SAS and Oracle: Big Data and Cloud Partnering Innovation Targets the Third Platform David Lawler, Oracle Senior Vice President, Product Management and Strategy Paul Kent, SAS Vice President, Big Data What

More information

EMC Greenplum. Big Data meets Big Integration. Wolfgang Disselhoff Sr. Technology Architect, Greenplum. André Münger Sr. Account Manager, Greenplum

EMC Greenplum. Big Data meets Big Integration. Wolfgang Disselhoff Sr. Technology Architect, Greenplum. André Münger Sr. Account Manager, Greenplum EMC Greenplum Big Data meets Big Integration Wolfgang Disselhoff Sr. Technology Architect, Greenplum André Münger Sr. Account Manager, Greenplum 1 2 GREENPLUM DATABASE Industry-Leading Massively Parallel

More information

Architecting for Big Data Analytics and Beyond: A New Framework for Business Intelligence and Data Warehousing

Architecting for Big Data Analytics and Beyond: A New Framework for Business Intelligence and Data Warehousing Architecting for Big Data Analytics and Beyond: A New Framework for Business Intelligence and Data Warehousing Wayne W. Eckerson Director of Research, TechTarget Founder, BI Leadership Forum Business Analytics

More information

Unlock the business value of enterprise data with in-database analytics

Unlock the business value of enterprise data with in-database analytics Unlock the business value of enterprise data with in-database analytics Achieve better business results through faster, more accurate decisions White Paper Table of Contents Executive summary...1 How can

More information

Big Data Analytics. with EMC Greenplum and Hadoop. Big Data Analytics. Ofir Manor Pre Sales Technical Architect EMC Greenplum

Big Data Analytics. with EMC Greenplum and Hadoop. Big Data Analytics. Ofir Manor Pre Sales Technical Architect EMC Greenplum Big Data Analytics with EMC Greenplum and Hadoop Big Data Analytics with EMC Greenplum and Hadoop Ofir Manor Pre Sales Technical Architect EMC Greenplum 1 Big Data and the Data Warehouse Potential All

More information

Datalogix. Using IBM Netezza data warehouse appliances to drive online sales with offline data. Overview. IBM Software Information Management

Datalogix. Using IBM Netezza data warehouse appliances to drive online sales with offline data. Overview. IBM Software Information Management Datalogix Using IBM Netezza data warehouse appliances to drive online sales with offline data Overview The need Infrastructure could not support the growing online data volumes and analysis required The

More information

In-Database Analytics

In-Database Analytics Embedding Analytics in Decision Management Systems In-database analytics offer a powerful tool for embedding advanced analytics in a critical component of IT infrastructure. James Taylor CEO CONTENTS Introducing

More information

Innovative technology for big data analytics

Innovative technology for big data analytics Technical white paper Innovative technology for big data analytics The HP Vertica Analytics Platform database provides price/performance, scalability, availability, and ease of administration Table of

More information

BIG (SMART) DATA ANALYTICS IN ENERGY TRENDS AND BENEFITS

BIG (SMART) DATA ANALYTICS IN ENERGY TRENDS AND BENEFITS BIG (SMART) DATA ANALYTICS IN ENERGY TRENDS AND BENEFITS SAS INSTITUTE 360 POWER FOR AN INTEGRATED COMPANY MANAGEMENT Customer around the globe trust SAS at more than 65.000 locations 91 of Top100 FORTUNE

More information

WHAT S NEW IN SAS 9.4

WHAT S NEW IN SAS 9.4 WHAT S NEW IN SAS 9.4 PLATFORM, HPA & SAS GRID COMPUTING MICHAEL GODDARD CHIEF ARCHITECT SAS INSTITUTE, NEW ZEALAND SAS 9.4 WHAT S NEW IN THE PLATFORM Platform update SAS Grid Computing update Hadoop support

More information

Five Technology Trends for Improved Business Intelligence Performance

Five Technology Trends for Improved Business Intelligence Performance TechTarget Enterprise Applications Media E-Book Five Technology Trends for Improved Business Intelligence Performance The demand for business intelligence data only continues to increase, putting BI vendors

More information

Integrated Big Data: Hadoop + DBMS + Discovery for SAS High Performance Analytics

Integrated Big Data: Hadoop + DBMS + Discovery for SAS High Performance Analytics Paper 1828-2014 Integrated Big Data: Hadoop + DBMS + Discovery for SAS High Performance Analytics John Cunningham, Teradata Corporation, Danville, CA ABSTRACT SAS High Performance Analytics (HPA) is a

More information

Up Your R Game. James Taylor, Decision Management Solutions Bill Franks, Teradata

Up Your R Game. James Taylor, Decision Management Solutions Bill Franks, Teradata Up Your R Game James Taylor, Decision Management Solutions Bill Franks, Teradata Today s Speakers James Taylor Bill Franks CEO Chief Analytics Officer Decision Management Solutions Teradata 7/28/14 3 Polling

More information

Performance and Scalability Overview

Performance and Scalability Overview Performance and Scalability Overview This guide provides an overview of some of the performance and scalability capabilities of the Pentaho Business Analytics Platform. Contents Pentaho Scalability and

More information

DATA is just like CRUDE. It s valuable, but if unrefined it cannot really be used.

DATA is just like CRUDE. It s valuable, but if unrefined it cannot really be used. Data is the new Oil DATA is just like CRUDE. It s valuable, but if unrefined it cannot really be used. Clive Humby "Digitale Informationsspeicher Im Meer der Daten" "Die Menschen produzieren immer mehr

More information

Eric Ledu, The Createch Group, a BELL company

Eric Ledu, The Createch Group, a BELL company Eric Ledu, The Createch Group, a BELL company Intelligence Analytics maturity Past Present Future Predictive Modeling Optimization What is the best that could happen? Raw Data Cleaned Data Standard Reports

More information

Netezza and Business Analytics Synergy

Netezza and Business Analytics Synergy Netezza Business Partner Update: November 17, 2011 Netezza and Business Analytics Synergy Shimon Nir, IBM Agenda Business Analytics / Netezza Synergy Overview Netezza overview Enabling the Business with

More information

Einsatzfelder von IBM PureData Systems und Ihre Vorteile.

Einsatzfelder von IBM PureData Systems und Ihre Vorteile. Einsatzfelder von IBM PureData Systems und Ihre Vorteile demirkaya@de.ibm.com Agenda Information technology challenges PureSystems and PureData introduction PureData for Transactions PureData for Analytics

More information

Performance and Scalability Overview

Performance and Scalability Overview Performance and Scalability Overview This guide provides an overview of some of the performance and scalability capabilities of the Pentaho Business Analytics platform. PENTAHO PERFORMANCE ENGINEERING

More information

Game-Changing Analytics

Game-Changing Analytics Game-Changing Analytics How IT Executives Can Use Analytics to Create Innovation and Business Success WHITE PAPER SAS White Paper Table of Contents The CIO Role: From Tactical Technology Service Provider

More information

High Level Overview. Evolution ready for innovation! Copyright 2011 SAS Institute Inc. All rights reserved.

High Level Overview. Evolution ready for innovation! Copyright 2011 SAS Institute Inc. All rights reserved. High Level Overview SAS 9.3 Evolution ready for innovation! Copyright 2011 SAS Institute Inc. All rights reserved. One of these three things is NOT true about me Landed a plane in an emergency without

More information

Conquering Big Data Analytics with SAS, Teradata and Hadoop

Conquering Big Data Analytics with SAS, Teradata and Hadoop Paper BI15-2014 Conquering Big Data Analytics with SAS, Teradata and Hadoop John Cunningham, Teradata Corporation, Danville, California Tho Nguyen, Teradata Corporation, Raleigh, North Carolina Paul Segal,

More information

Customer Insight Appliance. Enabling retailers to understand and serve their customer

Customer Insight Appliance. Enabling retailers to understand and serve their customer Customer Insight Appliance Enabling retailers to understand and serve their customer Customer Insight Appliance Enabling retailers to understand and serve their customer. Technology has empowered today

More information

Find the Hidden Signal in Market Data Noise

Find the Hidden Signal in Market Data Noise Find the Hidden Signal in Market Data Noise Revolution Analytics Webinar, 13 March 2013 Andrie de Vries Business Services Director (Europe) @RevoAndrie andrie@revolutionanalytics.com Agenda Find the Hidden

More information

Making Sense of the Madness

Making Sense of the Madness Making Sense of the Madness Deploying Big Data techniques to deal with real world Bigish Data issues Copyright James Mitchell 2014 1 Introduction Warning! Parental Guidance Recommended Please read the

More information

Hadoop Beyond Hype: Complex Adaptive Systems Conference Nov 16, 2012. Viswa Sharma Solutions Architect Tata Consultancy Services

Hadoop Beyond Hype: Complex Adaptive Systems Conference Nov 16, 2012. Viswa Sharma Solutions Architect Tata Consultancy Services Hadoop Beyond Hype: Complex Adaptive Systems Conference Nov 16, 2012 Viswa Sharma Solutions Architect Tata Consultancy Services 1 Agenda What is Hadoop Why Hadoop? The Net Generation is here Sizing the

More information

BIG DATA APPLIANCES. July 23, TDWI. R Sathyanarayana. Enterprise Information Management & Analytics Practice EMC Consulting

BIG DATA APPLIANCES. July 23, TDWI. R Sathyanarayana. Enterprise Information Management & Analytics Practice EMC Consulting BIG DATA APPLIANCES July 23, TDWI R Sathyanarayana Enterprise Information Management & Analytics Practice EMC Consulting 1 Big data are datasets that grow so large that they become awkward to work with

More information

Predictive Analytics: Too Important to Ignore The six secrets to success with predictive analytics

Predictive Analytics: Too Important to Ignore The six secrets to success with predictive analytics Predictive Analytics: Too Important to Ignore The six secrets to success with predictive analytics Webinar December 18, 2013 Sponsored by: Tony Cosentino VP & Research Director, Business Analytics Ventana

More information

Solve your toughest challenges with data mining

Solve your toughest challenges with data mining IBM Software IBM SPSS Modeler Solve your toughest challenges with data mining Use predictive intelligence to make good decisions faster Solve your toughest challenges with data mining Imagine if you could

More information

How to make BIG DATA work for you. Faster results with Microsoft SQL Server PDW

How to make BIG DATA work for you. Faster results with Microsoft SQL Server PDW How to make BIG DATA work for you. Faster results with Microsoft SQL Server PDW Roger Breu PDW Solution Specialist Microsoft Western Europe Marcus Gullberg PDW Partner Account Manager Microsoft Sweden

More information

Age of Analytics: Competing in the 21 st Century

Age of Analytics: Competing in the 21 st Century SAS Analytics Day Age of Analytics: Competing in the 21 st Century Dr. Radhika Kulkarni Vice President, Advanced Analytics R&D SAS Institute April 22, 2011 Outline Key challenges in today s marketplace

More information

Hadoop and Relational Database The Best of Both Worlds for Analytics Greg Battas Hewlett Packard

Hadoop and Relational Database The Best of Both Worlds for Analytics Greg Battas Hewlett Packard Hadoop and Relational base The Best of Both Worlds for Analytics Greg Battas Hewlett Packard The Evolution of Analytics Mainframe EDW Proprietary MPP Unix SMP MPP Appliance Hadoop? Questions Is Hadoop

More information

Next Generation Data Warehousing Appliances 23.10.2014

Next Generation Data Warehousing Appliances 23.10.2014 Next Generation Data Warehousing Appliances 23.10.2014 Presentert av: Espen Jorde, Executive Advisor Bjørn Runar Nes, CTO/Chief Architect Bjørn Runar Nes Espen Jorde 2 3.12.2014 Agenda Affecto s new Data

More information

Microsoft Business Intelligence solution. What makes Microsoft BI difference

Microsoft Business Intelligence solution. What makes Microsoft BI difference Business Intelligence today Microsoft Business Intelligence solution What makes Microsoft BI difference Case study and Demo Gartner BI Platform Software Revenue (in $Billions) CIO Priorities: Data Analysis

More information

VIEWPOINT. High Performance Analytics. Industry Context and Trends

VIEWPOINT. High Performance Analytics. Industry Context and Trends VIEWPOINT High Performance Analytics Industry Context and Trends In the digital age of social media and connected devices, enterprises have a plethora of data that they can mine, to discover hidden correlations

More information

Intelligent Business Operations

Intelligent Business Operations Intelligent Business Operations Energieeffizienz durch den Einsatz von IBO Sascha Höcherl 11.11.2015 Software AG Transformation zum digitalen Unternehmen CUSTOMER BASE More than 70 % of the Global 1000

More information

Big Data and Your Data Warehouse Philip Russom

Big Data and Your Data Warehouse Philip Russom Big Data and Your Data Warehouse Philip Russom TDWI Research Director for Data Management April 5, 2012 Sponsor Speakers Philip Russom Research Director, Data Management, TDWI Peter Jeffcock Director,

More information

ROME, 17-10-2013 BIG DATA ANALYTICS

ROME, 17-10-2013 BIG DATA ANALYTICS ROME, 17-10-2013 BIG DATA ANALYTICS BIG DATA FOUNDATIONS Big Data is #1 on the 2012 and the 2013 list of most ambiguous terms - Global language monitor 2 BIG DATA FOUNDATIONS Big Data refers to data sets

More information

In-memory computing with SAP HANA

In-memory computing with SAP HANA In-memory computing with SAP HANA June 2015 Amit Satoor, SAP @asatoor 2015 SAP SE or an SAP affiliate company. All rights reserved. 1 Hyperconnectivity across people, business, and devices give rise to

More information

Accelerate Business Advantage with Dynamic Warehousing

Accelerate Business Advantage with Dynamic Warehousing Accelerate Business Advantage with Dynamic Warehousing Mark McConnell Marketing Executive, Information Management IBM Asia Pacific 2007 IBM Corporation Is Information Technology delivering? Source: IBM

More information

Harnessing the power of advanced analytics with IBM Netezza

Harnessing the power of advanced analytics with IBM Netezza IBM Software Information Management White Paper Harnessing the power of advanced analytics with IBM Netezza How an appliance approach simplifies the use of advanced analytics Harnessing the power of advanced

More information

SAS In-Database. Forum Analytique d'affaires SAS 1 Déc. 2010. Ronald Allard SAS Montréal. Copyright 2010 SAS Institute Inc. All rights reserved.

SAS In-Database. Forum Analytique d'affaires SAS 1 Déc. 2010. Ronald Allard SAS Montréal. Copyright 2010 SAS Institute Inc. All rights reserved. SAS In-Database Forum Analytique d'affaires SAS 1 Déc. 2010 Ronald Allard SAS Montréal Copyright 2010 SAS Institute Inc. All rights reserved. Agenda Défis des entreprises Qu est-ce que SAS In-Database?

More information

Enabling Big Data with Cloud. Go faster Reduce risk Scale as you grow Avoid mistakes

Enabling Big Data with Cloud. Go faster Reduce risk Scale as you grow Avoid mistakes Enabling Big Data with Cloud Go faster Reduce risk Scale as you grow Avoid mistakes Dr. Phil Shelley Why Cloud and Big Data? Complexity Speed Cost Skills Support Technology Analytics 2.0 Industry Trends

More information

The Evolution of Microsoft SQL Server: The right time for Violin flash Memory Arrays

The Evolution of Microsoft SQL Server: The right time for Violin flash Memory Arrays The Evolution of Microsoft SQL Server: The right time for Violin flash Memory Arrays Executive Summary Microsoft SQL has evolved beyond serving simple workgroups to a platform delivering sophisticated

More information

Enterprise Data Management in an In-Memory World

Enterprise Data Management in an In-Memory World Enterprise Data Management in an In-Memory World Tactics for Loading SAS High-Performance Analytics Server and SAS Visual Analytics WHITE PAPER SAS White Paper Table of Contents Executive Summary.... 1

More information

Sybase IQ Supercharges Predictive Analytics

Sybase IQ Supercharges Predictive Analytics SOLUTIONS BROCHURE Sybase IQ Supercharges Predictive Analytics Deliver smarter predictions with Sybase IQ for SAP BusinessObjects users Optional Photos Here (fill space) www.sybase.com SOLUTION FEATURES

More information

Wolkige Versprechungen - Freiraum mit Tuecken

Wolkige Versprechungen - Freiraum mit Tuecken Wolkige Versprechungen - Freiraum mit Tuecken Aria_Naderi@bmc.com Wolkige Versprechungen Im Rechenzentrum Wölkchen sind inzwischen bereits einige Wölkchen am Netz Himmel aufgezogen, doch eine dichte Wolkendecke

More information

Accelerating Business Analytics

Accelerating Business Analytics Tech Dossier Accelerating Business Analytics Combining Grid Computing and In-Database Processing to Solve Big Data Problems Tech Dossier: Accelerating Business Analytics 2 High-Performance Mandate... 3

More information

Hadoop and Data Warehouse Friends, Enemies or Profiteers? What about Real Time?

Hadoop and Data Warehouse Friends, Enemies or Profiteers? What about Real Time? Hadoop and Data Warehouse Friends, Enemies or Profiteers? What about Real Time? Kai Wähner kwaehner@tibco.com @KaiWaehner www.kai-waehner.de Disclaimer! These opinions are my own and do not necessarily

More information

HIGH PERFORMANCE ANALYTICS FOR TERADATA

HIGH PERFORMANCE ANALYTICS FOR TERADATA F HIGH PERFORMANCE ANALYTICS FOR TERADATA F F BORN AND BRED IN FINANCIAL SERVICES AND HEALTHCARE. DECADES OF EXPERIENCE IN PARALLEL PROGRAMMING AND ANALYTICS. FOCUSED ON MAKING DATA SCIENCE HIGHLY PERFORMING

More information

An Integrated Analytics & Big Data Infrastructure September 21, 2012 Robert Stackowiak, Vice President Data Systems Architecture Oracle Enterprise

An Integrated Analytics & Big Data Infrastructure September 21, 2012 Robert Stackowiak, Vice President Data Systems Architecture Oracle Enterprise An Integrated Analytics & Big Data Infrastructure September 21, 2012 Robert Stackowiak, Vice President Data Systems Architecture Oracle Enterprise Solutions Group The following is intended to outline our

More information

Understanding Data Warehouse Needs Session #1568 Trends, Issues and Capabilities

Understanding Data Warehouse Needs Session #1568 Trends, Issues and Capabilities Understanding Data Warehouse Needs Session #1568 Trends, Issues and Capabilities Dr. Frank Capobianco Advanced Analytics Consultant Teradata Corporation Tracy Spadola CPCU, CIDM, FIDM Practice Lead - Insurance

More information

Ramesh Bhashyam Teradata Fellow Teradata Corporation bhashyam.ramesh@teradata.com

Ramesh Bhashyam Teradata Fellow Teradata Corporation bhashyam.ramesh@teradata.com Challenges of Handling Big Data Ramesh Bhashyam Teradata Fellow Teradata Corporation bhashyam.ramesh@teradata.com Trend Too much information is a storage issue, certainly, but too much information is also

More information

Main Memory Data Warehouses

Main Memory Data Warehouses Main Memory Data Warehouses Robert Wrembel Poznan University of Technology Institute of Computing Science Robert.Wrembel@cs.put.poznan.pl www.cs.put.poznan.pl/rwrembel Lecture outline Teradata Data Warehouse

More information

Moving Large Data at a Blinding Speed for Critical Business Intelligence. A competitive advantage

Moving Large Data at a Blinding Speed for Critical Business Intelligence. A competitive advantage Moving Large Data at a Blinding Speed for Critical Business Intelligence A competitive advantage Intelligent Data In Real Time How do you detect and stop a Money Laundering transaction just about to take

More information

2009 Oracle Corporation 1

2009 Oracle Corporation 1 The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material,

More information

BIG DATA-AS-A-SERVICE

BIG DATA-AS-A-SERVICE White Paper BIG DATA-AS-A-SERVICE What Big Data is about What service providers can do with Big Data What EMC can do to help EMC Solutions Group Abstract This white paper looks at what service providers

More information

EMC Federation Big Data Solutions. Copyright 2015 EMC Corporation. All rights reserved.

EMC Federation Big Data Solutions. Copyright 2015 EMC Corporation. All rights reserved. EMC Federation Big Data Solutions 1 Introduction to data analytics Federation offering 2 Traditional Analytics! Traditional type of data analysis, sometimes called Business Intelligence! Type of analytics

More information

Big Data Are You Ready? Jorge Plascencia Solution Architect Manager

Big Data Are You Ready? Jorge Plascencia Solution Architect Manager Big Data Are You Ready? Jorge Plascencia Solution Architect Manager Big Data: The Datafication Of Everything Thoughts Devices Processes Thoughts Things Processes Run the Business Organize data to do something

More information

Big Data Are You Ready? Thomas Kyte http://asktom.oracle.com

Big Data Are You Ready? Thomas Kyte http://asktom.oracle.com Big Data Are You Ready? Thomas Kyte http://asktom.oracle.com The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated

More information

Big Data Technologies Compared June 2014

Big Data Technologies Compared June 2014 Big Data Technologies Compared June 2014 Agenda What is Big Data Big Data Technology Comparison Summary Other Big Data Technologies Questions 2 What is Big Data by Example The SKA Telescope is a new development

More information

Upgrading to Microsoft SQL Server 2008 R2 from Microsoft SQL Server 2008, SQL Server 2005, and SQL Server 2000

Upgrading to Microsoft SQL Server 2008 R2 from Microsoft SQL Server 2008, SQL Server 2005, and SQL Server 2000 Upgrading to Microsoft SQL Server 2008 R2 from Microsoft SQL Server 2008, SQL Server 2005, and SQL Server 2000 Your Data, Any Place, Any Time Executive Summary: More than ever, organizations rely on data

More information

Big Data + Big Analytics Transforming the way you do business

Big Data + Big Analytics Transforming the way you do business Big Data + Big Analytics Transforming the way you do business Bryan Harris Chief Technology Officer VSTI A SAS Company 1 AGENDA Lets get Real Beyond the Buzzwords Who is SAS? Our PerspecDve of Big Data

More information

SAS. Predictive Analytics. Overview. Turning Your Data into Timely Insight for Better, Faster Decision Making. Challenges SOLUTION OVERVIEW

SAS. Predictive Analytics. Overview. Turning Your Data into Timely Insight for Better, Faster Decision Making. Challenges SOLUTION OVERVIEW SOLUTION OVERVIEW SAS Predictive Analytics Turning Your Data into Timely Insight for Better, Faster Decision Making Overview Is your organization overflowing with enterprise data but failing to turn it

More information

Poslovni slučajevi upotrebe IBM Netezze

Poslovni slučajevi upotrebe IBM Netezze Poslovni slučajevi upotrebe IBM Netezze data at the Speed and with Simplicity businesses need 25. ožujak 2015. vedran.travica@hr.ibm.com Agenda A. IBM PureData for Analytics Netezza B. Scenarij 1.: Novi

More information

TRANSFORM BIG DATA INTO ACTIONABLE INFORMATION

TRANSFORM BIG DATA INTO ACTIONABLE INFORMATION TRANSFORM BIG DATA INTO ACTIONABLE INFORMATION Make Big Available for Everyone Syed Rasheed Solution Marketing Manager January 29 th, 2014 Agenda Demystifying Big Challenges Getting Bigger Red Hat Big

More information

Intro to BI. Mul0- dimensional Analysis

Intro to BI. Mul0- dimensional Analysis Intro to BI BI Vendor Landscape BI Roles & Responsibili0es Data Governance and Quality DW Architectures ETL Processes BI Capabili0es & Maturity Mul0- dimensional Analysis BI Vendors and Products Module

More information

Achieving Business Value through Big Data Analytics Philip Russom

Achieving Business Value through Big Data Analytics Philip Russom Achieving Business Value through Big Data Analytics Philip Russom TDWI Research Director for Data Management October 3, 2012 Sponsor 2 Speakers Philip Russom Research Director, Data Management, TDWI Brian

More information

SAS High-Performance Analytics

SAS High-Performance Analytics SAS High-Performance Analytics What Could You Do with Faster, Better Answers? Transform Your Organization and Gain Competitive Advantage WHITE PAPER SAS White Paper Table of Contents Executive Summary....1

More information

From Big Data to Meaningful Information with SAS High-Performance Analytics

From Big Data to Meaningful Information with SAS High-Performance Analytics 20 From Big Data to Meaningful Information with SAS High-Performance Analytics From Big Data to Meaningful Information with SAS High-Performance Analytics Silvia BOLOHAN, Sebastian CIOBANU SAS Analytical

More information

Big Data & QlikView. Democratizing Big Data Analytics. David Freriks Principal Solution Architect

Big Data & QlikView. Democratizing Big Data Analytics. David Freriks Principal Solution Architect Big Data & QlikView Democratizing Big Data Analytics David Freriks Principal Solution Architect TDWI Vancouver Agenda What really is Big Data? How do we separate hype from reality? How does that relate

More information

Microsoft Analytics Platform System. Solution Brief

Microsoft Analytics Platform System. Solution Brief Microsoft Analytics Platform System Solution Brief Contents 4 Introduction 4 Microsoft Analytics Platform System 5 Enterprise-ready Big Data 7 Next-generation performance at scale 10 Engineered for optimal

More information

Real-Time Data Integration for BI and Data Warehousing

<Insert Picture Here> Real-Time Data Integration for BI and Data Warehousing Real-Time Data Integration for BI and Data Warehousing Agenda Why Real-Time Data for BI? Architectures for Real-Time BI Oracle GoldenGate for Real-Time Data Integration Customer Examples

More information

Introducing Oracle Exalytics In-Memory Machine

Introducing Oracle Exalytics In-Memory Machine Introducing Oracle Exalytics In-Memory Machine Jon Ainsworth Director of Business Development Oracle EMEA Business Analytics 1 Copyright 2011, Oracle and/or its affiliates. All rights Agenda Topics Oracle

More information

A Whole New World. Big Data Technologies Big Discovery Big Insights Endless Possibilities

A Whole New World. Big Data Technologies Big Discovery Big Insights Endless Possibilities A Whole New World Big Data Technologies Big Discovery Big Insights Endless Possibilities Dr. Phil Shelley Query Execution Time Why Big Data Technology? Days EDW Hours Hadoop Minutes Presto Seconds Milliseconds

More information

Beyond the Single View with IBM InfoSphere

Beyond the Single View with IBM InfoSphere Ian Bowring MDM & Information Integration Sales Leader, NE Europe Beyond the Single View with IBM InfoSphere We are at a pivotal point with our information intensive projects 10-40% of each initiative

More information

Inge Os Sales Consulting Manager Oracle Norway

Inge Os Sales Consulting Manager Oracle Norway Inge Os Sales Consulting Manager Oracle Norway Agenda Oracle Fusion Middelware Oracle Database 11GR2 Oracle Database Machine Oracle & Sun Agenda Oracle Fusion Middelware Oracle Database 11GR2 Oracle Database

More information

Three steps to put Predictive Analytics to Work

Three steps to put Predictive Analytics to Work Three steps to put Predictive Analytics to Work The most powerful examples of analytic success use Decision Management to deploy analytic insight in day to day operations helping organizations make more

More information

IBM Netezza High Capacity Appliance

IBM Netezza High Capacity Appliance IBM Netezza High Capacity Appliance Petascale Data Archival, Analysis and Disaster Recovery Solutions IBM Netezza High Capacity Appliance Highlights: Allows querying and analysis of deep archival data

More information