Achieving Business Value through Big Data Analytics Philip Russom

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Achieving Business Value through Big Data Analytics Philip Russom"

Transcription

1 Achieving Business Value through Big Data Analytics Philip Russom TDWI Research Director for Data Management October 3, 2012

2 Sponsor 2

3 Speakers Philip Russom Research Director, Data Management, TDWI Brian Ng Director, Enterprise Services, HP

4 Today s Agenda The Need for Business Value from Big Data Definitions of Big Data Analytics Use Cases for Big Data Analytics that deliver Business Value The Future & How to Prepare for It

5 Background The quantity and diversity of Big Data has been exploding for years Traditional applications grow larger & more numerous every day Older big data sources: RFID, call detail record, machine/robotic data New big data sources: sensors, social media, new Web apps User organizations are starting to achieve business value from big data The consensus today is that Advanced Analytics yields valuable business insights As long as big data is managed well and treated to the right forms of analytics Today we ll look at how Big Data Analytics can deliver business value In your organization is big data considered mostly a problem or mostly an opportunity? 70% 30% Opportunity because it yields detailed analytics for business advantage Problem because it's hard to manage from a technical viewpoint Source TDWI. Survey of 325 respondents, June 2011

6 Big Data Advanced Analytics Definition of Big Data Analytics It s where advanced analytic techniques operate on big data sets. It s about two things: big data AND advanced analytics. The two have teamed up to leverage big data. The combo turns big data into an opportunity. Big Data isn t new. Advanced Analytics isn t new. Their successful combination is new. Both users and technologies are now more capable of success. The combo is new & technical. But hasn t yet aligned with business. Big Data Analytics

7 The 3 Vs of Big Data summarize technical properties Business Value should be the 4th V, since this is what IT must deliver. VOLUME VOLUME VELOCITY VARIETY VELOCITY VARIETY BUSINESS VALUE

8 Defining Advanced Analytics OLAP & its Variants Users have this They ll keep & grow it OLAP won t go away Advanced Analytics Discovery oriented Excels with Big Data Experiencing strong adoption by users Online Analytic Processing (OLAP) It s somewhat rudimentary, but required. Demands multidimensional data modeling, but works well with most EDWs. There are multiple approaches to OLAP. Extreme SQL Uses well-known SQL-based tools & techniques. Relies on long, complex SQL statements. Predictive Analytics Uses data mining and/or statistics to anticipate future events. Multi-Structured Data Analytics Natural language processing (NLP) Search, text analytics, sentiment & social analytic apps Other Analytic Methods Visualization, artificial intelligence Analytic database functions: in-database analytics, inmemory databases, columnar data stores, appliances, etc.

9 TDWI SURVEY SAYS: Opportunities for Big Data Analytics Anything involving customers benefits from big data analytics better-targeted social-influencer marketing (61%) customer-base segmentation (41%) recognition of sales/market opportunities (38%) BI, in general, benefits from big data analytics more numerous and accurate business insights (45%) understanding business change (30%) better planning and forecasting (29%) identification of root causes of cost (29%) Specific analytics applications are likely beneficiaries detection of fraud (33%), quantification of risks (30%) market sentiment trending (30%) Source TDWI. Survey of 325 respondents, June 2011

10 USE CASE Exploratory Analytics with Big Data Big Data enables exploratory analytics. Discover patterns and new facts the business didn t know Customer base segments Customer behaviors and their meaning Forms of churn and their root causes Relationships among customers and products Root causes for bottom line costs State of biz today; predict future events

11 USE CASE Analyze Big Data You ve Hoarded Yes, it s true: Many firms have squirreled away large datasets, because they sensed business value, yet didn t know how to get value out of big data. Finally understand: Web site visitor behavior Products of affinity based on ecommerce shopping carts Product and supply quality based on robotic & QA data from manufacturing Product movement via RFID in retail

12 USE CASE Big Data Analytics per Industry The type and content of big data can vary by industry, thus have different value propositions per industry: Call detail records (CDRs) in telecommunications RFID in retail, manufacturing, and other product-oriented industries Sensor data from robots in manufacturing, especially automotive and consumer electronics

13 USE CASE Analytics for Unstructured Big Data Tools based on natural language processing, search, and text analytics (plus new platforms like Hadoop) provide visibility into text-laden business processes: Claims process in insurance Medical records in healthcare Call center and help desk applications in any industry Sentiment analysis in customer-oriented businesses, with both enterprise and social media big data

14 I love/hate your product! USE CASE Customer Analytics with Social Media Data Customers can influence each other by commenting on brands, reviewing products, reacting to marketing campaigns, and revealing shared interests Predictive analytics to discover patterns, anticipate product/service issues Measuring share of voice and brand reputation Broader input for customer satisfaction Understanding sentiment drivers Voice of the customer analytics Determining marketing effectiveness Identifying new customer segments

15 USE CASE Big Data for Complete Customer Views Big data can add more granular detail to analytic datasets: Data from all customer touch points Broaden 360-degree views of customers and other entities, from hundreds of attributes to thousands For more detailed and accurate customer base segmentation, direct marketing, and other customer analytics

16 USE CASE Big Data Can Improve Older Analytics Big data enlarges and improves data samples for older analytic applications: Any analytic technologies that depend on large samples, such as statistics or data mining Fraud detection Risk management Actuarial calculations

17 USE CASE Analytics with Streaming Big Data Monitoring & Analysis in True Real Time Energy utility, communication network; any grid, service, facility Surveillance, cyber security, situational awareness Fraud detection, risk calc Logistics, truck/rail freight, mobile asset mgt Near Time Review of loan applications submitted online $$$$$ $$$$$ $$$$$ $$$$$ $$$$$

18 A Look Into the Future of Big Data Analytics Big data analytics is here to stay It will spread into more apps in more industries, becoming mainstream Big data will be less of a problem Due to advances in storage, clouds, CPUs, memory, databases, analytic tools, etc. Analytics will draw biz value from big data That s why the two have come together New types of analytic apps will appear Old ones will be revamped Big Data Analytics is mostly batch today Will go real time as users/techs mature Analytics is new competency for many shops They will hire & train, plus acquire tools and seek professional services

19 Recommendations Insist on business value from big data Don t merely hoard it in a cost center that wastes valuable storage & other resources The path to business value is through analytics Go beyond reporting and OLAP into advanced analytics You need discovery analytics, but reporting and OLAP won t go away Embrace the brave new world of big data Data from Web, machine, and social sources Upgrade, extend or distribute your BI/DW tech stack and other software portfolios with technologies for big data and analytics Change is needed to accommodate analytics with big data Give the business what it needs Discovery analytics to understand change, find opportunities Broader, more complete views of customers & other business entities Analytics tailored to your industry and your organization s unique requirements

20 Achieving business value through big data analytics HP Enterprise Services, Information Management and Analytics, Brian Ng / Oct 2012 Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

21 Our point of view Thriving in the age of big data Call Records Sentimen t Risk RFID Sensors Claims Social Fraud We are at a fundamental inflection point in the evolution of information and intelligence. Traditional approaches, architectures and organizations models were not designed for today s complexity. Leadership will be defined by those who excel in information sciences, via innovative solutions, advanced technologies & new 21 talent Copyright models Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

22 Use cases and architecture Copyright Copyright Hewlett-Packard Development Company, Company, L.P. L.P. The information The information contained contained herein herein is subject is subject to change to change without without notice. notice.

23 Data capture 1. Unstructured and structured analysis Logical architecture Service management Portfolio management Operations management Event processing Complex event processing Data acquisition Human Language Rich Media Structured data Semistructured data External data Internal data Unstructure d data Rules engine Capture Data transformation Master data Derive metadata and Data index quality Master data mgt Match and Integration combine Aggregation Populate repositories SOA services Repository Raw Data Repository Relational DBMS data warehouse Applications Non-relational DBMS (e.g. Relational HDFS, Hbase,...) DBMS Staging Non-relational Integration Enterprise DBMS DW (content mgt systems) Applications Sentiment, Mark-up & Integrate Data Virtualization Data mart (e.g. OLAP Data cube) marts Olap Real-time cubes analytical RDBMS Analysis and reporting Rules generator Analytical Data mining Data Mart engine SQL analytics engine Reporting Search Dashboards engine Olap Statistical analysis NoSQL Data mining (e.g. Visualization MapReduce) engine Visualization Visualization Analysis Static and OLAP reports Dashboards and alerts Statistical analysis Predictive analysis Governance Data governance Data audit, balance and control 23 Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

24 Data capture 2. Machine generated data streams Logical architecture Service management Portfolio management Operations management Event processing Complex Applications event processing Rules engine Data acquisition Sensor Network Structured data Semistructured data External data Internal data Unstructure d data Capture Data transformation Master data Derive metadata and Data index quality Master data mgt Match and Integration combine Aggregation Populate repositories Complex Event Processing SOA Rules services Engine Applications Repository Raw Data Markup, stream, Repository integrate Relational DBMS Data data warehouse Virtualization Non-relational DBMS (e.g. Relational HDFS, Hbase,...) DBMS Staging Non-relational Integration Enterprise DBMS DW (content mgt systems) Data mart (e.g. OLAP Data cube) marts Olap Real-time cubes analytical RDBMS Analysis and reporting Rules generator Analytical Data mining Data Mart engine SQL analytics engine Reporting Search Dashboards engine Olap Statistical analysis NoSQL Data mining (e.g. Visualization MapReduce) engine Visualization Model and Rule development. Static and Real OLAP time reports visualization and analysis Dashboards and alerts Statistical analysis Predictive analysis Governance Data governance Data audit, balance and control 24 Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

25 Use case: Insurance claim fraud Fraud detection Business issue Insurance claim fraud continues to be a major cost Big Data sources Claims form (human language) Contact records (call center logs, audio, , instant message, video calls) Process Sentiment analysis and meaning-based scoring Input structured result-set into fraud analysis Machine learning for key patterns Business benefit Avoid cost Improve margins Competitive pricing 25 Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

26 Data capture Use case: Insurance claim fraud Human language data and analysis Service management Portfolio management Operations management Event processing Complex event processing Data acquisition Claims form contact data Structured data Semistructured data External data Internal data Unstructure d data Rules engine Capture Data transformation Master data Derive metadata and Data index quality Master data mgt Match and Integration combine Aggregation Populate repositories SOA services Raw data repository Repository Claims application Applications Sentiment analyses & Relational integrate DBMS data warehouse Non-relational DBMS (e.g. Relational HDFS, Hbase,...) DBMS Staging Non-relational Integration Enterprise DBMS DW (content mgt systems) Analytical Data data mart Virtualization Data mart (e.g. OLAP Data cube) marts Olap Real-time cubes analytical RDBMS Analysis and reporting Rules generator Data mining engine SQL analytics engine Reporting Search Dashboards engine Olap Statistical analysis NoSQL Data mining (e.g. Visualization MapReduce) engine Visualization Static and OLAP reports Dashboards and alerts Statistical analysis Predictive analysis Governance Data governance Data audit, balance and control 26 Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

27 Use case: Operations Optimization Supply Chain Business Issue Under utilized facilities Less effective Supply and Delivery Chains Less accurate R&D Big Data sources Sensors in supply/delivery chains Network sensors (communication, smart grid) Physical sensors (seismic, health, equipment) Process Statistical, Segmentation and Pattern analysis Real time advanced visualization Business Benefit Optimized supply and delivery chain operations Better utilization of facilities 27 Improved R&D results Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

28 Data capture Machine generated data streams Logical architecture Service management Portfolio management Operations management Event processing Process Complex Applications event Rules engine processing Data acquisition Sensor Network Structured data Semistructured data External data Internal data Unstructure d data Capture Data transformation Master data Derive metadata and Data index quality Master data mgt Match and Integration combine Aggregation Populate repositories Complex Event Processing SOA services Applications Repository Raw Data Markup, stream, Repository integrate Relational DBMS Data data warehouse Virtualization Non-relational DBMS (e.g. Relational HDFS, Hbase,...) DBMS Staging Non-relational Integration Enterprise DBMS DW (content mgt systems) Data mart (e.g. OLAP Data cube) marts Olap Real-time cubes analytical RDBMS Analysis and reporting Rules generator Analytical Data mining Data Mart engine SQL analytics engine Reporting Search Dashboards engine Olap Statistical analysis NoSQL Data mining (e.g. Visualization MapReduce) engine Visualization Model and Rule development. Static and Real OLAP time reports visualization and analysis Dashboards and alerts Statistical analysis Predictive analysis Governance Data governance Data audit, balance and control 28 Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

29 Information Taxonomy Taxonomy Aggregation HP Changing the Analytics Paradigm Advanced Analytics using Vertica, Autonomy, and Hadoop Information Insight by business analyst Business Users Search Engine, Trends (Market, Consumers, etc) Data Exploration 1 NoSQL Business Objects, Cognos, OBIEE, Microstrategy SQL SAS, R Predictive, Performance, Operations Advanced Analytics 5 2 Meaning Based Computing Operational Data Store Teradata, Oracle, DB2 Analytic Data Store Vertica 4 Taxonomy Aggregation (IDOL) Information Transformation Automated Information Integration Hadoop Unstructured Data Store 3 Structured Transaction Data Device Data Seamless Data Exploration and Analytics Ability explore unstructured information to uncover important attributes, time periods, groups, or areas of information using Non-Sql techniques 1. Conduct Information research using data visualization, trends, and Google like search tools by accessing the Hadoop information repository 2. Leverages a common information Unstructured taxonomy (ontology) that creates Consumer Data business views across all information from all sources 3. Automatically move this data from research to analytics environment 4. Conduct Business Analytics using metrics and KPI s 5. All from real-time information initiated from End User request 29 Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

30 Next steps MasterPlan Services Business Value Assessment Information Strategy and Organization Services Business Solutions Social Intelligence Advanced Analytics On Premise Managed Service Cloud Service Strategy Roadmap Design Implement Consume Big Data Experience Transformation Workshop Social Intelligence Workshop EDW OnTrack Workshop BI Implementation Advanced Information Services for HP, SAP and Microsoft 30 Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

31 Thank you Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

32 Questions? 32

33 Contacting Speakers If you have further questions or comments: Philip Russom, TDWI Brian Ng, HP

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

Evolving Data Warehouse Architectures

Evolving Data Warehouse Architectures Evolving Data Warehouse Architectures In the Age of Big Data Philip Russom April 15, 2014 TDWI would like to thank the following companies for sponsoring the 2014 TDWI Best Practices research report: Evolving

More information

Integrating Hadoop. Into Business Intelligence & Data Warehousing. Philip Russom TDWI Research Director for Data Management, April 9 2013

Integrating Hadoop. Into Business Intelligence & Data Warehousing. Philip Russom TDWI Research Director for Data Management, April 9 2013 Integrating Hadoop Into Business Intelligence & Data Warehousing Philip Russom TDWI Research Director for Data Management, April 9 2013 TDWI would like to thank the following companies for sponsoring the

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 May 7, 2013 Sponsor Speakers Philip Russom TDWI Research Director, Data Management Chris Twogood VP, Product and

More information

The Intersection of Big Data and Analytics. Philip Russom TDWI Research Director for Data Management May 5, 2011

The Intersection of Big Data and Analytics. Philip Russom TDWI Research Director for Data Management May 5, 2011 The Intersection of Big Data and Analytics Philip Russom TDWI Research Director for Data Management May 5, 2011 Sponsor 2 Speakers Philip Russom TDWI Research Director, Data Management Francois Ajenstat

More information

Getting Started Practical Input For Your Roadmap

Getting Started Practical Input For Your Roadmap Getting Started Practical Input For Your Roadmap Mike Ferguson Managing Director, Intelligent Business Strategies BA4ALL Big Data & Analytics Insight Conference Stockholm, May 2015 About Mike Ferguson

More information

DAMA NY DAMA Day October 17, 2013 IBM 590 Madison Avenue 12th floor New York, NY

DAMA NY DAMA Day October 17, 2013 IBM 590 Madison Avenue 12th floor New York, NY Big Data Analytics DAMA NY DAMA Day October 17, 2013 IBM 590 Madison Avenue 12th floor New York, NY Tom Haughey InfoModel, LLC 868 Woodfield Road Franklin Lakes, NJ 07417 201 755 3350 tom.haughey@infomodelusa.com

More information

Architecting for the Internet of Things & Big Data

Architecting for the Internet of Things & Big Data Architecting for the Internet of Things & Big Data Robert Stackowiak, Oracle North America, VP Information Architecture & Big Data September 29, 2014 Safe Harbor Statement The following is intended to

More information

5 Keys to Unlocking the Big Data Analytics Puzzle. Anurag Tandon Director, Product Marketing March 26, 2014

5 Keys to Unlocking the Big Data Analytics Puzzle. Anurag Tandon Director, Product Marketing March 26, 2014 5 Keys to Unlocking the Big Data Analytics Puzzle Anurag Tandon Director, Product Marketing March 26, 2014 1 A Little About Us A global footprint. A proven innovator. A leader in enterprise analytics for

More information

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

Operational Intelligence: Real-Time Business Analytics for Big Data Philip Russom

Operational Intelligence: Real-Time Business Analytics for Big Data Philip Russom Operational Intelligence: Real-Time Business Analytics for Big Data Philip Russom TDWI Research Director for Data Management August 14, 2012 Sponsor Speakers Philip Russom Research Director, Data Management,

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

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

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

Endeca Introduction to Big Data Analytics

Endeca Introduction to Big Data Analytics Endeca Introduction to Big Data Analytics Overview May 8, 2013 1 Agenda Introduction Overview Analytics for Big Data Overview Endeca Information Discovery Q & A 2 Introduction Business vs. IT Big Data

More information

Are You Ready for Big Data?

Are You Ready for Big Data? Are You Ready for Big Data? Jim Gallo National Director, Business Analytics April 10, 2013 Agenda What is Big Data? How do you leverage Big Data in your company? How do you prepare for a Big Data initiative?

More information

The Future of Data Management

The Future of Data Management The Future of Data Management with Hadoop and the Enterprise Data Hub Amr Awadallah (@awadallah) Cofounder and CTO Cloudera Snapshot Founded 2008, by former employees of Employees Today ~ 800 World Class

More information

www.pwc.com/oracle Next presentation starting soon Business Analytics using Big Data to gain competitive advantage

www.pwc.com/oracle Next presentation starting soon Business Analytics using Big Data to gain competitive advantage www.pwc.com/oracle Next presentation starting soon Business Analytics using Big Data to gain competitive advantage If every image made and every word written from the earliest stirring of civilization

More information

Extend your analytic capabilities with SAP Predictive Analysis

Extend your analytic capabilities with SAP Predictive Analysis September 9 11, 2013 Anaheim, California Extend your analytic capabilities with SAP Predictive Analysis Charles Gadalla Learning Points Advanced analytics strategy at SAP Simplifying predictive analytics

More information

Are You Ready for Big Data?

Are You Ready for Big Data? Are You Ready for Big Data? Jim Gallo National Director, Business Analytics February 11, 2013 Agenda What is Big Data? How do you leverage Big Data in your company? How do you prepare for a Big Data initiative?

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

This Symposium brought to you by www.ttcus.com

This Symposium brought to you by www.ttcus.com This Symposium brought to you by www.ttcus.com Linkedin/Group: Technology Training Corporation @Techtrain Technology Training Corporation www.ttcus.com Big Data Analytics as a Service (BDAaaS) Big Data

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

IBM AND NEXT GENERATION ARCHITECTURE FOR BIG DATA & ANALYTICS!

IBM AND NEXT GENERATION ARCHITECTURE FOR BIG DATA & ANALYTICS! The Bloor Group IBM AND NEXT GENERATION ARCHITECTURE FOR BIG DATA & ANALYTICS VENDOR PROFILE The IBM Big Data Landscape IBM can legitimately claim to have been involved in Big Data and to have a much broader

More information

Big data and corrections: what s the big issue? Corrections Technology Association June 4, 2013

Big data and corrections: what s the big issue? Corrections Technology Association June 4, 2013 Big data and corrections: what s the big issue? Corrections Technology Association June 4, 2013 1 Big is a relative term What is considered "big data" varies depending on the capabilities of the organization

More information

Il mondo dei DB Cambia : Tecnologie e opportunita`

Il mondo dei DB Cambia : Tecnologie e opportunita` Il mondo dei DB Cambia : Tecnologie e opportunita` Giorgio Raico Pre-Sales Consultant Hewlett-Packard Italiana 2011 Hewlett-Packard Development Company, L.P. The information contained herein is subject

More information

Master big data to optimize the oil and gas lifecycle

Master big data to optimize the oil and gas lifecycle Viewpoint paper Master big data to optimize the oil and gas lifecycle Information management and analytics (IM&A) helps move decisions from reactive to predictive Table of contents 4 Getting a handle on

More information

Alexander Nikov. 5. Database Systems and Managing Data Resources. Learning Objectives. RR Donnelley Tries to Master Its Data

Alexander Nikov. 5. Database Systems and Managing Data Resources. Learning Objectives. RR Donnelley Tries to Master Its Data INFO 1500 Introduction to IT Fundamentals 5. Database Systems and Managing Data Resources Learning Objectives 1. Describe how the problems of managing data resources in a traditional file environment are

More information

An Integrated Big Data & Analytics Infrastructure June 14, 2012 Robert Stackowiak, VP Oracle ESG Data Systems Architecture

An Integrated Big Data & Analytics Infrastructure June 14, 2012 Robert Stackowiak, VP Oracle ESG Data Systems Architecture An Integrated Big Data & Analytics Infrastructure June 14, 2012 Robert Stackowiak, VP ESG Data Systems Architecture Big Data & Analytics as a Service Components Unstructured Data / Sparse Data of Value

More information

The BIg Picture. Dinsdag 17 september 2013

The BIg Picture. Dinsdag 17 september 2013 The BIg Picture Dinsdag 17 september 2013 2 Agenda A short historical overview on BI Current Issues Current trends Future architecture First steps to this architecture 3 MIS/EIS Data Warehouse BI Multidimensional

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

A New Era Of Analytic

A New Era Of Analytic Penang egovernment Seminar 2014 A New Era Of Analytic Megat Anuar Idris Head, Project Delivery, Business Analytics & Big Data Agenda Overview of Big Data Case Studies on Big Data Big Data Technology Readiness

More information

BIG DATA ANALYTICS REFERENCE ARCHITECTURES AND CASE STUDIES

BIG DATA ANALYTICS REFERENCE ARCHITECTURES AND CASE STUDIES BIG DATA ANALYTICS REFERENCE ARCHITECTURES AND CASE STUDIES Relational vs. Non-Relational Architecture Relational Non-Relational Rational Predictable Traditional Agile Flexible Modern 2 Agenda Big Data

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

What do Big Data & HAVEn mean? Robert Lejnert HP Autonomy

What do Big Data & HAVEn mean? Robert Lejnert HP Autonomy What do Big Data & HAVEn mean? Robert Lejnert HP Autonomy Much higher Volumes. Processed with more Velocity. With much more Variety. Is Big Data so big? Big Data Smart Data Project HAVEn: Adaptive Intelligence

More information

BIG DATA: FROM HYPE TO REALITY. Leandro Ruiz Presales Partner for C&LA Teradata

BIG DATA: FROM HYPE TO REALITY. Leandro Ruiz Presales Partner for C&LA Teradata BIG DATA: FROM HYPE TO REALITY Leandro Ruiz Presales Partner for C&LA Teradata Evolution in The Use of Information Action s ACTIVATING MAKE it happen! Insights OPERATIONALIZING WHAT IS happening now? PREDICTING

More information

A Tour of the Zoo the Hadoop Ecosystem Prafulla Wani

A Tour of the Zoo the Hadoop Ecosystem Prafulla Wani A Tour of the Zoo the Hadoop Ecosystem Prafulla Wani Technical Architect - Big Data Syntel Agenda Welcome to the Zoo! Evolution Timeline Traditional BI/DW Architecture Where Hadoop Fits In 2 Welcome to

More information

Architecting your Business for Big Data Your Bridge to a Modern Information Architecture

Architecting your Business for Big Data Your Bridge to a Modern Information Architecture Architecting your Business for Big Data Your Bridge to a Modern Information Architecture Robert Stackowiak Vice President, Information Architecture & Big Data Oracle Safe Harbor Statement The following

More information

BEYOND BI: Big Data Analytic Use Cases

BEYOND BI: Big Data Analytic Use Cases BEYOND BI: Big Data Analytic Use Cases Big Data Analytics Use Cases This white paper discusses the types and characteristics of big data analytics use cases, how they differ from traditional business intelligence

More information

Peninsula Strategy. Creating Strategy and Implementing Change

Peninsula Strategy. Creating Strategy and Implementing Change Peninsula Strategy Creating Strategy and Implementing Change PS - Synopsis Professional Services firm Industries include Financial Services, High Technology, Healthcare & Security Headquartered in San

More information

SAP Predictive Analytics

SAP Predictive Analytics SAP Predictive Analytics What s the best that COULD happen? Bringing predictive analytics to the end user SAP Forum Belgium September 9, 2015 Waldemar Adams @adamsw SVP & GM Analytics SAP Europe, Middle-East

More information

Real-Time Big Data Analytics + Internet of Things (IoT) = Value Creation

Real-Time Big Data Analytics + Internet of Things (IoT) = Value Creation Real-Time Big Data Analytics + Internet of Things (IoT) = Value Creation January 2015 Market Insights Report Executive Summary According to a recent customer survey by Vitria, executives across the consumer,

More information

Business Intelligence. Advanced visualization. Reporting & dashboards. Mobile BI. Packaged BI

Business Intelligence. Advanced visualization. Reporting & dashboards. Mobile BI. Packaged BI Data & Analytics 1 Data & Analytics Solutions - Overview Information Management Business Intelligence Advanced Analytics Data governance Data modeling & architecture Master data management Enterprise data

More information

Chapter 6 8/12/2015. Foundations of Business Intelligence: Databases and Information Management. Problem:

Chapter 6 8/12/2015. Foundations of Business Intelligence: Databases and Information Management. Problem: Foundations of Business Intelligence: Databases and Information Management VIDEO CASES Chapter 6 Case 1a: City of Dubuque Uses Cloud Computing and Sensors to Build a Smarter, Sustainable City Case 1b:

More information

Industry Impact of Big Data in the Cloud: An IBM Perspective

Industry Impact of Big Data in the Cloud: An IBM Perspective Industry Impact of Big Data in the Cloud: An IBM Perspective Inhi Cho Suh IBM Software Group, Information Management Vice President, Product Management and Strategy email: inhicho@us.ibm.com twitter: @inhicho

More information

Safe Harbor Statement

Safe Harbor Statement Defining a Roadmap to Big Data Success Robert Stackowiak, Oracle Vice President, Big Data 17 November 2015 Safe Harbor Statement The following is intended to outline our general product direction. It is

More information

Big Data and Advanced Analytics Applications and Capabilities Steven Hagan, Vice President, Server Technologies

Big Data and Advanced Analytics Applications and Capabilities Steven Hagan, Vice President, Server Technologies Big Data and Advanced Analytics Applications and Capabilities Steven Hagan, Vice President, Server Technologies 1 Copyright 2011, Oracle and/or its affiliates. All rights Big Data, Advanced Analytics:

More information

Big Data: Are You Ready? Kevin Lancaster

Big Data: Are You Ready? Kevin Lancaster Big Data: Are You Ready? Kevin Lancaster Director, Engineered Systems Oracle Europe, Middle East & Africa 1 A Data Explosion... Traditional Data Sources Billing engines Custom developed New, Non-Traditional

More information

Big Data in the Nordics 2012

Big Data in the Nordics 2012 Big Data in the Nordics 2012 A survey about increasing data volumes and Big Data analysis among private and governmental organizations in Sweden, Norway, Denmark and Finland. Unexplored Big Data Potential

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

Chapter 6. Foundations of Business Intelligence: Databases and Information Management

Chapter 6. Foundations of Business Intelligence: Databases and Information Management Chapter 6 Foundations of Business Intelligence: Databases and Information Management VIDEO CASES Case 1a: City of Dubuque Uses Cloud Computing and Sensors to Build a Smarter, Sustainable City Case 1b:

More information

Evolution to Revolution: Big Data 2.0

Evolution to Revolution: Big Data 2.0 Evolution to Revolution: Big Data 2.0 An ENTERPRISE MANAGEMENT ASSOCIATES (EMA ) White Paper Prepared for Actian March 2014 IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Table of Contents

More information

HP Converged Systems Update. Tom Joyce Senior Vice President, Converged Systems August, 2013

HP Converged Systems Update. Tom Joyce Senior Vice President, Converged Systems August, 2013 HP Converged Systems Update Tom Joyce Senior Vice President, Converged Systems August, 2013 1 Copyright Copyright 2013 2013 Hewlett-Packard Development Development Company, Company, L.P. The L.P. information

More information

Big Data Maturity - The Photo and The Movie

Big Data Maturity - The Photo and The Movie Big Data Maturity - The Photo and The Movie Mike Ferguson Managing Director, Intelligent Business Strategies BA4ALL Big Data & Analytics Insight Conference Stockholm, May 2015 About Mike Ferguson Mike

More information

Datenverwaltung im Wandel - Building an Enterprise Data Hub with

Datenverwaltung im Wandel - Building an Enterprise Data Hub with Datenverwaltung im Wandel - Building an Enterprise Data Hub with Cloudera Bernard Doering Regional Director, Central EMEA, Cloudera Cloudera Your Hadoop Experts Founded 2008, by former employees of Employees

More information

Big Data and the new trends for BI and Analytics Juha Teljo Business Intelligence and Predictive Solutions Executive IBM Europe

Big Data and the new trends for BI and Analytics Juha Teljo Business Intelligence and Predictive Solutions Executive IBM Europe Big Data and the new trends for BI and Analytics Juha Teljo Business Intelligence and Predictive Solutions Executive IBM Europe 2012 IBM Corporation The Mega Trends Cloud Mobile Social Analytics 2014 International

More information

BIG DATA What it is and how to use?

BIG DATA What it is and how to use? BIG DATA What it is and how to use? Lauri Ilison, PhD Data Scientist 21.11.2014 Big Data definition? There is no clear definition for BIG DATA BIG DATA is more of a concept than precise term 1 21.11.14

More information

Big Data (Adv. Analytics) in 15 Mins. Peter LePine Managing Director Sales Support IM & BI Practice

Big Data (Adv. Analytics) in 15 Mins. Peter LePine Managing Director Sales Support IM & BI Practice Big Data (Adv. Analytics) in 15 Mins. Peter LePine Managing Director Sales Support IM & BI Practice Agenda Big Data in 15 Mins. Goal: Provide a basic understanding of; What is Big Data; Why it s important

More information

Key Considerations for a Successful Deployment of Real Time Analytics July 23, 2014

Key Considerations for a Successful Deployment of Real Time Analytics July 23, 2014 Key Considerations for a Successful Deployment of Real Time Analytics July 23, 2014 Brought to you by Vivit Big Data Special Interest Group led by Kate Fontanella, Pramod Singh, Akshar Dave, Abdul B. Rafi,

More information

High Performance Data Management Use of Standards in Commercial Product Development

High Performance Data Management Use of Standards in Commercial Product Development v2 High Performance Data Management Use of Standards in Commercial Product Development Jay Hollingsworth: Director Oil & Gas Business Unit Standards Leadership Council Forum 28 June 2012 1 The following

More information

Aligning Your Strategic Initiatives with a Realistic Big Data Analytics Roadmap

Aligning Your Strategic Initiatives with a Realistic Big Data Analytics Roadmap Aligning Your Strategic Initiatives with a Realistic Big Data Analytics Roadmap 3 key strategic advantages, and a realistic roadmap for what you really need, and when 2012, Cognizant Topics to be discussed

More information

Bringing Big Data into the Enterprise

Bringing Big Data into the Enterprise Bringing Big Data into the Enterprise Overview When evaluating Big Data applications in enterprise computing, one often-asked question is how does Big Data compare to the Enterprise Data Warehouse (EDW)?

More information

Self-Service Big Data Analytics for Line of Business

Self-Service Big Data Analytics for Line of Business I D C A N A L Y S T C O N N E C T I O N Dan Vesset Program Vice President, Business Analytics and Big Data Self-Service Big Data Analytics for Line of Business March 2015 Big data, in all its forms, is

More information

The Enterprise Data Hub and The Modern Information Architecture

The Enterprise Data Hub and The Modern Information Architecture The Enterprise Data Hub and The Modern Information Architecture Dr. Amr Awadallah CTO & Co-Founder, Cloudera Twitter: @awadallah 1 2013 Cloudera, Inc. All rights reserved. Cloudera Overview The Leader

More information

Big Data and Analytics in Government

Big Data and Analytics in Government Big Data and Analytics in Government Nov 29, 2012 Mark Johnson Director, Engineered Systems Program 2 Agenda What Big Data Is Government Big Data Use Cases Building a Complete Information Solution Conclusion

More information

Predictive Analytics. Noam Zeigerson, CTO

Predictive Analytics. Noam Zeigerson, CTO Predictive Analytics Noam Zeigerson, CTO Agenda The Predictive Analytics Need Innovative Technologies Business Solutions The problem: Inconsistent stream of revenue Available Data Sources ERP data Web

More information

Turn your information into a competitive advantage

Turn your information into a competitive advantage INDLÆG 03 Data Driven Business Value Turn your information into a competitive advantage Jonas Linders 04.10.2015 (dato) CGI Group Inc. 2015 Jonas Linders Education Role Industries M.Sc Informatics Experience

More information

The Future of Data Management with Hadoop and the Enterprise Data Hub

The Future of Data Management with Hadoop and the Enterprise Data Hub The Future of Data Management with Hadoop and the Enterprise Data Hub Amr Awadallah Cofounder & CTO, Cloudera, Inc. Twitter: @awadallah 1 2 Cloudera Snapshot Founded 2008, by former employees of Employees

More information

Addressing Open Source Big Data, Hadoop, and MapReduce limitations

Addressing Open Source Big Data, Hadoop, and MapReduce limitations Addressing Open Source Big Data, Hadoop, and MapReduce limitations 1 Agenda What is Big Data / Hadoop? Limitations of the existing hadoop distributions Going enterprise with Hadoop 2 How Big are Data?

More information

Architecture & Experience

Architecture & Experience Architecture & Experience Data Mining - Combination from SAP HANA, R & Hadoop Markus Severin, Solution Principal Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein

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

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

End to End Solution to Accelerate Data Warehouse Optimization. Franco Flore Alliance Sales Director - APJ

End to End Solution to Accelerate Data Warehouse Optimization. Franco Flore Alliance Sales Director - APJ End to End Solution to Accelerate Data Warehouse Optimization Franco Flore Alliance Sales Director - APJ Big Data Is Driving Key Business Initiatives Increase profitability, innovation, customer satisfaction,

More information

Big Data Zurich, November 23. September 2011

Big Data Zurich, November 23. September 2011 Institute of Technology Management Big Data Projektskizze «Competence Center Automotive Intelligence» Zurich, November 11th 23. September 2011 Felix Wortmann Assistant Professor Technology Management,

More information

How to Leverage Big Data in the Cloud to Gain Competitive Advantage

How to Leverage Big Data in the Cloud to Gain Competitive Advantage How to Leverage Big Data in the Cloud to Gain Competitive Advantage James Kobielus, IBM Big Data Evangelist Editor-in-Chief, IBM Data Magazine Senior Program Director, Product Marketing, Big Data Analytics

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

Exploiting Data at Rest and Data in Motion with a Big Data Platform

Exploiting Data at Rest and Data in Motion with a Big Data Platform Exploiting Data at Rest and Data in Motion with a Big Data Platform Sarah Brader, sarah_brader@uk.ibm.com What is Big Data? Where does it come from? 12+ TBs of tweet data every day 30 billion RFID tags

More information

Foundations of Business Intelligence: Databases and Information Management

Foundations of Business Intelligence: Databases and Information Management Foundations of Business Intelligence: Databases and Information Management Wienand Omta Fabiano Dalpiaz 1 drs. ing. Wienand Omta Learning Objectives Describe how the problems of managing data resources

More information

The Future of Business Analytics is Now! 2013 IBM Corporation

The Future of Business Analytics is Now! 2013 IBM Corporation The Future of Business Analytics is Now! 1 The pressures on organizations are at a point where analytics has evolved from a business initiative to a BUSINESS IMPERATIVE More organization are using analytics

More information

TECHNOLOGY TRANSFER PRESENTS MIKE FERGUSON BIG DATA MULTI-PLATFORM JUNE 25-27, 2014 RESIDENZA DI RIPETTA - VIA DI RIPETTA, 231 ROME (ITALY)

TECHNOLOGY TRANSFER PRESENTS MIKE FERGUSON BIG DATA MULTI-PLATFORM JUNE 25-27, 2014 RESIDENZA DI RIPETTA - VIA DI RIPETTA, 231 ROME (ITALY) TECHNOLOGY TRANSFER PRESENTS MIKE FERGUSON BIG DATA MULTI-PLATFORM ANALYTICS JUNE 25-27, 2014 RESIDENZA DI RIPETTA - VIA DI RIPETTA, 231 ROME (ITALY) info@technologytransfer.it www.technologytransfer.it

More information

Big Analytics: A Next Generation Roadmap

Big Analytics: A Next Generation Roadmap Big Analytics: A Next Generation Roadmap Cloud Developers Summit & Expo: October 1, 2014 Neil Fox, CTO: SoftServe, Inc. 2014 SoftServe, Inc. Remember Life Before The Web? 1994 Even Revolutions Take Time

More information

Big Data Analytics- Innovations at the Edge

Big Data Analytics- Innovations at the Edge Big Data Analytics- Innovations at the Edge Brian Reed Chief Technologist Healthcare Four Dimensions of Big Data 2 The changing Big Data landscape Annual Growth ~100% Machine Data 90% of Information Human

More information

With the Emergence of Big Data, Where do Relational Technologies Fit?

With the Emergence of Big Data, Where do Relational Technologies Fit? With the Emergence of Big Data, Where do Relational Technologies Fit? Donna Burbank VP Product Marketing CA Technologies DAMA Chicago June 2013 Who am I? More than more than 15 years of experience in the

More information

Native Connectivity to Big Data Sources in MSTR 10

Native Connectivity to Big Data Sources in MSTR 10 Native Connectivity to Big Data Sources in MSTR 10 Bring All Relevant Data to Decision Makers Support for More Big Data Sources Optimized Access to Your Entire Big Data Ecosystem as If It Were a Single

More information

Data Virtualization A Potential Antidote for Big Data Growing Pains

Data Virtualization A Potential Antidote for Big Data Growing Pains perspective Data Virtualization A Potential Antidote for Big Data Growing Pains Atul Shrivastava Abstract Enterprises are already facing challenges around data consolidation, heterogeneity, quality, and

More information

Introduction to Business Intelligence

Introduction to Business Intelligence IBM Software Group Introduction to Business Intelligence Vince Leat ASEAN SW Group 2007 IBM Corporation Discussion IBM Software Group What is Business Intelligence BI Vision Evolution Business Intelligence

More information

Executive Summary... 2 Introduction... 3. Defining Big Data... 3. The Importance of Big Data... 4 Building a Big Data Platform...

Executive Summary... 2 Introduction... 3. Defining Big Data... 3. The Importance of Big Data... 4 Building a Big Data Platform... Executive Summary... 2 Introduction... 3 Defining Big Data... 3 The Importance of Big Data... 4 Building a Big Data Platform... 5 Infrastructure Requirements... 5 Solution Spectrum... 6 Oracle s Big Data

More information

AGENDA. What is BIG DATA? What is Hadoop? Why Microsoft? The Microsoft BIG DATA story. Our BIG DATA Roadmap. Hadoop PDW

AGENDA. What is BIG DATA? What is Hadoop? Why Microsoft? The Microsoft BIG DATA story. Our BIG DATA Roadmap. Hadoop PDW AGENDA What is BIG DATA? What is Hadoop? Why Microsoft? The Microsoft BIG DATA story Hadoop PDW Our BIG DATA Roadmap BIG DATA? Volume 59% growth in annual WW information 1.2M Zetabytes (10 21 bytes) this

More information

The 3 questions to ask yourself about BIG DATA

The 3 questions to ask yourself about BIG DATA The 3 questions to ask yourself about BIG DATA Do you have a big data problem? Companies looking to tackle big data problems are embarking on a journey that is full of hype, buzz, confusion, and misinformation.

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

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

The 4 Pillars of Technosoft s Big Data Practice

The 4 Pillars of Technosoft s Big Data Practice beyond possible Big Use End-user applications Big Analytics Visualisation tools Big Analytical tools Big management systems The 4 Pillars of Technosoft s Big Practice Overview Businesses have long managed

More information

Using Predictions to Power the Business. Wayne Eckerson Director of Research and Services, TDWI February 18, 2009

Using Predictions to Power the Business. Wayne Eckerson Director of Research and Services, TDWI February 18, 2009 Using Predictions to Power the Business Wayne Eckerson Director of Research and Services, TDWI February 18, 2009 Sponsor 2 Speakers Wayne Eckerson Director, TDWI Research Caryn A. Bloom Data Mining Specialist,

More information

Big Data Analytics: Today's Gold Rush November 20, 2013

Big Data Analytics: Today's Gold Rush November 20, 2013 Copyright 2013 Vivit Worldwide Big Data Analytics: Today's Gold Rush November 20, 2013 Brought to you by Copyright 2013 Vivit Worldwide Hosted by Bernard Szymczak Vivit Leader Ohio Chapter TQA SIG Copyright

More information

1 Performance Moves to the Forefront for Data Warehouse Initiatives. 2 Real-Time Data Gets Real

1 Performance Moves to the Forefront for Data Warehouse Initiatives. 2 Real-Time Data Gets Real Top 10 Data Warehouse Trends for 2013 What are the most compelling trends in storage and data warehousing that motivate IT leaders to undertake new initiatives? Which ideas, solutions, and technologies

More information

INTELLIGENT BUSINESS STRATEGIES WHITE PAPER

INTELLIGENT BUSINESS STRATEGIES WHITE PAPER INTELLIGENT BUSINESS STRATEGIES WHITE PAPER Improving Access to Data for Successful Business Intelligence Part 1: Meeting Today s Business Requirements in an Increasingly Complex Environment By Mike Ferguson

More information

GAIN BETTER INSIGHT FROM BIG DATA USING JBOSS DATA VIRTUALIZATION

GAIN BETTER INSIGHT FROM BIG DATA USING JBOSS DATA VIRTUALIZATION GAIN BETTER INSIGHT FROM BIG DATA USING JBOSS DATA VIRTUALIZATION Syed Rasheed Solution Manager Red Hat Corp. Kenny Peeples Technical Manager Red Hat Corp. Kimberly Palko Product Manager Red Hat Corp.

More information

Deploying Big Data to the Cloud: Roadmap for Success

Deploying Big Data to the Cloud: Roadmap for Success Deploying Big Data to the Cloud: Roadmap for Success James Kobielus Chair, CSCC Big Data in the Cloud Working Group IBM Big Data Evangelist. IBM Data Magazine, Editor-in- Chief. IBM Senior Program Director,

More information

Big Data overview. Livio Ventura. SICS Software week, Sept 23-25 Cloud and Big Data Day

Big Data overview. Livio Ventura. SICS Software week, Sept 23-25 Cloud and Big Data Day Big Data overview SICS Software week, Sept 23-25 Cloud and Big Data Day Livio Ventura Big Data European Industry Leader for Telco, Energy and Utilities and Digital Media Agenda some data on Data Big Data

More information