1 MENA Summit 2013: Enabling innovation, driving profitability The big data business model: opportunity and key success factors 6 November 2013 Justin van der Lande EVENT PARTNERS:
2 2 Introduction What is big data analytics? What is the scope and size of the market? What systems are needed to support big data? What are the challenges today?
3 Historically, data storage and computation has been a challenge because of technological constraints 3 This was 1956 Doris Day was in the top UK charts with the hit Que Sera, Sera you would need an entire plane to carry the digitalised version of one of her albums and a lab with a dozen scientists just to copy it 5MB hard drive being forklifted into a plane but things have changed
4 Cost (USD million per petabyte) The big data business model: opportunity and key success factors Open-source tools and cloud-based data infrastructure continue to drive down the costs of analytics Storage area network (SAN) Network access server (NAS) The declining cost of storage Local storage Predictive analytics Inline analytics 0.5 Business analytics Cloud storage 0.0 Enterprise data warehouse Time Quick facts: It costs USD600 to buy a disk drive that can store all the world s music 1 billion pieces of content are shared daily on Facebook
5 Big data and analytics drive business value in three core areas Super-charge established operations by providing deeper insight into the current processes to make better decisions. Expand established revenue streams a sub-set of the above to provide more targeted, timely, context-aware offers to customers to encourage them to spend more. Create new revenue streams such as selling of customer insights, providing data analysis for M2M applications or offering targeted advertising. Create new revenue streams Business demands Data Analytics tools Expand established revenue streams Super-charge established operations 5
6 The large-scale utilisation of internal data is becoming a key competitive factor for all industries 6 Source: McKinsey Global Institute, Big Data: The next frontier for innovation, competition and productivity.
7 The telecoms industry is in the front line of the big data revenue generation in the short term 7 Key factors for the telecoms industry Quantity of customers Quantity of data Diversity of data Telecoms services have typically high penetration rates Telecoms operators have a large percentage of the population in their customer base Telecoms groups have access to worldwide customer database The average telecoms customer generates data entries on a daily basis Frequency will tend to increase as Internet services become widespread, potentially resulting in continuous generation of data Telecoms operators data includes different dimensions, including telecoms patterns, location, devices used, content accessed, online transactions, demographics and so on and the range is growing
8 8 Growing mobile Internet usage creates opportunities for operators to expand their data capturing capabilities New analytical dimensions The increasing use of mobile Internet brings new data sets to explore: Content: Being able to understand what types of website and application individual customers access gives operators a more detailed overview. Device: These were an analytical dimension before the rise of mobile Internet, but the type of device, the screen size and the utilisation of multiple devices have now become a more relevant. Location: Mobile data is permanently switched on, creating a continuous flow of information to the operator. Deeper levels of segmentation Traditionally, telecoms analytical segmentation used voice and SMS usage data and segmented customers using that data and the demographic information available. By offering insights into location, the content accessed and the devices used, mobile Internet analytics allows operators to perform a behavioural segmentation on an individual basis, getting a much better knowledge of individual needs. Operators can use the available data for internal purposes, or can package it as a product for third parties.
9 Operators are now using their internal data as a direct source of revenue 9 Big aggregates Segmented data Big data Description Operator does not capture and manipulate individual data Operator captures individual data for internal analysis Operator uses its data and customer data is a standalone product Data utilisation Data not captured Data is captured and aggregated at customer level Data is captured, aggregated at customer level and as B2B2C product Revenue Impact No impact Indirect impact through the improvement of decisions Direct impact on revenue by selling customer data
10 10 Introduction What is big data analytics? What is the scope and size of the market? What systems are needed to support big data? What are the challenges today?
11 Setting up the right analytical methodologies is more important than setting up the right IT infrastructure 11 What is big data analytics? Big data analytics is the business opportunity to enhance revenue and operational efficiency by exploiting the widespread availability of valuable data on individual customers across the organisation. What is not big data analytics Big data analytics is not only about volume of data, but about the value of that data Big data analytics is not about IT infrastructure implementation, but about the methodologies and analytical frameworks needed to take advantage of that infrastructure Establishing the analytical capabilities, frameworks and algorithms before investing in IT infrastructure is key to ensuring that operators can truly take advantage of big data and obtain a return on their IT investment.
12 Big data analytics is not only about the characteristics of the available data, but what operators can do with it The large volume of available data is a key characteristic of big data. The velocity, understood as the frequency at which data is generated. The high frequency of data capture brings new opportunities in terms of real-time and reporting. Velocity Volume The 5 Vs of big data analytics Visualisation Presenting the data in a meaningful and insightful manner, doing the correct slicing and dicing to achieve the defined objective. Variety Value 12 Data comes from established sources (CDR, billing data sessions) and interactive data social networks, location and in different formats (structured and unstructured). The value of data is the key component of big data analytics. Establishing the right methodologies and analytical frameworks to extract value from data is essential.
13 Big data analytics can be used both to improve internal decision making and as a source of revenue 13 How companies can gain from big data analytics Internal use External use Use big data to support internal decisions as well as improve operational efficiency and support commercial strategy. Internal decision making can be supported at different hierarchic levels, with each level requiring a different model and different analytical frameworks and tools. The data generated in a telco is part of the assets that it should exploit. By packaging the available data in a structured and visual way, access can be sold to third parties for example, handset vendors can benefit from information about usage trends among customers with different handsets.
14 14 Introduction What is big data analytics? What is the scope and size of the market? What systems are needed to support big data? What are the challenges today?
15 Big data analytics solutions are set for growth as CSP margins come under pressure and solution costs fall 15 Dutch mobile market declines by 4.5% to EUR5.8 billion in 2012 Orange s profit declines by 79% amid economic decline Increasing competitive pressures Decreasing margins and growth Growing demands from smarter customers Spain loses 5% of its mobile subscriber base in 2012: warning for Europe Forbes billion mobile phone users worldwide Analytics is set for growth Declining cost of computing Decreasing cost of storage Increasing data for modelling customers, operations and capital requirements AT&T s call data record database has 1.9 trillion rows of data Facebook works on more than 30PB of user-generated data
16 16 CSPs have applied analytics to a rich set of use cases across different aspects of their business Customer Supply and partner Financial and legal Marketing and campaign Network and operations Digital data services Churn Channel Revenue assurance Campaign Network asset optimisation Mobile advertising Customer care Partner Fraud Customer segmentation Dynamic policy Market data research Social media Golden path analysis Settlements liability Risk Offer margin analysis Offer creation and pricing Customer lifetime value analysis Revenue-driven network planning Fulfilment Identity verification services M2M monitoring services Dynamic offer Data retention and retrieval Net promoter Root-cause analysis Traffic flow services Cross-sell and upsell Social network analysis Performance Proximity promotions
17 Market maturity dictates the analytics solutions that CSPs need to deploy 17 New segments Developing segments Mature segments Crossselling models Base segmentation Churn models Fraud and bad debt Lifetime value Credit risk Campaign Sentiment analysis Network planning Social network analysis Insight services Context-based marketing Mobile advertising services
18 18 Scaling of market opportunities Market size Potential impact for CSPs Expanding established revenue streams USD1.9 trillion Telecoms worldwide revenue in 2012 First-mover advantage could provide a sustained market advantage, but it will become table stakes when competitors follow. Supercharging established processes USD1.71 trillion Telecoms worldwide IT spending in 2011 Annual spend can be impacted each and every subsequent year to exploit efficiencies. Creating new revenue streams USD500 billion Advertising and market research Non-telecoms revenue potential to compete with online, mobile and proximity advertising. Other opportunities can greatly increase potential.
19 19 Introduction What is big data analytics? What is the scope and size of the market? What systems are needed to support big data? What are the challenges today?
20 Value The big data business model: opportunity and key success factors Big data analytics is challenging established systems, and leading CSPs are investing in new infrastructure The market maturity of analytics tools 20 Business intelligence tools Predictive analytics tools In-line analytics tools Data view Data view Data view What will happen? What is happening now? Growing volume, variety and velocity of big data What happened?
21 21 Key components in an analytics framework BPM APIs Applications Tool users Front-line workers Presentation or visualisation Portals Mash-ups Business intelligence and analytics tools Optimisation Data access Data infrastructure (can be part of EDW) Database Analysable data Server Storage Operational systems Customers or partners Executives Data scientists Business analysts Engineers Dashboards Reports Departmental data marts APIs Predictive modelling Forecasting Statistical analysis Audio and video ELT or ETL tools Source data Images Docs Text Billing CRM Network ERP Web and social media
22 Vendors are continuing to expand into the telecoms analytics market from different perspectives Types of analytics vendor 22 Analytics and business intelligence tool vendors, which apply generic tools to the telecoms business environment through configuration and deployment of specific templates and models Storage tools vendors, which have expanded from providing data storage solutions to include analytics Analytics software solutions Customised solutions based on open-source software and generic hardware Specialist software vendors, which have developed products they have used to support specific business applications, such as revenue assurance, service assurance, mediation and customer care
23 23 Introduction What is big data analytics? What is the scope and size of the market? What systems are needed to support big data? What are today s challenges?
24 24 CSPs are struggling with three broad issues today Prioritisation of use case development Systems selection and implementation architecture Big data challenges Organisation structure to adopt
25 25 CSPs are struggling with three broad issues today Prioritisation of use case development Systems selection and implementation architecture Big data challenges Organisation structure to adopt
26 Big data analytics is creating lots of complex choices for IT and business managers 26 Presentation or visualisation Tool selection, legacy integration Business intelligence and analytics tools Batch Data processing Language SQL, NoSQL, open source, vendor-specific Real-time data processing Real-time query engines NoSQL database selection MPP database selection Database resourcing Hadoop which distribution, other vendor options Big data appliances General purpose, vendor specific, re-use of the EDW and utilisation of cloud resources
27 that need to be developed to support different use cases 27 Platform Resource Data transformation Real-time analytics Online transaction processing RDBMS Large structured data Cloudera MAP/R Horntonetworks Hadoop Hive Data storage and indexing for analytics applications 100T data with multi-structured with minute or more latency Cloudera MAP/R Horntonetworks Cloudera MAP/R Horntonetworks Offer creation and pricing Hive Pig Java MapReduce 100T data with multi-structured with minute or less latency MPP DB EDW
28 Supporting specific use cases can be efficiently achieved through the use of tightly integrated applications Supporting a use case requires the data sources to be incorporated into specific data marts and the development of a data model, which is then visualised through the presentation tools and incorporated into a process via business process tools, if required. Analytics applications provide these components pre-configured, which enables fast and low-risk deployment. However, in order for these analytics applications to work in this way a prerequisite platform is need. Most use cases are not supported by analytics applications, but are supported through a combination of configuration, data adapters, integration and professional services support by vendors. Analytics application 28 Data source adapters Data source Data models Analytics models Presentation Automation Common data infrastructure platform
29 29 CSPs are struggling with three broad issues today Prioritisation of use case development Systems selection and implementation architecture Big data challenges Organisation structure to adopt
30 To understand the organisational changes required, you need to define the value chain you want to operate in 30 Organisational changes required + Data pipe Data aggregation Data analytics Product packaging Market analysis Operator keeps business as usual, allowing third parties to extract data directly from IT or network systems Operator captures and aggregates the data from its own systems into a big data datamart, providing anonymised data to third parties Operator captures, aggregates and segments customer data Operator uses segmentation algorithms and packages the data for different types of client, based on their needs Operators offer turnkey customisable solutions based on client needs, without ever providing customer data to third parties
31 Ultimately, as a significant source of revenue, big data should become a department on its own 31 Big data practice Data warehousing Responsible for creating and maintain the ETL processes required to extract data from systems Responsible for ensuring data capture requirements are in place from a network perspective Product development Team responsible for analysing aggregated data enterprise-wide Responsible for product design and algorithm generation for product creation Business development Responsible for identifying and contacting potential clients Co-operation in the product packaging based on the knowledge of clients needs
32 32 CSPs are struggling with three broad issues today Prioritisation of use case development Systems selection and implementation architecture Big data challenges Organisation structure to adopt
33 The selection and prioritisation of different projects on which to implement a new BDA strategy is complex 33 New segments Developing segments Mature segments Crossselling models Base segmentation Churn models Fraud and bad debt Lifetime value Credit risk Campaign Sentiment analysis Network planning Social network analysis Insight services Context-based marketing Mobile advertising services
34 34 Thank you for your attention. Any questions? Justin van der Lande Analyst
35 Our unique skillset ensures a holistic understanding of the big data phenomenon 35 Big data Operational + Regulatory + Software research Operational analytics implementation RFP support Operational analytics strategy Centralisation of analytics functions Customer lifetime value Regulatory support and advice New generation big data strategies RFP support Technical strategy and implementation
36 Second breakout streams choose one to attend pm 36 Mobile Internet trends in the Middle East Africa Region MEA Mobile Internet Survey Results: Karim Yaici and Ronan de Renesse Godolphin Ballroom (this main room) The impact of real-time network analytics on making faster, more informed business decisions: Justin van der Lande and Patrick Kelly Level 5, Room 2
Research Report Analytics framework: creating the data-centric organisation to optimise business performance October 2013 Justin van der Lande 2 Contents  Slide no. 5. Executive summary 6. Executive
RESEARCH STRATEGY REPORT CONTEXT-AWARE MARKETING SYSTEMS ENABLE CSPs TO GENERATE ADDITIONAL REVENUE ATUL ARORA AND JUSTIN VAN DER LANDE analysysmason.com About this report This report analyses communications
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,
mwd a d v i s o r s Turning Big Data into Big Insights Helena Schwenk A special report prepared for Actuate May 2013 This report is the fourth in a series and focuses principally on explaining what s needed
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
Master Management MDM and Warehousing Complement Each Other Greater business value from both 2011 IBM Corporation Executive Summary Master Management (MDM) and Warehousing (DW) complement each other There
Applied Business Intelligence Iakovos Motakis, Ph.D. Director, DW & Decision Support Systems Intrasoft SA Agenda Business Drivers and Perspectives Technology & Analytical Applications Trends Challenges
Survey Results Table of Contents Survey Results... 4 Big Data Company Strategy... 6 Big Data Business Drivers and Benefits Received... 8 Big Data Integration... 10 Big Data Implementation Challenges...
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
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
INTELLIGENT BUSINESS STRATEGIES WHITE PAPER Improving Access to Data for Successful Business Intelligence Part 2: Supporting Multiple Analytical Workloads in a Changing Analytical Landscape By Mike Ferguson
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
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
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
Why is BIG Data Important? March 2012 1 Why is BIG Data Important? A Navint Partners White Paper May 2012 Why is BIG Data Important? March 2012 2 What is Big Data? Big data is a term that refers to data
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 email@example.com
Research Forecast Report Analytics software solutions: worldwide forecast 2014 2018 September 2014 Justin van der Lande and Atul Arora 2 Contents Slide no. 5. Executive summary 6. Capsule summary: analytics
Cloudera Enterprise Data Hub in Telecom: Three Customer Case Studies Version: 103 Table of Contents Introduction 3 Cloudera Enterprise Data Hub for Telcos 4 Cloudera Enterprise Data Hub in Telecom: Customer
Managing Big Data with Hadoop & Vertica A look at integration between the Cloudera distribution for Hadoop and the Vertica Analytic Database Copyright Vertica Systems, Inc. October 2009 Cloudera and Vertica
QLIKVIEW DEPLOYMENT FOR BIG DATA ANALYTICS AT KING.COM QlikView Technical Case Study Series Big Data June 2012 qlikview.com Introduction This QlikView technical case study focuses on the QlikView deployment
Some Economics of Cultural PSI: the Micro Perspective Massimiliano Nuccio Research Affiliate ASK Bocconi Research Centre Bocconi University Milan - 10 October 2014 1 Agenda Which new sources of data can
Forecast of Big Data Trends Assoc. Prof. Dr. Thanachart Numnonda Executive Director IMC Institute 3 September 2014 Big Data transforms Business 2 Data created every minute Source http://mashable.com/2012/06/22/data-created-every-minute/
III Big Data Technologies Today, new technologies make it possible to realize value from Big Data. Big data technologies can replace highly customized, expensive legacy systems with a standard solution
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
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
ACS 2015 Annual Canberra Conference Are You Big Data Ready? Vladimir Videnovic Business Solutions Director Oracle Big Data and Analytics Introduction Introduction What is Big Data? If you can't explain
UNIFY YOUR (BIG) DATA ANALYTIC STRATEGY GIVE ANY USER ANY ANALYTIC ON ANY DATA Scott Gnau President, Teradata Labs firstname.lastname@example.org t Unify Your (Big) Data Analytic Strategy Technology excitement:
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
2015 Analyst and Advisor Summit Advanced Data Analytics Dr. Rod Fontecilla Vice President, Application Services, Chief Data Scientist Agenda Key Facts Offerings and Capabilities Case Studies When to Engage
1. Introduction Outline BI and Enterprise-wide decisions BI in different Business Areas BI Strategy, Architecture, and Perspectives 2 Case study: Netflix and House of Cards Source: Andrew Stephen 3 Case
Data Virtualization for Agile Business Intelligence Systems and Virtual MDM To View This Presentation as a Video Click Here Agenda Data Virtualization New Capabilities New Challenges in Data Integration
Harnessing big data with Hortonworks Data Platform and Red Hat JBoss Data Virtualization Kimberly Palko, Product Manager Red Hat JBoss Doug Reid, Director Partner Product Management Hortonworks Cojan van
HDP Enabling the Modern Data Architecture Herb Cunitz President, Hortonworks Page 1 Hortonworks enables adoption of Apache Hadoop through HDP (Hortonworks Data Platform) Founded in 2011 Original 24 architects,
MAKING BIG DATA COME ALIVE Big Data Architecture & Analytics A comprehensive approach to harness big data architecture and analytics for growth Steve Gonzales, Principal Manager email@example.com
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
5 Big Data Use Cases to Understand Your Customer Journey CUSTOMER ANALYTICS EBOOK CUSTOMER JOURNEY Technology is radically transforming the customer journey. Today s customers are more empowered and connected
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
The need for data integration tools exists in every company, small to large. Whether it is extracting data that exists in spreadsheets, packaged applications, databases, sensor networks or social media
Insightful Analytics: Leveraging the data explosion for business optimisation 09 Top Ten Challenges for Investment Banks 2015 Insightful Analytics: Leveraging the data explosion for business optimisation
Next-Generation Cloud Analytics with Amazon Redshift What s inside Introduction Why Amazon Redshift is Great for Analytics Cloud Data Warehousing Strategies for Relational Databases Analyzing Fast, Transactional
Architected Blended Big Data with Pentaho A Solution Brief Copyright 2013 Pentaho Corporation. Redistribution permitted. All trademarks are the property of their respective owners. For the latest information,
Challenges of Handling Big Data Ramesh Bhashyam Teradata Fellow Teradata Corporation firstname.lastname@example.org Trend Too much information is a storage issue, certainly, but too much information is also
International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 7 (2013), pp. 655-662 International Research Publications House http://www. irphouse.com /ijict.htm Data
Big Data Putting data to productive use Fast Forward What is big data, and why should you care? Get familiar with big data terminology, technologies, and techniques. Getting started with big data to realize
Create and Drive Big Data Success Don t Get Left Behind The performance boost from MapR not only means we have lower hardware requirements, but also enables us to deliver faster analytics for our users.
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
Turning Big into More Effective Experiences Experience the Difference with Lily Enterprise Table of Contents Confidentiality Purpose of this Document The Conceptual Solution About NGDATA The Solution The
Research Report The Connected Consumer Survey 2015: OTT communication services April 2015 Patrick Rusby and Stephen Sale 2 About this report This report focuses on aspects of Analysys Mason s annual Connected
The Principles of the Business Data Lake The Business Data Lake Culture eats Strategy for Breakfast, so said Peter Drucker, elegantly making the point that the hardest thing to change in any organization
Wikibon.com - http://wikibon.com Wikibon Big Data Analytics Adoption Survey, 2014-2015 Frequency Analysis by Jeff Kelly - 1 July 2014 http://wikibon.com/wikibon-big-data-analytics-adoption-survey-2014-2015-frequency-analysis/
BIG DATA STRATEGY Rama Kattunga Chair at American institute of Big Data Professionals Building Big Data Strategy For Your Organization In this session What is Big Data? Prepare your organization Building
Hadoop and Data Warehouse Friends, Enemies or Profiteers? What about Real Time? Kai Wähner email@example.com @KaiWaehner www.kai-waehner.de Disclaimer! These opinions are my own and do not necessarily
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
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
Descriptive to Predictive to Prescriptive Analytics: Move Up the Value Chain Suren Nathan CTO What We Do Deliver cloud based predictive analytics solutions to the communications industry to help streamline
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
GINeVRA Digital Research Hub Customized Report- Big Data 1 2014. All Rights Reserved. Agenda Context Challenges and opportunities Solutions Market Case studies Recommendations 2 2014. All Rights Reserved.
TECHNOLOGY TRANSFER PRESENTS MIKE FERGUSON BIG DATA MULTI-PLATFORM ANALYTICS JUNE 25-27, 2014 RESIDENZA DI RIPETTA - VIA DI RIPETTA, 231 ROME (ITALY) firstname.lastname@example.org www.technologytransfer.it
HDP Hadoop From concept to deployment. Ankur Gupta Senior Solutions Engineer Rackspace: Page 41 27 th Jan 2015 Where are you in your Hadoop Journey? A. Researching our options B. Currently evaluating some
Hortonworks & SAS Analytics everywhere. Page 1 A change in focus. A shift in Advertising From mass branding A shift in Financial Services From Educated Investing A shift in Healthcare From mass treatment
Decisioning for Telecom Customer Intimacy Experian Telecom Analytics Turning disruption into opportunity The traditional telecom business model is being disrupted by a variety of pressures from heightened
Frameworx 10 Business Process Framework R8.0 Product Conformance Certification Report Microsoft Business Analytics Accelerator for Telecommunications Release 1.0 November 2011 TM Forum 2011 Table of Contents
INSIGHT Oracle's All- Out Assault on the Big Data Market: Offering Hadoop, R, Cubes, and Scalable IMDB in Familiar Packages Carl W. Olofson IDC OPINION Global Headquarters: 5 Speen Street Framingham, MA
ACCURACY DELIVERED ABOUT US WHO WE ARE BizAcuity is a fast growing Business intelligence strategy company, providing reliable, scalable and cost effective consultancy and services to clients across the
Decisioning for Telecom Customer Intimacy Experian Telecom Analytics Turning disruption into opportunity The traditional telecom business model is being disrupted by a variety of pressures. From heightened
A Big Data Storage Architecture for the Second Wave David Sunny Sundstrom Principle Product Director, Storage Oracle Growth in Data Diversity and Usage 1.8 Zettabytes of Data in 2011, 20x Growth by 2020
Big Data in Telecom value chain Presented by: Gurjot S Sandhu Director Sales Xalted Information Systems Pvt. Ltd. Analyzing Big Picture Big Data to CSPs Traditional Analytics vs Big Data Approach Traditional
The Challenge of Digital Analytics & Intelligence Giovanni Lorenzoni, CEO Seville, 22/09/2015 BitBang Overview Digital Analytics, Digital Intelligence, Customer Intelligence, Advanced Analytics, Optimization
Modernizing Your Data Warehouse for Hadoop Big data. Small data. All data. Audie Wright, DW & Big Data Specialist Audie.Wright@Microsoft.com O 425-538-0044, C 303-324-2860 Unlock Insights on Any Data Taking
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
An Oracle White Paper June 2013 Oracle: Big Data for the Enterprise Executive Summary... 2 Introduction... 3 Defining Big Data... 3 The Importance of Big Data... 4 Building a Big Data Platform... 5 Infrastructure
Big Data Analytics 1 Priority Discussion Topics What are the most compelling business drivers behind big data analytics? Do you have or expect to have data scientists on your staff, and what will be their
Making Sense of Big Data in Insurance Amir Halfon, CTO, Financial Services, MarkLogic Corporation BIG DATA?.. SLIDE: 2 The Evolution of Data Management For your application data! Application- and hardware-specific
Version 2.0 - October 2014 NetVision Solution Datasheet NetVision: Smart Energy Smart Grids and Smart Meters - Towards Smarter Energy Management According to analyst firm Berg Insight, the installed base
mwd a d v i s o r s Navigating the Big Data infrastructure layer Helena Schwenk A special report prepared for Actuate May 2013 This report is the second in a series of four and focuses principally on explaining
. Whitepaper Using Big Data to build value for operators May 2013 Justin van der Lande Sponsored by Using Big Data to build value for Operators i Contents 1 Executive summary 2 2 Recommendations for Service
Big Data Unlock the mystery and see what the future holds Philip Sow SE Manager, SEA THE ERA OF BIG DATA Big Data Market: Reach $32.1 Billion in 2015 & to $54.4 billion by 2017 The 3 + 1 Vs Structure/Semi/Unstructured
Infosys Labs Briefings VOL 11 NO 1 2013 Big Data: Testing Approach to Overcome Quality Challenges By Mahesh Gudipati, Shanthi Rao, Naju D. Mohan and Naveen Kumar Gajja Validate data quality by employing
Bringing Together ESB and Big Data Bringing Together ESB and Big Data Table of Contents Why ESB and Big Data?...3 Exploring the Promise of Big Data and ESB... 4 Moving Forward With ESB and Big Data...5
Practical Considerations for Real-Time Business Intelligence Donovan Schneider Yahoo! September 11, 2006 Outline Business Intelligence (BI) Background Real-Time Business Intelligence Examples Two Requirements
Processing and Analyzing Streams of CDRs in Real Time Streaming Analytics for CDRs 2 The V of Big Data Velocity means both how fast data is being produced and how fast the data must be processed to meet
Brochure More information from http://www.researchandmarkets.com/reports/2643647/ Big Data and Telecom Analytics Market: Business Case, Market Analysis & Forecasts 2014-2019 Description: Big Data refers
Ganzheitliches Datenmanagement für Hadoop Michael Kohs, Senior Sales Consultant @mikchaos The Problem with Big Data Projects in 2016 Relational, Mainframe Documents and Emails Data Modeler Data Scientist
Embracing the Cloud, Mobile, Social & Big Data Introducing ARIS 9.0 and webmethods 9.0 Dr. Wolfram Jost CTO Software AG 2010-2013 2 Positioning 3 2013 Software 2013 AG. Software All rights AG. reserved.
Oracle Big Data SQL Technical Update Jean-Pierre Dijcks Oracle Redwood City, CA, USA Keywords: Big Data, Hadoop, NoSQL Databases, Relational Databases, SQL, Security, Performance Introduction This technical
A TECHNICAL WHITE PAPER ATTUNITY VISIBILITY Analytics for Enterprise Data Warehouse Management and Optimization Executive Summary Successful enterprise data management is an important initiative for growing