For an Executive Summary of this report please contact ediz.ibrahim@visiongain.com (+44 (0)20 7549 9976) or refer to our website http://www.visiongain.com/Report/1175/Top-20-Big-Data-Companies-2014
Microsoft HDInsight as a Big Data and Interoperability Platform to Drive Poin...DataWorks Summit
Learn how a small team of 3-4 technology and subject matter experts developed an Azure HDInsight solution. The solution captures genomics data for solid tumors, summary data from a third party and various internal sources, and does genomic Clinical Trial matching. This was done strictly using the Azure cloud and interactions with cloud-based Office 365 SharePoint web applications utilizing only batch scripting, Hive, and Sqoop. HD Insights is the data munging layer and SharePoint is the user access layer.
The process was stood up in a 6-8 week period, while doing our day jobs. The business benefit is to enable providers, at the point of care, to suggest clinical trials for oncology patients based on genomic matches (Molecular Tumor Board). This has increased participation rates in clinical trials with the goal to improve the survival rates and quality of life for patients. The success of this project has spread to capturing local home grown registries in data silos to share with other like-minded providers within Levine Cancer Institute.
Data, BI and Advanced Analytics: where Microsoft Dynamics ends and Microsoft ...DXC Eclipse
Microsoft Dynamics 365: Continue Your Transformation Journey.
Data, BI and Advanced Analytics: where Microsoft Dynamics ends and Microsoft BI begins.
Achieve meaningful insight into your data by extending Dynamics’ out-of-the-box BI capabilities. Let us introduce the ‘Too Easy’ BI Proof of Concept.
Presented by Ramond Haynes - BI & Analytics Practice Director, DXC Eclipse
How to Scale BI and Analytics with Hadoop-based PlatformsArcadia Data
You’re using Apache Hadoop and cloud-based data platforms, but can your BI and analytics tools keep up? Can you provide fast, secure, self-service access to all the data business users want?
Analyzing big data poses multiple challenges. Highly parallel distributed data architecture is one solution, but until recently it has been mostly limited to databases, not business intelligence (BI) application servers.
Join this informative webinar with guest speaker Boris Evelson, VP and principal analyst at Forrester Research, and Priyank Patel, co-founder and chief product officer at Arcadia Data. Enterprise architects, data scientists, and application development and delivery (AD&D) pros will learn:
What is a distributed BI platform? How is it different from existing BI tools?
How to scale BI and visual analytics for users without moving data
What features matter most for distributed BI platforms for Hadoop
How to unify security natively in Hadoop without more administration
Speed Matters - Intelligent Strategies to Accelerate Data-Driven DecisionsDATAVERSITY
COVID-19 has shown us the importance of data in being able to quickly make decisions when market variables are out of our control. In order to accelerate and harness the process, an organization needs an agile approach to data integration and analytics that avoids the limitations of predefined schemas and data models.
Learn from 451 Research, now part of S&P Global Market Intelligence, a leading global IT research and advisory firm, and Qlik about best practices that can help you accelerate the data to decision path with agility. You’ll understand how to:
-Rethink traditional assumptions about data management and analytic roles and technologies
-Recognize trends that drive the demand to reduce the time required to investigate, analyze and take action on business data.
See a new state of business intelligence, where the data pipeline is optimized to enable organizations to make decisions and act in real-time. Seeking alternatives to the traditional approaches to become more agile in today’s evolving market and economy? Then don’t miss this presentation!
ADV Slides: Modern Analytic Data Architecture Maturity ModelingDATAVERSITY
Maturity frameworks have varying levels of Data Management maturity. Each level corresponds to not only increased data maturity, but also increased organizational maturity and bottom-line ROI. There are recommended targets to achieve an effective information management program. The speaker’s maturity framework sequences the information management activities for your consideration. It is based on real client roadmaps. This webinar promises to offer a wealth of ideas for key quick wins to benefit the organization’s information management program.
Attendees can self-assess their current information management capabilities as we go through data strategy, organization, architecture, and technology, yielding an overall view of the current level of information management maturity.
This webinar provides a foundation for enhancing current data and analytic capabilities and updating the strategy and plans for achievement of improved information management maturity, aligned with major initiatives.
Users and customers don't just want products and services anymore - they also want the data and analytics that are under the hood! The good news is that delivering value with data is more achievable than ever before thanks to greater access to diverse data sources and the ability to process, blend, and refine data at unprecedented scale.
Google Data Studio is a tool for data visualization and analysis. It allows users to connect to various data sources, including Google Sheets, and build interactive reports and dashboards. The document provides an introduction to Google Data Studio and its key building blocks, such as data sources, dimensions, and metrics. It explains how to access and navigate Data Studio, and discusses why users should first manipulate data in Google Sheets before building reports in Data Studio for better performance.
Sabre: Mastering a strong foundation for operational excellence and enhanced ...Orchestra Networks
1. Sabre implemented a master data management program to establish a single authoritative source of trusted master reference data across the enterprise. This would improve data quality, consistency, and access for analytics.
2. The program addressed issues like a lack of data governance and standards by defining roles and processes for data stewardship, developing master data standards, and implementing tools for data management.
3. Having consistent master data available across contexts improves analytics by ensuring accurate business metrics and reports, eliminating data synchronization issues, and allowing data scientists easy access to trusted data.
Microsoft HDInsight as a Big Data and Interoperability Platform to Drive Poin...DataWorks Summit
Learn how a small team of 3-4 technology and subject matter experts developed an Azure HDInsight solution. The solution captures genomics data for solid tumors, summary data from a third party and various internal sources, and does genomic Clinical Trial matching. This was done strictly using the Azure cloud and interactions with cloud-based Office 365 SharePoint web applications utilizing only batch scripting, Hive, and Sqoop. HD Insights is the data munging layer and SharePoint is the user access layer.
The process was stood up in a 6-8 week period, while doing our day jobs. The business benefit is to enable providers, at the point of care, to suggest clinical trials for oncology patients based on genomic matches (Molecular Tumor Board). This has increased participation rates in clinical trials with the goal to improve the survival rates and quality of life for patients. The success of this project has spread to capturing local home grown registries in data silos to share with other like-minded providers within Levine Cancer Institute.
Data, BI and Advanced Analytics: where Microsoft Dynamics ends and Microsoft ...DXC Eclipse
Microsoft Dynamics 365: Continue Your Transformation Journey.
Data, BI and Advanced Analytics: where Microsoft Dynamics ends and Microsoft BI begins.
Achieve meaningful insight into your data by extending Dynamics’ out-of-the-box BI capabilities. Let us introduce the ‘Too Easy’ BI Proof of Concept.
Presented by Ramond Haynes - BI & Analytics Practice Director, DXC Eclipse
How to Scale BI and Analytics with Hadoop-based PlatformsArcadia Data
You’re using Apache Hadoop and cloud-based data platforms, but can your BI and analytics tools keep up? Can you provide fast, secure, self-service access to all the data business users want?
Analyzing big data poses multiple challenges. Highly parallel distributed data architecture is one solution, but until recently it has been mostly limited to databases, not business intelligence (BI) application servers.
Join this informative webinar with guest speaker Boris Evelson, VP and principal analyst at Forrester Research, and Priyank Patel, co-founder and chief product officer at Arcadia Data. Enterprise architects, data scientists, and application development and delivery (AD&D) pros will learn:
What is a distributed BI platform? How is it different from existing BI tools?
How to scale BI and visual analytics for users without moving data
What features matter most for distributed BI platforms for Hadoop
How to unify security natively in Hadoop without more administration
Speed Matters - Intelligent Strategies to Accelerate Data-Driven DecisionsDATAVERSITY
COVID-19 has shown us the importance of data in being able to quickly make decisions when market variables are out of our control. In order to accelerate and harness the process, an organization needs an agile approach to data integration and analytics that avoids the limitations of predefined schemas and data models.
Learn from 451 Research, now part of S&P Global Market Intelligence, a leading global IT research and advisory firm, and Qlik about best practices that can help you accelerate the data to decision path with agility. You’ll understand how to:
-Rethink traditional assumptions about data management and analytic roles and technologies
-Recognize trends that drive the demand to reduce the time required to investigate, analyze and take action on business data.
See a new state of business intelligence, where the data pipeline is optimized to enable organizations to make decisions and act in real-time. Seeking alternatives to the traditional approaches to become more agile in today’s evolving market and economy? Then don’t miss this presentation!
ADV Slides: Modern Analytic Data Architecture Maturity ModelingDATAVERSITY
Maturity frameworks have varying levels of Data Management maturity. Each level corresponds to not only increased data maturity, but also increased organizational maturity and bottom-line ROI. There are recommended targets to achieve an effective information management program. The speaker’s maturity framework sequences the information management activities for your consideration. It is based on real client roadmaps. This webinar promises to offer a wealth of ideas for key quick wins to benefit the organization’s information management program.
Attendees can self-assess their current information management capabilities as we go through data strategy, organization, architecture, and technology, yielding an overall view of the current level of information management maturity.
This webinar provides a foundation for enhancing current data and analytic capabilities and updating the strategy and plans for achievement of improved information management maturity, aligned with major initiatives.
Users and customers don't just want products and services anymore - they also want the data and analytics that are under the hood! The good news is that delivering value with data is more achievable than ever before thanks to greater access to diverse data sources and the ability to process, blend, and refine data at unprecedented scale.
Google Data Studio is a tool for data visualization and analysis. It allows users to connect to various data sources, including Google Sheets, and build interactive reports and dashboards. The document provides an introduction to Google Data Studio and its key building blocks, such as data sources, dimensions, and metrics. It explains how to access and navigate Data Studio, and discusses why users should first manipulate data in Google Sheets before building reports in Data Studio for better performance.
Sabre: Mastering a strong foundation for operational excellence and enhanced ...Orchestra Networks
1. Sabre implemented a master data management program to establish a single authoritative source of trusted master reference data across the enterprise. This would improve data quality, consistency, and access for analytics.
2. The program addressed issues like a lack of data governance and standards by defining roles and processes for data stewardship, developing master data standards, and implementing tools for data management.
3. Having consistent master data available across contexts improves analytics by ensuring accurate business metrics and reports, eliminating data synchronization issues, and allowing data scientists easy access to trusted data.
This document discusses care coordination in New Zealand's complex health system. It outlines challenges like rising costs, an aging population, and high rates of chronic disease. Care coordination aims to organize care activities between providers to facilitate efficient care delivery. Key components include collaboration, continuity of care, and patient-centered care plans. The document then discusses DXC Healthcare's role in New Zealand, including large clinical system implementations. It presents DXC's digital care coordination solution using a CRM platform to engage and coordinate patients. Finally, it discusses how health data analytics can help with predictive modeling, machine learning and insights to support care coordination goals.
Network World’s State of the Network research is conducted annually to gain a deeper understanding of the network environments within today’s organizations.
CIO’s 17th annual “State of the CIO” survey was conducted with the goal of understanding how the CIO role continues to evolve in today’s business climate and to help define the CIO agenda for 2018.
This document summarizes the results of a survey of 250 respondents from 590 total attendees of the "Big Data: Value & Hidden Insights" event in South Korea. It finds that over half of respondents were from computer software/internet services and most held roles in research & development or engineering. It also shows that respondents mostly dealt with structured data and used Hadoop for analytics. The majority planned to implement big data solutions within a year and had budgets under $500,000. Most respondents were satisfied with the event and found it a good way to acquire market trends and information.
Learn about the organizational and architectural strategies needed to make self-service analytics successful. Self-service is more about process and training instead of only focusing on tools.
Download this research to read about self-service architecture in detail:https://www.eckerson.com/articles/a-reference-architecture-for-self-service-analytics
If you need help with self-service analytics, data architecture or data management, contact us on the following link: https://www.eckerson.com/consulting
Bridging the Data Divide: Driving Data and Analytics Projects Forward with Tr...Precisely
There’s no debate that data is the most valuable strategic asset available to your business today. According to ‘Voice of the Enterprise: Data Management and Analytics 2020’ by 451 Research, 63% of enterprises use data to drive nearly all or most of their strategic decisions.
Join Amy O’Connor, Precisely Chief Data and Information Officer, and Paige Bartley, 451 Research Senior Research Analyst for Data, AI, and Analytics as Paige shares the latest research on data and analytics drawn from surveys of business and technology decision-makers and chats with Amy about her experience implementing Precisely products to ensure data integrity and fuel the company’s data-driven business model.
View this on-demand webinar to hear Amy and Paige share their perspectives on key points from the research, including how:
• Only 25% of respondents rate more than 80% of their recent data and analytics initiatives as successful
• 78% of those most successful with data and analytics initiatives are using or considering using technologies to accelerate the analysis of distributed data
• 24% of respondents are investing in programs that increase trust in data by improving accuracy, quality, lineage and/or governance to improve their data culture
This document provides an overview and strategy for big and fast data initiatives in 2017. It discusses the data landscape including volume, velocity, variety and validity. It evaluates different data platform technologies and outlines requirements. The vision is described as "Business Insights at the Speed of Light". The strategy focuses on speed and leveraging key technologies like Spark. A roadmap with initiatives around insights, infrastructure, ingestion and big BI is presented. High level architectures for streaming and data flow are shown. Finally, data preparation vendors are compared.
Governing and Preparing Data for Analytics and BusinessMark Smith
As business becomes more self-sufficient in accessing and putting data to work for analytics, there are many steps that are circumvented that can jeopardize the quality of business decisions. While it might seem easy to do one-off data preparation cycles that create analytic silos, the importance of placing governance on the data and users is essential to ensure accuracy of information used by business. The solution for these challenges can be addressed by applying effective processes and systems that are shared across business and IT. In this presentation, you'll will learn the latest best practices and steps to increase data governance and preparation processes that will shorten the time to efficiently connect users and data at any time
Slides: The Business Value of Data ModelingDATAVERSITY
With changes in software development methodologies, the role of the data modeler has changed significantly. In many organizations, data modelers now find themselves on the outside looking in, relegated to documentation "after the fact" rather than active participation where the true value is added. In order to participate fully, modelers must not only adapt to an Agile work style, but must also be able to communicate the business value of model driven development.
This session is based on a real case study in which data modeling was introduced part-way through a significant software development project that was quickly losing momentum due to high defect levels. Ron Huizenga will show the contrast in metrics and cost when utilizing skilled data modelers versus a development-only approach, with topics including:
Modeler participation in multiple Agile teams
Defect categories and impact
Measurement and analysis techniques
Remediation strategy
Breakthrough quality improvements
This "must see" session is not only for data modelers and architects, but also the decision makers for these initiatives, with information that is vital to modelers, IT executives and business sponsors. So bring your boss to the session!
.
The document discusses delivering data governance with data intelligence software. It begins with introductions of the authors and an agenda for the discussion. It then outlines how data in the digital transformation era is dynamic, diverse and distributed across hybrid cloud environments. This complexity leads to inefficiencies like 81% of time being spent searching for and preparing data with only 20% left for analysis. Data intelligence software can help by providing data discovery, cataloging and profiling to answer the "5 W's of data" and build trust. The document prescribes a three step plan for organizations to deliver trusted data using data intelligence software: 1) discover and clean data, 2) organize and empower data stewards, 3) automate and enable self service access
Real-time Data is Changing the Face of the Insurance IndustryDataWorks Summit
The insurance industry was founded on data and yet, new data sources and the “speed” of data are entirely changing how the industry conducts its business. Real-time data used to be a foreign term for insurers but in the digital and connected world it has a significant impact on how the industry engages with customers, manages relationships, conducts core operations of risk assessments and manages claims.
Predictive analytics is the minimum table stakes to remain competitive. Preventive analytics and machine learning are on the rise to the extent they are called out and considered critical success factors in an insurance company’s business strategy. The question is, how do you prepare the organization and adjust the mindset of a business to use real-time data to better serve customers whether individuals or companies?
During this interactive session insurance industry leaders will discuss a variety of topics, including:
· how business data strategies are changing
· filling the skills gap
· value of open data sources and incorporating machine learning
In an age where the insurer must be founded on machine learning and advanced analytics, you’ll hear from the leaders who have a grasp on the opportunities, as well as how to avoid and/or prepare for the bumps along the way
Speakers for this Session:
1. Cindy Maike
2. Denise Rogers
3. Naresh Mudunuru
The document discusses how leading organizations are evolving to adopt more data-driven decision making cultures. It finds that organizations face increasing pressure to make decisions faster amid shrinking time windows. As a result, many organizations are enhancing employee skills to better integrate analytics, balancing data with experience, and forging new relationships between decision makers and analytics professionals. The most advanced organizations are developing best practices that distribute data and tools widely to promote transparency.
Big Data Maturity as a Business: A Retail Case StudyHortonworks
In this webinar, we will share findings and insights from the maturity scorecards we have completed with the world's leading retailers, how they used this to secure executive sponsorship to ensure the data technology and business requirements were in tandem, as well as the use cases typically pursued. We will discuss the typical organizational constructs we see applicable based on the different stages of maturity and also discuss some best practices for driving best in class process for data driven transformation.
Big data analytic_ecosystem - bigdataanalyticsecosystemwwAidelisa Gutierrez
The document discusses opportunities for growth in analytics, big data, and in-memory databases from 2013 to 2017. It finds that the global market for analytics and big data will grow to $220 billion by 2017, with North America representing $90 billion of that total. It also reports that for every $1 of revenue SAP makes from in-memory databases or analytics and big data, SAP partners will make $10.86 and $2.71 in revenue, respectively, presenting significant opportunities for partners. The top verticals for analytics and big data are discrete manufacturing, process manufacturing, government, and communications and media.
Data Catalog as the Platform for Data IntelligenceAlation
Data catalogs are in wide use today across hundreds of enterprises as a means to help data scientists and business analysts find and collaboratively analyze data. Over the past several years, customers have increasingly used data catalogs in applications beyond their search & discovery roots, addressing new use cases such as data governance, cloud data migration, and digital transformation. In this session, the founder and CEO of Alation will discuss the evolution of the data catalog, the many ways in which data catalogs are being used today, the importance of machine learning in data catalogs, and discuss the future of the data catalog as a platform for a broad range of data intelligence solutions.
This document provides a template and methodology for conducting a business intelligence (BI) assessment. The assessment examines organizational data management across several pillars including strategy, processes, applications, key performance indicators and people/ownership. It involves defining the current ("as-is") state, desired future ("to-be") state, and gap closing program to transition between the two states in phases. The gap closing program consists of strategic phases and tactical projects. The overall methodology includes planning, reviewing the as-is state, defining the to-be state, developing the gap closing program, and delivering the final assessment package.
2017 Role & Influence of the Technology Decision-MakerIDG
The 2017 IDG Role & Influence of the Technology Decision-Maker survey examines the evolving role of IT decision-makers (ITDMs) in today’s corporations, specifically as organizations move towards a more digital-focused business.
RWDG Slides: Governing Your Data Catalog, Business Glossary, and Data DictionaryDATAVERSITY
The document discusses governing data catalogs, business glossaries, and data dictionaries. It describes these tools as core components of a successful data governance program and important at the operational and tactical levels. Governing the metadata in these tools provides value, but requires effort to govern roles, processes, communications, and metrics around these tools. The document advocates a pragmatic approach to governance through these tools to guide participation and knowledge sharing in a community.
Improving the Business of Healthcare through Better Analytics Pentaho
The document discusses challenges in the US healthcare system and how big data analytics can help address these challenges. It then provides details on MedeAnalytics, a company that provides big data analytics solutions for healthcare. MedeAnalytics collects over 21 billion records from 10,000+ data feeds, manages over 300 terabytes of data, and serves over 900 hospital and payer clients. The document outlines MedeAnalytics' suite of solutions and how they help improve financial, operational, and clinical outcomes for healthcare providers and health plans.
The document discusses predictive analytics and its applications. It begins by defining predictive analytics as using data patterns to predict future outcomes. It then discusses how various industries like marketing, risk management, and operations are using predictive analytics for applications such as targeting customers, assessing risk, and optimizing processes. The document provides examples of how predictive models are used for response modeling, customer segmentation, loyalty/retention, and assessing customer profitability in marketing. It also discusses using predictive models for predicting defaults in risk applications.
Summary of three National webinars. Three V's, market, Functional areas showing most traction, Hot Revenue/ROI areas, Architecture options and using Use cases to overcome objections.,
The document discusses the Hadoop Big Data Analytics market. It states that the market is expected to grow at a CAGR of 47.30% by 2025 due to growing demand from organizations for analytics on customer and enterprise data. Key factors driving market growth include increasing data volumes, digitalization, and the convergence of big data and IoT. The market is segmented based on application, vertical, component, and region. Major players in the market include Cloudera, Pentaho, Marklogic, SAP, and Microsoft.
This document discusses care coordination in New Zealand's complex health system. It outlines challenges like rising costs, an aging population, and high rates of chronic disease. Care coordination aims to organize care activities between providers to facilitate efficient care delivery. Key components include collaboration, continuity of care, and patient-centered care plans. The document then discusses DXC Healthcare's role in New Zealand, including large clinical system implementations. It presents DXC's digital care coordination solution using a CRM platform to engage and coordinate patients. Finally, it discusses how health data analytics can help with predictive modeling, machine learning and insights to support care coordination goals.
Network World’s State of the Network research is conducted annually to gain a deeper understanding of the network environments within today’s organizations.
CIO’s 17th annual “State of the CIO” survey was conducted with the goal of understanding how the CIO role continues to evolve in today’s business climate and to help define the CIO agenda for 2018.
This document summarizes the results of a survey of 250 respondents from 590 total attendees of the "Big Data: Value & Hidden Insights" event in South Korea. It finds that over half of respondents were from computer software/internet services and most held roles in research & development or engineering. It also shows that respondents mostly dealt with structured data and used Hadoop for analytics. The majority planned to implement big data solutions within a year and had budgets under $500,000. Most respondents were satisfied with the event and found it a good way to acquire market trends and information.
Learn about the organizational and architectural strategies needed to make self-service analytics successful. Self-service is more about process and training instead of only focusing on tools.
Download this research to read about self-service architecture in detail:https://www.eckerson.com/articles/a-reference-architecture-for-self-service-analytics
If you need help with self-service analytics, data architecture or data management, contact us on the following link: https://www.eckerson.com/consulting
Bridging the Data Divide: Driving Data and Analytics Projects Forward with Tr...Precisely
There’s no debate that data is the most valuable strategic asset available to your business today. According to ‘Voice of the Enterprise: Data Management and Analytics 2020’ by 451 Research, 63% of enterprises use data to drive nearly all or most of their strategic decisions.
Join Amy O’Connor, Precisely Chief Data and Information Officer, and Paige Bartley, 451 Research Senior Research Analyst for Data, AI, and Analytics as Paige shares the latest research on data and analytics drawn from surveys of business and technology decision-makers and chats with Amy about her experience implementing Precisely products to ensure data integrity and fuel the company’s data-driven business model.
View this on-demand webinar to hear Amy and Paige share their perspectives on key points from the research, including how:
• Only 25% of respondents rate more than 80% of their recent data and analytics initiatives as successful
• 78% of those most successful with data and analytics initiatives are using or considering using technologies to accelerate the analysis of distributed data
• 24% of respondents are investing in programs that increase trust in data by improving accuracy, quality, lineage and/or governance to improve their data culture
This document provides an overview and strategy for big and fast data initiatives in 2017. It discusses the data landscape including volume, velocity, variety and validity. It evaluates different data platform technologies and outlines requirements. The vision is described as "Business Insights at the Speed of Light". The strategy focuses on speed and leveraging key technologies like Spark. A roadmap with initiatives around insights, infrastructure, ingestion and big BI is presented. High level architectures for streaming and data flow are shown. Finally, data preparation vendors are compared.
Governing and Preparing Data for Analytics and BusinessMark Smith
As business becomes more self-sufficient in accessing and putting data to work for analytics, there are many steps that are circumvented that can jeopardize the quality of business decisions. While it might seem easy to do one-off data preparation cycles that create analytic silos, the importance of placing governance on the data and users is essential to ensure accuracy of information used by business. The solution for these challenges can be addressed by applying effective processes and systems that are shared across business and IT. In this presentation, you'll will learn the latest best practices and steps to increase data governance and preparation processes that will shorten the time to efficiently connect users and data at any time
Slides: The Business Value of Data ModelingDATAVERSITY
With changes in software development methodologies, the role of the data modeler has changed significantly. In many organizations, data modelers now find themselves on the outside looking in, relegated to documentation "after the fact" rather than active participation where the true value is added. In order to participate fully, modelers must not only adapt to an Agile work style, but must also be able to communicate the business value of model driven development.
This session is based on a real case study in which data modeling was introduced part-way through a significant software development project that was quickly losing momentum due to high defect levels. Ron Huizenga will show the contrast in metrics and cost when utilizing skilled data modelers versus a development-only approach, with topics including:
Modeler participation in multiple Agile teams
Defect categories and impact
Measurement and analysis techniques
Remediation strategy
Breakthrough quality improvements
This "must see" session is not only for data modelers and architects, but also the decision makers for these initiatives, with information that is vital to modelers, IT executives and business sponsors. So bring your boss to the session!
.
The document discusses delivering data governance with data intelligence software. It begins with introductions of the authors and an agenda for the discussion. It then outlines how data in the digital transformation era is dynamic, diverse and distributed across hybrid cloud environments. This complexity leads to inefficiencies like 81% of time being spent searching for and preparing data with only 20% left for analysis. Data intelligence software can help by providing data discovery, cataloging and profiling to answer the "5 W's of data" and build trust. The document prescribes a three step plan for organizations to deliver trusted data using data intelligence software: 1) discover and clean data, 2) organize and empower data stewards, 3) automate and enable self service access
Real-time Data is Changing the Face of the Insurance IndustryDataWorks Summit
The insurance industry was founded on data and yet, new data sources and the “speed” of data are entirely changing how the industry conducts its business. Real-time data used to be a foreign term for insurers but in the digital and connected world it has a significant impact on how the industry engages with customers, manages relationships, conducts core operations of risk assessments and manages claims.
Predictive analytics is the minimum table stakes to remain competitive. Preventive analytics and machine learning are on the rise to the extent they are called out and considered critical success factors in an insurance company’s business strategy. The question is, how do you prepare the organization and adjust the mindset of a business to use real-time data to better serve customers whether individuals or companies?
During this interactive session insurance industry leaders will discuss a variety of topics, including:
· how business data strategies are changing
· filling the skills gap
· value of open data sources and incorporating machine learning
In an age where the insurer must be founded on machine learning and advanced analytics, you’ll hear from the leaders who have a grasp on the opportunities, as well as how to avoid and/or prepare for the bumps along the way
Speakers for this Session:
1. Cindy Maike
2. Denise Rogers
3. Naresh Mudunuru
The document discusses how leading organizations are evolving to adopt more data-driven decision making cultures. It finds that organizations face increasing pressure to make decisions faster amid shrinking time windows. As a result, many organizations are enhancing employee skills to better integrate analytics, balancing data with experience, and forging new relationships between decision makers and analytics professionals. The most advanced organizations are developing best practices that distribute data and tools widely to promote transparency.
Big Data Maturity as a Business: A Retail Case StudyHortonworks
In this webinar, we will share findings and insights from the maturity scorecards we have completed with the world's leading retailers, how they used this to secure executive sponsorship to ensure the data technology and business requirements were in tandem, as well as the use cases typically pursued. We will discuss the typical organizational constructs we see applicable based on the different stages of maturity and also discuss some best practices for driving best in class process for data driven transformation.
Big data analytic_ecosystem - bigdataanalyticsecosystemwwAidelisa Gutierrez
The document discusses opportunities for growth in analytics, big data, and in-memory databases from 2013 to 2017. It finds that the global market for analytics and big data will grow to $220 billion by 2017, with North America representing $90 billion of that total. It also reports that for every $1 of revenue SAP makes from in-memory databases or analytics and big data, SAP partners will make $10.86 and $2.71 in revenue, respectively, presenting significant opportunities for partners. The top verticals for analytics and big data are discrete manufacturing, process manufacturing, government, and communications and media.
Data Catalog as the Platform for Data IntelligenceAlation
Data catalogs are in wide use today across hundreds of enterprises as a means to help data scientists and business analysts find and collaboratively analyze data. Over the past several years, customers have increasingly used data catalogs in applications beyond their search & discovery roots, addressing new use cases such as data governance, cloud data migration, and digital transformation. In this session, the founder and CEO of Alation will discuss the evolution of the data catalog, the many ways in which data catalogs are being used today, the importance of machine learning in data catalogs, and discuss the future of the data catalog as a platform for a broad range of data intelligence solutions.
This document provides a template and methodology for conducting a business intelligence (BI) assessment. The assessment examines organizational data management across several pillars including strategy, processes, applications, key performance indicators and people/ownership. It involves defining the current ("as-is") state, desired future ("to-be") state, and gap closing program to transition between the two states in phases. The gap closing program consists of strategic phases and tactical projects. The overall methodology includes planning, reviewing the as-is state, defining the to-be state, developing the gap closing program, and delivering the final assessment package.
2017 Role & Influence of the Technology Decision-MakerIDG
The 2017 IDG Role & Influence of the Technology Decision-Maker survey examines the evolving role of IT decision-makers (ITDMs) in today’s corporations, specifically as organizations move towards a more digital-focused business.
RWDG Slides: Governing Your Data Catalog, Business Glossary, and Data DictionaryDATAVERSITY
The document discusses governing data catalogs, business glossaries, and data dictionaries. It describes these tools as core components of a successful data governance program and important at the operational and tactical levels. Governing the metadata in these tools provides value, but requires effort to govern roles, processes, communications, and metrics around these tools. The document advocates a pragmatic approach to governance through these tools to guide participation and knowledge sharing in a community.
Improving the Business of Healthcare through Better Analytics Pentaho
The document discusses challenges in the US healthcare system and how big data analytics can help address these challenges. It then provides details on MedeAnalytics, a company that provides big data analytics solutions for healthcare. MedeAnalytics collects over 21 billion records from 10,000+ data feeds, manages over 300 terabytes of data, and serves over 900 hospital and payer clients. The document outlines MedeAnalytics' suite of solutions and how they help improve financial, operational, and clinical outcomes for healthcare providers and health plans.
The document discusses predictive analytics and its applications. It begins by defining predictive analytics as using data patterns to predict future outcomes. It then discusses how various industries like marketing, risk management, and operations are using predictive analytics for applications such as targeting customers, assessing risk, and optimizing processes. The document provides examples of how predictive models are used for response modeling, customer segmentation, loyalty/retention, and assessing customer profitability in marketing. It also discusses using predictive models for predicting defaults in risk applications.
Summary of three National webinars. Three V's, market, Functional areas showing most traction, Hot Revenue/ROI areas, Architecture options and using Use cases to overcome objections.,
The document discusses the Hadoop Big Data Analytics market. It states that the market is expected to grow at a CAGR of 47.30% by 2025 due to growing demand from organizations for analytics on customer and enterprise data. Key factors driving market growth include increasing data volumes, digitalization, and the convergence of big data and IoT. The market is segmented based on application, vertical, component, and region. Major players in the market include Cloudera, Pentaho, Marklogic, SAP, and Microsoft.
The global data integration software market generated revenue of US$ 8.6 billion in 2020 and is expected to reach US$ 15.7 billion by 2025 with a CAGR of 12.6% in the forecast period. The data integration software market report offers a comprehensive market analysis of the different segments and regions that lets readers make crucial business-related decisions with a wealth of information enclosed in this report. The research report offers both qualitative and quantitative information on the global data integration software market. In qualitative terms, the data integration software market report provides insights into numerous factors, such as market determinants, value chain analysis, emerging trends, growth opportunity analysis, porters five-force model analysis and macro-economic factors, segment analysis, regional analysis at a granular level. Similarly, in quantitative terms, the report provides historical and forecast market numbers of data integration software in various segments such as by component, deployment model, enterprise size, application and industry at global, regional, and country-level. In addition, the report provides a detailed analysis of the market vendors and their product offerings. The report also covers details of the competitive market environment and includes information on the capabilities and competencies of market vendors.
- Teradata presented its first quarter 2015 results, highlighting its portfolio of data warehousing and big data analytics platforms.
- It discussed its various workload-specific platforms that scale from terabytes to petabytes and its offerings for Hadoop, positioning itself as the leader in data warehousing.
- The presentation also reviewed Teradata's industry-leading technology, partnerships, financial results and positioning by analysts as the leader in data warehousing and marketing solutions.
- Teradata presented its second quarter 2015 results, highlighting its realigned structure into two divisions focused on data warehouse/analytics and marketing applications.
- It discussed its portfolio of workload-specific platforms that scale from small to extremely large environments and its leadership in analytics according to Gartner and Forrester reports.
- Teradata has the broadest and most flexible offerings for Hadoop, including appliances and software-only options.
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- Teradata presented its second quarter 2015 investor presentation, which outlined its realigned business structure, leadership in analytics platforms, flexible Hadoop offerings, and ability to scale across multiple dimensions.
- It discussed its two new divisions, portfolio of workload-specific platforms, integrated marketing cloud solutions, and strong financial position with growing recurring revenue and consistent cash flow generation.
- Teradata presented its second quarter 2015 results, highlighting its realigned structure into two divisions focused on data warehouse/analytics and marketing applications.
- It discussed its broad portfolio of workload-specific platforms and analytical ecosystem, noting industry-leading capabilities in scalability, performance, availability, and data handling.
- Financial results showed continued growth, a strong balance sheet and cash flow, and an increasing shift to recurring revenue streams.
- Teradata presented its third quarter 2015 results, highlighting its realigned structure into two divisions focused on data and analytics and marketing applications.
- It discussed its broad portfolio of workload-specific platforms that can scale across multiple dimensions to meet business needs rather than being limited by technology.
- Teradata has a strong balance sheet and cash flow position with over 40% of revenue coming from recurring sources and continued growth in cash from operations and free cash flow.
The document discusses opportunities for growth in analytics, big data, and in-memory databases from 2013 to 2017. It finds that the global market for analytics and big data will grow to $220 billion by 2017, with North America representing $90 billion of that total. It also reports that for every $1 of revenue SAP makes from in-memory databases or analytics and big data, SAP partners will make $10.86 and $2.71 in revenue, respectively, presenting significant opportunities for partners through 2017. Customers need help with analytics and big data strategies, skills, and technologies as 90% of future IT industry growth will be driven by technologies in these areas.
Big Data as a Service (BDaaS) Market PPT: Growth, Outlook, Demand, Keyplayer ...IMARC Group
According to the latest research report by IMARC Group, The global big data as a service (BDaaS) market size reached US$ 38.6 Billion in 2022. Looking forward, IMARC Group expects the market to reach US$ 120.7 Billion by 2028, exhibiting a growth rate (CAGR) of 20.78% during 2023-2028.
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Big Data as a Service (BDaaS) Market PPT: Growth, Outlook, Demand, Keyplayer ...IMARC Group
The global big data as a service (BDaaS) market size reached US$ 46.6 Billion in 2023. Looking forward, IMARC Group expects the market to reach US$ 217.9 Billion by 2032, exhibiting a growth rate (CAGR) of 18.1% during 2024-2032.
More Info:- https://www.imarcgroup.com/big-data-as-a-service-market
The document discusses business intelligence and analytics solutions. It notes challenges around accessing heterogeneous data from various sources, manual data processing, long analysis times, and limited operational and performance data. The solution proposed is a business intelligence system that can provide consolidated analysis, dashboards and reports to help make more informed decisions. Key benefits are reducing time and costs for analysis and decision making, improving customer satisfaction and operational efficiency through better insights into business and market trends.
The document discusses how the global data integration market has grown due to increased use of computing devices and need to integrate data from various sources. It provides an overview of the impact of COVID-19 on the market and segmentation of the market by components, organization size, deployment model, solutions, verticals, and region. Major players in the data integration market are also mentioned. The global data integration market is expected to grow at a CAGR of 14.93% until 2025.
Emerging Trends in Data Architecture – What’s the Next Big ThingDATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Q4 2015 investor presentation web copy 11 5 2015 435pmteradata2014
This document provides an investor presentation for Teradata's fourth quarter 2015 results. It includes the following key points:
- Teradata leads in the analytics market according to Gartner and Forrester evaluations.
- Teradata offers a range of workload-specific platforms and appliances to address different analytics needs and workloads.
- Teradata has the broadest and most flexible offerings for Hadoop in the market.
- Teradata's technology provides extreme scalability, performance, availability and support for a variety of data and workloads.
The document discusses new opportunities arising from Big Data 2.0. It provides biographies of the two presenters, Shawn Rogers and John Santaferraro, and outlines the agenda and logistics for the webinar. The presentation then covers the shift towards more sophisticated Big Data use, the emergence of hybrid data ecosystems combining traditional and modern data sources, and the technical drivers and common use cases behind Big Data projects.
The document discusses a presentation on data discovery tools and the current market landscape. It summarizes research from Gartner and IDC on trends in business intelligence, analytics platforms, and data discovery vendors. The main points are that data discovery tools are gaining popularity and market share as they are easier for business users to use and implement compared to traditional top-down BI platforms. However, the document notes that ensuring proper governance of data as these bottom-up tools proliferate remains a challenge. It provides an overview of various vendors and market predictions on the growth of data discovery tools.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
eTailing India Launches Big Data Report - 2015 eTailing India
The document discusses the current state of big data in India and its potential impact on eCommerce growth. It notes that big data involves collecting, processing, and applying insights from large, diverse data sets. While still nascent in India, big data is projected to significantly impact eCommerce by providing deeper customer insights and more personalized experiences. Major players are adopting strategies like Hadoop to analyze customer behavior and improve conversions. Widespread adoption is expected to drive industry competition and innovation.
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Part 2 Deep Dive: Navigating the 2024 Slowdownjeffkluth1
Introduction
The global retail industry has weathered numerous storms, with the financial crisis of 2008 serving as a poignant reminder of the sector's resilience and adaptability. However, as we navigate the complex landscape of 2024, retailers face a unique set of challenges that demand innovative strategies and a fundamental shift in mindset. This white paper contrasts the impact of the 2008 recession on the retail sector with the current headwinds retailers are grappling with, while offering a comprehensive roadmap for success in this new paradigm.
Understanding User Needs and Satisfying ThemAggregage
https://www.productmanagementtoday.com/frs/26903918/understanding-user-needs-and-satisfying-them
We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.
In this webinar, we won't focus on the research methods for discovering user-needs. We will focus on synthesis of the needs we discover, communication and alignment tools, and how we operationalize addressing those needs.
Industry expert Scott Sehlhorst will:
• Introduce a taxonomy for user goals with real world examples
• Present the Onion Diagram, a tool for contextualizing task-level goals
• Illustrate how customer journey maps capture activity-level and task-level goals
• Demonstrate the best approach to selection and prioritization of user-goals to address
• Highlight the crucial benchmarks, observable changes, in ensuring fulfillment of customer needs
2. Contents
1. Executive Summary
1.1 Big Data Adoption Rising Amongst Enterprises
1.2 Big Data Proving To Be Beneficial to Organizations Across All Sectors
1.3 Big Data Benefits Outweigh the Security Risks
1.4 Ecosystem Members in the Big Data Market
1.5 Big Data Enterprise Migration Driving Growth
1.6 Aim of the Report
1.7 Structure of the Report
1.8 Report Scope
1.9 Highlights in the report include:
1.10 Who is This Report For?
1.11 Methodology
2. Introduction to the Big Data Market
2.1 The Practice of Big Data
2.2 The Concept behind Big Data
2.3 Defining the Term Big Data
2.4 Categorizing Big Data
2.5 Different Types of Big Data
2.6 Business Case for Big Data Analytics
2.7 Enterprise Application for Big Data Analytics
2.8 Big Data a Catalyst for Innovation & Productivity
2.9 Trust Issues & Security Concerns with Regards to Big Data Outsourcing
2.10 Challenges of Big Data
2.11 Big Data Processing Pipeline
2.11.1 Big Data Processing Pipeline – Major Steps
2.11.2 Big Data Processing Pipeline – Common Challenges
2.12 Big Data Technologies
2.12.1 Apache Hadoop
2.12.2 NoSQL Database
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3. Contents
2.12.3 Additional Big Data Technologies
2.13 Visual Representation of Big Data
3. Global Big Data Market Forecasts 2013-2018
3.1 Significant Enterprise Interest Driving the Big Data Market Forward
4. Competitor Positioning in the Big Data Market
4.1 Leading 20 Company Revenues in the Big Data Market
4.2 Composition of the Big Data Market in 2014
5. Leading 20 Companies in the Big Data Market
5.1 IBM Company Overview
5.1.1 IBM Smart Analytics System
5.2 HP Company Overview
5.3 Teradata Company Overview
5.3.1 Teradata Big Data Analytics Offering – Teradata Unified Data
5.4 Dell Company Overview
5.4.1 Kitenga Analytics Suite
5.5 Oracle Company Overview
5.5.1 Oracle Big Data Analytics Solution
5.6 SAP Company Overview
5.6.1 SAP Big Data Analytics Offering
5.7 EMC Company Overview
5.5.1 EMC Products and Services
5.8 Cisco Systems Company Overview
5.9 PwC Company Overview
5.9.1 PwC Big Data Offering
5.10 Microsoft Company Overview
5.10 Microsoft Big Data Analytics – Offerings and Advantages
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4. Contents
5.11 Accenture Company Overview
5.11.1 Accenture Big Data Offering
5.11.2 Accenture Big Data Services
5.12 Palantir Technologies Company Overview
5.12.1 Palantir Technologies Big Data Focus
5.12.1 Palantir Products
5.12.2 Palantir Customers and Focus
5.12.3 Palantir Big Data Analytics Services
5.13 Fusion-io Company Overview
5.13.1 Fusion-io Customers and Market Standing
5.14 SAS Institute Company Overview
5.14.1 SAS Analytics Portfolio Analysis
5.15 Splunk Company Overview
5.15.1 Splunk Big Data Analytics Offering
5.16 Deloitte Company Overview
5.16.1 Big Data Analytics Offerings
5.17 NetApp Company Overview
5.15.1 NetApp Open Solution for Hadoop
5.18 Hitachi Company Overview
5.18.1 Hitachi Big Data Analytics Offering
5.19 Opera Solutions Company Overview
5.19.1 Opera Solutions Big Data Analytics Offerings
5.20 CSC Company Overview
5.20.1 CSC Big Data Analytics Offerings Analysis
5.21 Additional Players in the Big Data Market Ecosystem
6. SWOT Analysis of the Big Data Market
7. Expert Opinion
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5. Contents
7.1 Deloitte
7.1.1 Deloitte Company Background and Involvement in Big Data
7.1.2 Key Trends & Recent Developments in the Big Data Market
7.1.3 Primary Drivers & Restraints of the Big Data Market
7.1.4 Expected Technological Developments in the Big Data Analytics Market
7.1.5 Leading Players in the Big Data Market
7.1.6 Competitive Landscape Outlook
7.1.7 Regional Growth Focus in the Big Data Market
7.1.8 Challenges & Opportunities in the Big Data Market
7.1.9 Final Thoughts
7.2 Fusion-io
7.2.1 Fusion-io Company Background and Involvement in Big Data
7.2.2 Key Trends & Recent Developments in the Big Data Market
7.2.3 Expected Technological Developments in the Big Data Analytics Market
7.2.4 Leading Players in the Big Data Market
7.2.5 Revenue Growth Estimates
7.2.6 Competitive Landscape Outlook
7.2.7 Regional Growth Focus in the Big Data Market
7.2.8 Challenges & Opportunities in the Big Data Market
7.2.9 Final Thoughts
7.3 IBM
7.3.1 IBM Company Background and Involvement in Big Data
7.3.2 Key Trends & Recent Developments in the Big Data Market
7.3.3 Expected Technological Developments in the Big Data Market
7.3.4 Regional Growth Focus in the Big Data Market
7.3.5 Challenges & Opportunities in the Big Data Market
7.3.6 Primary Drivers & Restraints of the Big Data Market
7.3.7 Business Case for Big Data Analytics
7.3.8 Future of IBM in the Big Data Market
7.4 Splunk
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6. Contents
7.4.1 Splunk Company Background and Involvement in Big Data
7.4.2 Key Trends & Recent Developments in the Big Data Market
7.4.3 Expected Technological Developments in the Big Data Analytics Market
7.4.4 Leading Players in the Big Data Market
7.4.5 Revenue Growth Estimates
7.4.6 Competitive Landscape Outlook
7.4.7 Regional Growth Focus in the Big Data Market
7.4.8 Challenges & Opportunities in the Big Data Market
8. Conclusions
8.1 Enterprise Adaption of Big Data Services
8.2 Choosing the Right Big Data Services
8.3 Increasing Availability of Public Data
8.4 Continued Growth of Big Data Analytics
8.5 Discussion
8.6 Market Share & Outlook for the 20 Leading Big Data Companies
9. Glossary
List of Tables
Table 2.1 Big Data – Defining Factors
Table 2.2 Key Types of Big Data
Table 2.3 Big Data Challenges
Table 2.4 Big Data Processing Pipeline – Major Steps
Table 2.5 Big Data Processing Pipeline – Common Challenges
Table 2.6 Apache Hadoop Modules
Table 2.7 Apache Hadoop Strengths & Limitations
Table 2.8 NoSQL vs SQL Database Summary
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7. Contents
Table 2.9 Additional Big Data Technologies
Table 3.1 Global Big Data Market Forecast 2013-2018 ($ bn, AGR %, CAGR%, Cumulative)
Table 4.1 Leading 20 Big Data Companies 2014 (Market Ranking, Revenue, Offerings, Market
Share %)
Table 5.1 IBM Company Overview 2014 (Total Revenue, Revenue from Big Data, % Revenue
From Big Data, Global Market Share %, HQ, Ticker, Contact, Website)
Table 5.2 IBM Big Data Platform - Key Capabilities
Table 5.3 IBM Big Data Platform - Supporting Services
Table 5.4 IBM Smart Analytics System Summary
Table 5.5 HP Company Overview 2014 (Total Revenue, Revenue from Big Data, % Revenue
From Big Data, Global Market Share %, HQ, Ticker, Contact, Website)
Table 5.6 HAVEn Key Summary (Advantages, Description)
Table 5.7 HAVEn - Technical Specifications
Table 5.8 HAVEn Solutions
Table 5.9 Teradata Company Overview 2014 (Total Revenue, Revenue from Big Data, %
Revenue From Big Data, Global Market Share %, HQ, Ticker, Contact, Website)
Table 5.10 Teradata Unified Data Architecture
Table 5.11 Dell Company Overview 2014 (Total Revenue, Revenue from Big Data, % Revenue
From Big Data, Global Market Share %, HQ, Ticker, Contact, Website)
Table 5.12 Kitenga Analytics Suite - Features and Benefits
Table 5.13 Oracle Company Overview 2014 (Total Revenue, Revenue from Big Data, % Revenue
From Big Data, Global Market Share %, HQ, Ticker, Contact, Website)
Table 5.14 SAP Company Overview 2014 (Total Revenue, Revenue from Big Data, % Revenue
From Big Data, Global Market Share %, HQ, Ticker, Contact, Website)
Table 5.15 SAP Big Data Offerings
Table 5.16 EMC Company Overview 2014 (Total Revenue, Revenue from Big Data, % Revenue
From Big Data, Global Market Share %, HQ, Ticker, Contact, Website)
Table 5.17 EMC Big Data Analytics Solutions
Table 5.18 EMC Big Data Analytics Solutions
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8. Contents
Table 5.19 Cisco Company Overview 2014 (Total Revenue, Revenue from Big Data, % Revenue
From Big Data, Global Market Share %, HQ, Ticker, Contact, Website)
Table 5.20 Cisco Big Data Offerings
Table 5.21 PwC Company Overview 2014 (Total Revenue, Revenue from Big Data, % Revenue
From Big Data, Global Market Share %, HQ, Ticker, Website)
Table 5.22 PwC Big Data Offering
Table 5.23 Microsoft Company Overview 2014 (Total Revenue, Revenue from Big Data, %
Revenue From Big Data, Global Market Share %, HQ, Ticker, Website)
Table 5.24 Microsoft Big Data Analysis Summary
Table 5.25 Accenture Company Overview 2014 (Total Revenue, Revenue from Big Data, %
Revenue From Big Data, Global Market Share %, HQ, Ticker, Contact, Website)
Table 5.26 Accenture Big Data Services
Table 5.27 Palantir Technologies Company Overview 2014 (Total Revenue, Revenue from Big
Data, % Revenue From Big Data, Global Market Share %, HQ, Contact, Website)
Table 5.28 Palantir Big Data Focus
Table 5.29 Palantir Products
Table 5.30 Palantir Insurance Analytics
Table 5.31 Fusion-io Company Overview 2014 (Total Revenue, Revenue from Big Data, %
Revenue From Big Data, Global Market Share %, HQ, Ticker, Contact, Website)
Table 5.32 SAS Institute Company Overview 2014 (Total Revenue, Revenue from Big Data, %
Revenue From Big Data, Global Market Share %, HQ, Ticker, Contact, Website)
Table 5.33 SAS Analytics Portfolio
Table 5.34 Splunk Company Overview 2014 (Total Revenue, Revenue from Big Data, % Revenue
From Big Data, Global Market Share %, HQ, Ticker, Contact, Website)
Table 5.35 Splunk Big Data Analytics Offerings
Table 5.36 Deloitte Company Overview 2014 (Total Revenue, Revenue from Big DAta, %
Revenue From Big Data, Global Market Share %, HQ, Contact, Website)
Table 5.37 Deloitte's Analytics Services
Table 5.38 NetApp Company Overview 2014 (Total Revenue, Revenue from Big Data, %
Revenue From Big Data, Global Market Share %, HQ, Ticker, Contact, Website)
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9. Contents
Table 5.39 Hitachi Company Overview 2014 (Total Revenue, Revenue from Big Data, %
Revenue From Big Data, Global Market Share %, HQ, Ticker, Contact, Website)
Table 5.40 Hitachi Big Data Analytics Offering - Features and Benefits
Table 5.41 Opera Solutions Company Overview 2014 (Total Revenue, Revenue from Big Data, %
Revenue From Big Data, Global Market Share %, HQ, Website)
Table 5.42 Operas Solutions Big Data Analytics Solutions and Services
Table 5.43 CSC Company Overview 2014 (Total Revenue, Revenue from Big Data, % Revenue
From Big Data, Global Market Share %, HQ, Ticker, Contact, Website)
Table 5.44 CSC Big Data Analytics Offerings
Table 5.45 Additional Players in the Big Data Ecosystem
Table 6.1 SWOT Analysis of the Big Data Market 2013-2018
List of Figures
Figure 2.1 Big Data Processing Pipeline - Major Steps and Common Challenges
Figure 2.2 Big Data Visualisation
Figure 3.1 Global Big Data Market Forecast 2013-2018 ($ bn, AGR%)
Figure 4.1 Leading 20 Big Data Companies Market Share 2014 (%)
Figure 5.1 IBM Big Data Market Share 2014 (%)
Figure 5.2 HP Big Data Market Share 2014 (%)
Figure 5.3 Teradata Big Data Market Share 2014 (%)
Figure 5.4 Dell Big Data Market Share 2014 (%)
Figure 5.5 Oracle Big Data Market Share 2014 (%)
Figure 5.6 SAP Big Data Market Share 2014 (%)
Figure 5.7 EMC Big Data Market Share 2014 (%)
Figure 5.8 Cisco Systems Big Data Market Share 2014 (%)
Figure 5.9 PwC Big Data Market Share 2014 (%)
Figure 5.10 Microsoft Big Data Market Share 2014 (%)
Figure 5.11 Accenture Big Data Market Share 2014 (%)
Figure 5.12 Palantir Big Data Market Share 2014 (%)
Figure 5.13 Fusion-io Big Data Market Share 2014 (%)
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10. Contents
Figure 5.14 SAS Institute Big Data Market Share 2014 (%)
Figure 5.15 Splunk Institute Big Data Market Share 2014 (%)
Figure 5.16 Deloitte Big Data Market Share 2014 (%)
Figure 5.17 NetApp Big Data Market Share 2014 (%)
Figure 5.18 Hitachi Big Data Market Share 2014 (%)
Figure 5.19 Opera Solutions Big Data Market Share 2014 (%)
Figure 5.20 CSC Big Data Market Share 2014 (%)
Figure 5.21 Rest of the Companies Big Data Market Share 2014 (%)
Companies Mentioned in This Report
1010data
10gen
Accenture
Accion Labs, Inc.
Actian
Actuate
Acunu
Aerospike
Alacer Technology Solutions
Alteryx
Amazon
Amazon Web Services (AWS)
Apache Software Foundation (ASF)
Apixio
Apple Inc.
ArcSight
Aspera
Atos S.A.
Attivio
Autonomy
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11. Contents
Avanade
Basho
BIConcepts IT Consulting GmbH
Big Data Partnership
Blue Coat
BlueKai
Booz Allen Hamilton
BPSolutions
Brightlight Consulting, Inc.
BTRG
Buckley Data Group LLC
Calpont
Capgemini
Centrifuge Systems
CGI
Cisco Systems
ClickFox
Cloudera
Concord
Contexti
Couchbase
Crowdflower
CSC
Daman Consulting
DataCrunchers
Dataguise
Datameer
DataPop
Datasift
dataspora
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12. Contents
DataStax
DataXu
DDN
Dell
Deloitte
Digital Reasoning
eBay
EcoSolutions Technology Inc.
EMC
Encore Software Services
Ernst & Young (E&Y)
Ethias
Expan
F5 Networks
Facebook
Factual
Findability
Fluidinfo
Focus Business Solutions
Ford
Fractal Analytics
Fujitsu Ltd.
Fusion-io
General Sentiment
GlassHouse Systems Inc.
Global Consulting Solutions LLC
Gnip
GoldBot Consulting
GoodData
Google
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13. Contents
GTRI
Guavus
Hadapt
Hexaware Technologies Inc
Hitachi
Hortonworks
HP
HPCC Systems
Huawei
Hyperpublic
Hyve Solutions
i2
IBM
Infochimps
Infomotion GmbH
Informatica
Information Control Corporation
In-Q-Tel
Instagram
Intel
Intelligent Communication (Intelcom)
IQ Associates
iRhythm
iSoftStone Information Technology(Group) Co., Ltd
ISS Inc.
Jaspersoft
Jibes Data Analytic
Juniper Networks
Kaggle
Karmasphere
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14. Contents
Kinetic Global Markets
Klarna
Knowesis Technology
Kognitio
KPMG
Lattice Engines
Leap Commerce
Lenovo
Level Seven
Lighthouse
Lilien LLC
Lincube Group AB
Linked-In
Logica
LucidWorks
MapR
MarkLogic
McKenney's
Mercedes
Metamarkets
Microsoft
Microstrategy
Middlecon AB
mLogica
MuSigma
Neo Technology
NES
NetApp
NewsCred
NewVantage
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15. Contents
nfrastructure
nPario
OakStream Systems LLC
Offspring Solutions LLC
OpenHeatMap
Opera Solutions
Oracle
Palantir
Palantir Technologies
ParAccel
Paypal
Pentaho
Perficient
Perot Systems
Persistent Systems
Pervasive Software
Philips
Pivotal
Precog
PROTEUS Technologies
PwC
QlikTech
Quantum
Quid
R Square, Inc.
Rackspace
RainStor
ReadyForZero
Recommind
Recorded Future
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17. Contents
Systech Solutions
Systex
Tableau Software
Talend
TamGroup
Tata Consultancy
TCS
Teradata
Teralytics AG
TerraEchos
The Trade Desk
Think Big Analytics
TIBCO Software
Twitter
Vertica Systems
VMware
Voci Technologies Incorporated
WANdisco
WaveStrong
Wavii
Wikipedia
WiPro
WISE MEN
Wonga
Xerox
Yahoo
ZestFinance
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18. Contents
Government Agencies and Other Organizations Mentioned in This
Report
IMEC (Interuniversity Microelectronics Centre)
US CIA (Central Intelligence Agency)
US DoD (Department of Defense)
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19. Top 20 Big Data Companies 2014: Competitive
Landscape Analysis
5.20 CSC Company Overview
Table 5.43 CSC Company Overview 2014 (Total Revenue, Revenue from Big Data, %
Revenue From Big Data, Global Market Share %, HQ, Ticker, Contact, Website)
2014
Total company revenue $bn
$24.5bn
Revenue from Big Data $bn
$0.17bn
% of revenue from Big Data
0.7%
Global big data market share %
1.0%
Headquarters
Virginia, US.
Ticker
CSC
IR Contact
investorrelations@csc.com
Website
www.csc.com
Source: Visiongain 2014
Figure 5.20 CSC Big Data Market Share 2014 (%)
1.0%
Source: Visiongain 2014
Computer Sciences Corporation (CSC) is an American multinational corporation that provides
information technology (IT) services and professional services. CSC offers services such as:
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20. Top 20 Big Data Companies 2014: Competitive
Landscape Analysis
•
IT and business process outsourcing like systems analysis, applications development,
network operations, end-user computing and data centre management;
•
Emerging services such as cloud computing and cybersecurity protection, Infrastructure as a
Service (IaaS), Software as a Service (SaaS), Business Process as a Service (BPaaS),
Platform as a Service (PaaS), Big Data Managed Services and other emerging technologies
and associated service delivery model; and
•
A variety of other IT and professional services, including systems integration, management
consulting, technology consulting and other professional services.
5.20.1 CSC Big Data Analytics Offerings Analysis
See Table 5.46 below for a detailed summary of CSC’s big data analytics offerings.
Table 5.44 CSC Big Data Analytics Offerings
Service/Solution
Description
Aims to modernize users BI environment by providing a BI strategy and
CSC Business Intelligence
renovating users capability to achieve a blend of function, agility and cost
Transformation
required to support business operations and improve organizational
performance.
CSC Data Integration and
Optimization
Combines ETL tools and proprietary methodology to focus on the complex
portions of data transformation, while modern metadata migration engines
enable the bulk of simpler data migration.
CSC Data Warehouse
A well-implemented, integrated data warehouse delivers a single view of
Implementation
core information across all functional departments and analytical systems.
CSC Enterprise Intelligence
Strategy
CSC Information Strategy and
Governance
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EI Strategy has the potential to help capitalize on the volume, variety and
velocity of both internal and external data to gain intelligence and take
effective decisions.
Data strategy, roadmap and high-level business case through consultancy
service that offers prioritized, actionable recommendations to align
information assets to tactical and strategic business objectives.
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21. Top 20 Big Data Companies 2014: Competitive
Landscape Analysis
7. Expert Opinion
7.1 Deloitte
The following interview was conducted in December 2013. Visiongain would like to thank Jo
Coutuer, Partner at Deloitte Belgium, Public Sector Technology and Analytics, for his participation
in this interview, and providing us with an expert insight on the big data ecosystem.
7.1.1 Deloitte Company Background and Involvement in Big Data
Visiongain: Please give us a little background about your company and your big data
service offerings.
Jo Coutuer: Deloitte is an international professional services company with a strong presence in
technology advisory and implementation. Deloitte focusses on transformational projects. These
are projects that lead to a substantial competitive advantage to our clients or projects that have a
strategic significance to our clients’ organisations. One of our technological advisory and
implementation fields is indeed the field of Big Data and Analytics. But before we dive into those
offerings, it is important to understand that Deloitte’s unique value proposition is its multidisciplinary
approach. Whereas Big Data is often defined as a technological challenge by IT companies,
Deloitte approaches the Big Data and Analytics topics both from the business side as well as from
the technological side. We believe that Big Data and Analytics find their reason of existence in the
way they impact our clients’ business, people, processes, ...their bottom line and shareholder
value.
Deloitte is active in three types of Big Data related activities:
1.
Advisory services: we challenge our clients in their existing business models and try to find
the appropriate Big-Data-impacted business models with them. We want to make sure our clients
do not become obsolete. Another way to provide value in this segment is by using Big Data driven
techniques to deliver better insights to our clients. A petrol company asked us to prove they had
the best gas pump network for a specific audience. Instead of proving this with a generic high level
consulting approach, we combined various data sources and calculated hundreds of thousands of
potential client cases and showed the strong points and weak points in the network in a fact based,
analytical manner. The credibility and thus the value of such an advice is much higher than “gut
feeling” advice.
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