What is the value of big data? How does a user get that value?
Before, analysts would have to wait months relying on IT for a new report or make changes to an existing one. Now, analysts are able to shrink that time down to days or even minutes. On top of that, analysts can ask questions that were not possible before. In this webinar, we’ll show you how this analysis is possible and the value that has been achieved by customers.
In this session, you will learn:
How analysts get value out of big data
How to visualize data at every step of analysis
How analysts can do big data analytics without IT, in one product
This webinar featuring Claudia Imhoff, President of Intelligent Solutions & Founder of the Boulder BI Brain Trust (BBBT), Matt Schumpert, Director of Product Management and Azita Martin, CMO at Datameer, will highlight the latest technology trends in extending BI with big data analytics and the top high impact use cases.
Attendees will hear about:
-- The extended architecture for today's modern analytics environment
-- The Internet of Things (IoT) and big data
-- The evolution of analytics – from descriptive to prescriptive
-- High impact use cases as a result of the changing analytics world
Getting Started with Big Data for Business ManagersDatameer
Big Data has become critical to the enterprise because of the massive amount of untapped data sources, and the potential to gain new insights that were previously not possible. So, how to get started with Big Data and Hadoop becomes a question more pertinent than ever before.
Listen to leading analyst at Ovum, Tony Baer, as he discusses answers to the key questions around how to:
Approach Big Data and associated business challenges
-- Identify what types of new insights can be revealed by Big Data
-- Staff for this undertaking and implement the technology necessary to be successful
-- Take the first steps toward getting started with Big Data on Hadoop
Customer Experience: A Catalyst for Digital TransformationCloudera, Inc.
Customer experience is a catalyst in many digital transformation projects. It is why many businesses invest in new technologies and processes to more effectively engage customers, constituents, or employees. The goal of putting digital tools to work in a transformative way is to ensure that data and insights connect people with information and processes that ultimately lead to a better experience for customers. Yet, it demands a modern approach that considers all of the platforms, processes, and data across the customer journey. The goal for many organizations is dynamically maintaining a single source of truth about each customer to drive personalized experiences based on individual preferences and behaviors.
However, businesses today have primarily invested in systems of record. While these systems are critical for managing internal operational processes, they are typically not effective for today's pace of business change. Insight-driven experiences require customer intelligence platforms that can finally create a customer 360. The deeper data and improved algorithms now available let users factor in individual affinity, segment, and a myriad of growing data sources. The result is greater relevance and effectiveness to deliver a differentiated experience that in today’s competitive landscape is not a luxury, but a necessity for survival.
In this session we will address:
3 things to learn:
•Leaders and Laggards of digital transformation
•How to create data-driven customer insights
•The importance of machine learning to uncover hidden insights
How to Avoid Pitfalls in Big Data Analytics WebinarDatameer
Big data analytics is revolutionizing the way businesses are collecting, storing, and more importantly, analyzing data. However, the adoption of a big data analytics solution has its share of failures and false starts.
Watch this webinar to learn how to navigate the most common obstacles of big data analytics.
Datameer and MapR have worked with customers to identify and solve the common pitfalls organizations face when deploying Hadoop-based analytics.
In this webinar, we will show you how to:
• Find the balance between infrastructure and business use cases
• Overcome challenges of using multiple tools that address big data analytics
• Leverage all your resources (data scientists, IT and analysts) most effectively
Datameer 6 is completely re-imagining the user experience for modern BI, helping you deliver new insights faster and more results to your data-hungry business. During this one-hour webinar, we demonstrated all that's new with Datameer 6 and how you can:
Discover answers to a new range of business questions using an iterative, exploratory approach
Find answers faster and deliver more insights with a new faster analytic workflow
Utilize Spark to speed analytic processing time without needing to know technical details
Watch this on-demand webinar, with special guest speaker, Sean Anderson, Senior Product Marketing Manager, from Cloudera, who discusses Cloudera's view of the Hadoop data processing stack and how the market place is benefiting from Spark.
This document discusses how big data is transforming business intelligence. It outlines some of the pains of traditional BI, including maintaining large data warehouses and only considering structured data. The document advocates for an open source approach using Hadoop as an "extended data warehouse" to address these issues. Examples of recent Solocal Group projects involving real-time business analytics and a search power selector are provided. Advice is given on how companies can activate big data projects and start the BI transformation.
Modernizing Architecture for a Complete Data StrategyCloudera, Inc.
The document outlines a presentation about modernizing data strategies. It discusses how companies' relationships with data are changing and the business drivers for adopting big data and analytics. It then provides guidance on building a modern data strategy, emphasizing the importance of people, process, and technology. Specifically, it recommends starting with high-impact use cases, staying agile, and evolving capabilities over time to maximize value from data. The presentation also covers how Hadoop is being used for different workloads in both on-premise and cloud environments.
What is the value of big data? How does a user get that value?
Before, analysts would have to wait months relying on IT for a new report or make changes to an existing one. Now, analysts are able to shrink that time down to days or even minutes. On top of that, analysts can ask questions that were not possible before. In this webinar, we’ll show you how this analysis is possible and the value that has been achieved by customers.
In this session, you will learn:
How analysts get value out of big data
How to visualize data at every step of analysis
How analysts can do big data analytics without IT, in one product
This webinar featuring Claudia Imhoff, President of Intelligent Solutions & Founder of the Boulder BI Brain Trust (BBBT), Matt Schumpert, Director of Product Management and Azita Martin, CMO at Datameer, will highlight the latest technology trends in extending BI with big data analytics and the top high impact use cases.
Attendees will hear about:
-- The extended architecture for today's modern analytics environment
-- The Internet of Things (IoT) and big data
-- The evolution of analytics – from descriptive to prescriptive
-- High impact use cases as a result of the changing analytics world
Getting Started with Big Data for Business ManagersDatameer
Big Data has become critical to the enterprise because of the massive amount of untapped data sources, and the potential to gain new insights that were previously not possible. So, how to get started with Big Data and Hadoop becomes a question more pertinent than ever before.
Listen to leading analyst at Ovum, Tony Baer, as he discusses answers to the key questions around how to:
Approach Big Data and associated business challenges
-- Identify what types of new insights can be revealed by Big Data
-- Staff for this undertaking and implement the technology necessary to be successful
-- Take the first steps toward getting started with Big Data on Hadoop
Customer Experience: A Catalyst for Digital TransformationCloudera, Inc.
Customer experience is a catalyst in many digital transformation projects. It is why many businesses invest in new technologies and processes to more effectively engage customers, constituents, or employees. The goal of putting digital tools to work in a transformative way is to ensure that data and insights connect people with information and processes that ultimately lead to a better experience for customers. Yet, it demands a modern approach that considers all of the platforms, processes, and data across the customer journey. The goal for many organizations is dynamically maintaining a single source of truth about each customer to drive personalized experiences based on individual preferences and behaviors.
However, businesses today have primarily invested in systems of record. While these systems are critical for managing internal operational processes, they are typically not effective for today's pace of business change. Insight-driven experiences require customer intelligence platforms that can finally create a customer 360. The deeper data and improved algorithms now available let users factor in individual affinity, segment, and a myriad of growing data sources. The result is greater relevance and effectiveness to deliver a differentiated experience that in today’s competitive landscape is not a luxury, but a necessity for survival.
In this session we will address:
3 things to learn:
•Leaders and Laggards of digital transformation
•How to create data-driven customer insights
•The importance of machine learning to uncover hidden insights
How to Avoid Pitfalls in Big Data Analytics WebinarDatameer
Big data analytics is revolutionizing the way businesses are collecting, storing, and more importantly, analyzing data. However, the adoption of a big data analytics solution has its share of failures and false starts.
Watch this webinar to learn how to navigate the most common obstacles of big data analytics.
Datameer and MapR have worked with customers to identify and solve the common pitfalls organizations face when deploying Hadoop-based analytics.
In this webinar, we will show you how to:
• Find the balance between infrastructure and business use cases
• Overcome challenges of using multiple tools that address big data analytics
• Leverage all your resources (data scientists, IT and analysts) most effectively
Datameer 6 is completely re-imagining the user experience for modern BI, helping you deliver new insights faster and more results to your data-hungry business. During this one-hour webinar, we demonstrated all that's new with Datameer 6 and how you can:
Discover answers to a new range of business questions using an iterative, exploratory approach
Find answers faster and deliver more insights with a new faster analytic workflow
Utilize Spark to speed analytic processing time without needing to know technical details
Watch this on-demand webinar, with special guest speaker, Sean Anderson, Senior Product Marketing Manager, from Cloudera, who discusses Cloudera's view of the Hadoop data processing stack and how the market place is benefiting from Spark.
This document discusses how big data is transforming business intelligence. It outlines some of the pains of traditional BI, including maintaining large data warehouses and only considering structured data. The document advocates for an open source approach using Hadoop as an "extended data warehouse" to address these issues. Examples of recent Solocal Group projects involving real-time business analytics and a search power selector are provided. Advice is given on how companies can activate big data projects and start the BI transformation.
Modernizing Architecture for a Complete Data StrategyCloudera, Inc.
The document outlines a presentation about modernizing data strategies. It discusses how companies' relationships with data are changing and the business drivers for adopting big data and analytics. It then provides guidance on building a modern data strategy, emphasizing the importance of people, process, and technology. Specifically, it recommends starting with high-impact use cases, staying agile, and evolving capabilities over time to maximize value from data. The presentation also covers how Hadoop is being used for different workloads in both on-premise and cloud environments.
Informatica Becomes Part of the Business Data Lake EcosystemCapgemini
Informatica is now part of the Business Data Lake ecosystem developed by Capgemini and Pivotal. Customers worldwide will now be able to leverage Informatica’s data integration software in addition to Pivotal’s advanced big data, analytics and application software, and Capgemini’s industry and implementation expertise. Informatica will deliver certified technologies for Data Integration, Data Quality and Master Data Management (MDM) to help enterprises distill raw data into actionable insights.
http://www.capgemini.com/resources/the-business-data-lake-delivering-the-speed-and-accuracy-to-solve-your-big-data-problems
Moving from data to insights: How to effectively drive business decisions & g...Cloudera, Inc.
Firms have become obsessed with data. But the key to competitive advantage is not just more or bigger data or big data technology, it is finding actionable insights from all the data as well as embedding insight in processes and applications. This requires a change in your approach - modernized architecture and embedding insights and data in you business decisions It also requires a change in how your people work systematically to find, test and implement insights. In this webinar, Forrester Vice President and Principal Analyst Brian Hopkins will present results from two years of research into these ideas and recommend to attendees how they can get the most out of their data and analytics to drive effective business decisions and gain competitive advantage.
No fewer than 80% have digital transformation at the centre of their corporate strategy with the aim of improving efficiency, driving innovation and becoming more agile. Though it's clear that insight into the data they hold is going to help them get there, many organisations find themselves at a crossroads. Big data, machine learning, data science: these are all initiatives every company knows they should take on in order to evolve their business, yet few know how to tackle the projects for successful outcomes.
Big data is an opportunity for communications service providers (CSPs) to create the intelligence for operating their infrastructures more efficiently, to analyze the success of their services, and to create a better personal experience for their customers.
CSP Top executives, Network and IT managers and Marketing, are eager to exploit the large amount of information to achieve better business decisions. They expect their Chief Technical Officer to provide end-to-end analytic solutions based on the data available in their IT and network infrastructure.
This presentation analyzes the complete value chain that can transform CSPs’ data to knowledge. It covers the sources of information, the data collection tools, the analytic platforms providing quick data access, and finally the business intelligence use cases with the presentation and visualization of the results and predictions.
How to Use Algorithms to Scale Digital BusinessTeradata
Gartner defines digital business as the creation of new business designs by blurring the digital and physical worlds. Digital business creates new business opportunities, but the amount of data generated will eclipse the human ability to process it. Further, many complex decisions will need to be made in timeframes, and at scales, that are impossible by human actors. Gartner analyst Chet Geschickter will explain share advice on how to leverage algorithmic business principles to drive digital business success.
Data is cheap; strategy still matters by Jason LeeData Con LA
Abstract:- What could a Strategy Consulting firm have to do with or say about big data? We see Big Data leading the way on new products but also disrupting our clients business processes and business models. For many clients and big data fans, the temptation is to think big data and machine learning disrupt the need for strategy. Just throw the data in the lake and a bunch of programmers with machine learning fishing poles and we will be done. Here is a rapid-fire review of what really happens Use case 1: Use case 2: Use case 3: What did we learn working with these clients? Strategy still matters: Data is cheap; attention is not. While data and computational power are increasingly plentiful, people have limited attention and energy. Complexity can kill not so much in the model itself but in how it affects processes and decisions. Data is not so cheap after all. We continue to underappreciate data architecture, governance, and engineering. These frequently take up most of the effort required for analytics success. Winning with Big Data is often less about the latest technology platform but in our strategy, culture, organizational capabilities, the way we implement algorithms, how we make decisions with data, and the impacts these have on employees and customers.
CaixaBank is using big data and its partnership with Oracle to develop a new technology platform to improve business and better anticipate customer needs with a 360 degree view of customers. CaixaBank consolidated 17 data marts into one centralized data pool built on Oracle technologies. This has improved customer relationships, employee efficiency, and regulatory reporting. The data pool collects data from various sources to power business use cases like deposits pricing, customized ATM menus, online risk scoring, and online marketing automation.
Understanding Big Data: Strategies to Re-envision Decision-Making
Amy Mayer, Vice President, Capgemini
Oracle Analytics Leader, North America
Presented at Oracle OpenWorld 2012
Best Practices in Implementing Social and Mobile CX for UtilitiesCapgemini
Are you having difficulties in implementing a modern customer experience solution strategy that meets your customers’ needs across all interaction channels, including mobile and social?
This presentation highlights best practices for the design and implementation of effective CX strategies adapted to the utilities industry.
Presented at Oracle OpenWorld 2014 by Bruna Gapo, Oracle's Utilities Industry Director, Ajay Verma, Capgemini's Global Utility Practice Leader, and Victor Jimenez, Capgemini Utilities Executive.
http://www.capgemini.com/oracle
The Hive Think Tank: AI in The Enterprise by Venkat SrinivasanThe Hive
This The Hive Think Tank talk by Venkat Srinivasan, CEO of RAGE Frameworks, focuses on successful applications of AI in the Enterprise. We start with a broad and more inclusive definition of AI in the context of enterprise business processes.
We introduce a taxonomy of AI solution methods that broaden the focus beyond a narrow focus on deep learning based on neural nets. In line with the taxonomy, we present several successful AI applications in use today at major corporations across industries including financial services, manufacturing/retail, professional services, logistics. These applications range from commercial lending, contract review, customer service intelligence, market and competitive intelligence, signals for capital markets, regulatory compliance and others.
Understand Your Customer Buying Journey with Big Data Datameer
Imagine being able to use insights about your customer acquisition journey to design campaigns that improve conversion rates. What if you could identify points of failure along the customer acquisition path or during product usage?
Today more than ever the role of the marketing and product executives have become data-driven. Business executives who leverage data to understand prospect and customer behavior have gained an edge over their peers. Big Data analytics is the key to unlocking insights from your customer behavior data and empowers you to combine and analyze all of your customer interaction data to drive customer acquisition and loyalty.
Traditional data management approaches are inadequate to handle today's data growth and real-time business needs. Siloed technology stacks have become too complex, slow, and expensive. This is driving many enterprises to rethink their data strategies and build self-service platforms that can consolidate different data sources and provide faster, more flexible access to data. An end-to-end data architecture is needed to collapse silos, integrate data, and support real-time analytics and decision making across the business.
Systems of Insights: BI Trends and the Smart Tools of the FutureYellowfin BI
Business intelligence (BI) is at the top of the enterprise agendas as they transform from data driven to insights driven business models. The opportunity created a large, fragmented market of BI vendors, and the older market segmentation models (technology centric vs. user centric, reporting vs. data visualization, on-premise vs. cloud) no longer work. Then how do you select the right BI platform from dozens of contenders who often pitch a very similar proposition? On this webinar you will learn about the:
- Role of BI in the insights-driven business model
- Commoditization of some BI platform features
- Emergence of a new set of key BI platform differentiators
- Demo of 2-3 new features of Yellowfin 7.4
Yellowfin, together with Forrester business intelligence expert Boris Evelson, presented Systems of Insight, as a webinar on October 31, 2017.
For more information visit http://www.yellowfinbi.com
The document profiles Jeroen ter Heerdt and outlines his expertise in areas such as cultural creativity, agile practices, observation, analysis, and helping others bring data under control to derive insights. It then discusses how data and technology can be leveraged to increase operational efficiency, improve customer experiences, and transform business models across various industries from elevators to healthcare to aviation. The document concludes by providing tips for organizations to facilitate a data-driven culture and take advantage of data through initiatives like "Bring Your Own Data."
The document discusses how companies can drive business agility through cloud-based big data analytics. It notes that traditional big data approaches are no longer sufficient due to the increasing volume, variety, and velocity of data. The document outlines a reference architecture for analyzing diverse data sources in real-time and iteratively to gain insights. It emphasizes the need for data products to be responsive to disruption, leverage external data sources, and be resilient through cloud elasticity. Examples from Ford and healthcare are provided to illustrate integrating diverse data for predictive analytics and personalized recommendations.
The document discusses HP's HAVEn big data platform. HAVEn integrates HP technologies like Vertica, Autonomy IDOL, and ArcSight to ingest, analyze, and understand both machine and human data at scale. The platform is designed to process both structured and unstructured data from various sources and provide analytics and visualization capabilities. Examples of companies using HAVEn solutions for log analysis, sensor data analysis, and early warning systems are also presented.
This document discusses IBM's big data and analytics solutions. It describes big data as involving large volumes and varieties of data. The document outlines challenges of traditional IT systems and how new systems of engagement require massive scale, rapid insights, and data elasticity. It promotes investing in IBM's big data and analytics platform, which harnesses all data and analytics paradigms. The platform includes infrastructure, governance, ingestion, warehousing, and analytics capabilities. It is presented as helping organizations be more right more often by understanding what happened, learning from data, discovering current trends, deciding on actions, and predicting outcomes.
The article explains how companies are using Big Data to grow their business. How big data is helping every business in understanding its customers, in improved marketing strategies, and personalized communication.
Dr. Maher salameh - new age of data analyticspromediakw
This document discusses the rise of big data and analytics. It notes that analytics uses data, technology, and quantitative methods to help managers make better decisions. The amount of data is doubling every 18 months due to factors like the internet of things. Analytics needs to evolve to deliver collective insights by engaging users, enabling prediction, and helping users visualize data. Advanced analytics can help anticipate business trends in real-time. The document provides an example of how predictive analytics could be used in customer intelligence. It also notes challenges in detecting meaningful signals in big data and applying predictive algorithms, and how analytics needs to bridge skills gaps.
Informatica Becomes Part of the Business Data Lake EcosystemCapgemini
Informatica is now part of the Business Data Lake ecosystem developed by Capgemini and Pivotal. Customers worldwide will now be able to leverage Informatica’s data integration software in addition to Pivotal’s advanced big data, analytics and application software, and Capgemini’s industry and implementation expertise. Informatica will deliver certified technologies for Data Integration, Data Quality and Master Data Management (MDM) to help enterprises distill raw data into actionable insights.
http://www.capgemini.com/resources/the-business-data-lake-delivering-the-speed-and-accuracy-to-solve-your-big-data-problems
Moving from data to insights: How to effectively drive business decisions & g...Cloudera, Inc.
Firms have become obsessed with data. But the key to competitive advantage is not just more or bigger data or big data technology, it is finding actionable insights from all the data as well as embedding insight in processes and applications. This requires a change in your approach - modernized architecture and embedding insights and data in you business decisions It also requires a change in how your people work systematically to find, test and implement insights. In this webinar, Forrester Vice President and Principal Analyst Brian Hopkins will present results from two years of research into these ideas and recommend to attendees how they can get the most out of their data and analytics to drive effective business decisions and gain competitive advantage.
No fewer than 80% have digital transformation at the centre of their corporate strategy with the aim of improving efficiency, driving innovation and becoming more agile. Though it's clear that insight into the data they hold is going to help them get there, many organisations find themselves at a crossroads. Big data, machine learning, data science: these are all initiatives every company knows they should take on in order to evolve their business, yet few know how to tackle the projects for successful outcomes.
Big data is an opportunity for communications service providers (CSPs) to create the intelligence for operating their infrastructures more efficiently, to analyze the success of their services, and to create a better personal experience for their customers.
CSP Top executives, Network and IT managers and Marketing, are eager to exploit the large amount of information to achieve better business decisions. They expect their Chief Technical Officer to provide end-to-end analytic solutions based on the data available in their IT and network infrastructure.
This presentation analyzes the complete value chain that can transform CSPs’ data to knowledge. It covers the sources of information, the data collection tools, the analytic platforms providing quick data access, and finally the business intelligence use cases with the presentation and visualization of the results and predictions.
How to Use Algorithms to Scale Digital BusinessTeradata
Gartner defines digital business as the creation of new business designs by blurring the digital and physical worlds. Digital business creates new business opportunities, but the amount of data generated will eclipse the human ability to process it. Further, many complex decisions will need to be made in timeframes, and at scales, that are impossible by human actors. Gartner analyst Chet Geschickter will explain share advice on how to leverage algorithmic business principles to drive digital business success.
Data is cheap; strategy still matters by Jason LeeData Con LA
Abstract:- What could a Strategy Consulting firm have to do with or say about big data? We see Big Data leading the way on new products but also disrupting our clients business processes and business models. For many clients and big data fans, the temptation is to think big data and machine learning disrupt the need for strategy. Just throw the data in the lake and a bunch of programmers with machine learning fishing poles and we will be done. Here is a rapid-fire review of what really happens Use case 1: Use case 2: Use case 3: What did we learn working with these clients? Strategy still matters: Data is cheap; attention is not. While data and computational power are increasingly plentiful, people have limited attention and energy. Complexity can kill not so much in the model itself but in how it affects processes and decisions. Data is not so cheap after all. We continue to underappreciate data architecture, governance, and engineering. These frequently take up most of the effort required for analytics success. Winning with Big Data is often less about the latest technology platform but in our strategy, culture, organizational capabilities, the way we implement algorithms, how we make decisions with data, and the impacts these have on employees and customers.
CaixaBank is using big data and its partnership with Oracle to develop a new technology platform to improve business and better anticipate customer needs with a 360 degree view of customers. CaixaBank consolidated 17 data marts into one centralized data pool built on Oracle technologies. This has improved customer relationships, employee efficiency, and regulatory reporting. The data pool collects data from various sources to power business use cases like deposits pricing, customized ATM menus, online risk scoring, and online marketing automation.
Understanding Big Data: Strategies to Re-envision Decision-Making
Amy Mayer, Vice President, Capgemini
Oracle Analytics Leader, North America
Presented at Oracle OpenWorld 2012
Best Practices in Implementing Social and Mobile CX for UtilitiesCapgemini
Are you having difficulties in implementing a modern customer experience solution strategy that meets your customers’ needs across all interaction channels, including mobile and social?
This presentation highlights best practices for the design and implementation of effective CX strategies adapted to the utilities industry.
Presented at Oracle OpenWorld 2014 by Bruna Gapo, Oracle's Utilities Industry Director, Ajay Verma, Capgemini's Global Utility Practice Leader, and Victor Jimenez, Capgemini Utilities Executive.
http://www.capgemini.com/oracle
The Hive Think Tank: AI in The Enterprise by Venkat SrinivasanThe Hive
This The Hive Think Tank talk by Venkat Srinivasan, CEO of RAGE Frameworks, focuses on successful applications of AI in the Enterprise. We start with a broad and more inclusive definition of AI in the context of enterprise business processes.
We introduce a taxonomy of AI solution methods that broaden the focus beyond a narrow focus on deep learning based on neural nets. In line with the taxonomy, we present several successful AI applications in use today at major corporations across industries including financial services, manufacturing/retail, professional services, logistics. These applications range from commercial lending, contract review, customer service intelligence, market and competitive intelligence, signals for capital markets, regulatory compliance and others.
Understand Your Customer Buying Journey with Big Data Datameer
Imagine being able to use insights about your customer acquisition journey to design campaigns that improve conversion rates. What if you could identify points of failure along the customer acquisition path or during product usage?
Today more than ever the role of the marketing and product executives have become data-driven. Business executives who leverage data to understand prospect and customer behavior have gained an edge over their peers. Big Data analytics is the key to unlocking insights from your customer behavior data and empowers you to combine and analyze all of your customer interaction data to drive customer acquisition and loyalty.
Traditional data management approaches are inadequate to handle today's data growth and real-time business needs. Siloed technology stacks have become too complex, slow, and expensive. This is driving many enterprises to rethink their data strategies and build self-service platforms that can consolidate different data sources and provide faster, more flexible access to data. An end-to-end data architecture is needed to collapse silos, integrate data, and support real-time analytics and decision making across the business.
Systems of Insights: BI Trends and the Smart Tools of the FutureYellowfin BI
Business intelligence (BI) is at the top of the enterprise agendas as they transform from data driven to insights driven business models. The opportunity created a large, fragmented market of BI vendors, and the older market segmentation models (technology centric vs. user centric, reporting vs. data visualization, on-premise vs. cloud) no longer work. Then how do you select the right BI platform from dozens of contenders who often pitch a very similar proposition? On this webinar you will learn about the:
- Role of BI in the insights-driven business model
- Commoditization of some BI platform features
- Emergence of a new set of key BI platform differentiators
- Demo of 2-3 new features of Yellowfin 7.4
Yellowfin, together with Forrester business intelligence expert Boris Evelson, presented Systems of Insight, as a webinar on October 31, 2017.
For more information visit http://www.yellowfinbi.com
The document profiles Jeroen ter Heerdt and outlines his expertise in areas such as cultural creativity, agile practices, observation, analysis, and helping others bring data under control to derive insights. It then discusses how data and technology can be leveraged to increase operational efficiency, improve customer experiences, and transform business models across various industries from elevators to healthcare to aviation. The document concludes by providing tips for organizations to facilitate a data-driven culture and take advantage of data through initiatives like "Bring Your Own Data."
The document discusses how companies can drive business agility through cloud-based big data analytics. It notes that traditional big data approaches are no longer sufficient due to the increasing volume, variety, and velocity of data. The document outlines a reference architecture for analyzing diverse data sources in real-time and iteratively to gain insights. It emphasizes the need for data products to be responsive to disruption, leverage external data sources, and be resilient through cloud elasticity. Examples from Ford and healthcare are provided to illustrate integrating diverse data for predictive analytics and personalized recommendations.
The document discusses HP's HAVEn big data platform. HAVEn integrates HP technologies like Vertica, Autonomy IDOL, and ArcSight to ingest, analyze, and understand both machine and human data at scale. The platform is designed to process both structured and unstructured data from various sources and provide analytics and visualization capabilities. Examples of companies using HAVEn solutions for log analysis, sensor data analysis, and early warning systems are also presented.
This document discusses IBM's big data and analytics solutions. It describes big data as involving large volumes and varieties of data. The document outlines challenges of traditional IT systems and how new systems of engagement require massive scale, rapid insights, and data elasticity. It promotes investing in IBM's big data and analytics platform, which harnesses all data and analytics paradigms. The platform includes infrastructure, governance, ingestion, warehousing, and analytics capabilities. It is presented as helping organizations be more right more often by understanding what happened, learning from data, discovering current trends, deciding on actions, and predicting outcomes.
The article explains how companies are using Big Data to grow their business. How big data is helping every business in understanding its customers, in improved marketing strategies, and personalized communication.
Dr. Maher salameh - new age of data analyticspromediakw
This document discusses the rise of big data and analytics. It notes that analytics uses data, technology, and quantitative methods to help managers make better decisions. The amount of data is doubling every 18 months due to factors like the internet of things. Analytics needs to evolve to deliver collective insights by engaging users, enabling prediction, and helping users visualize data. Advanced analytics can help anticipate business trends in real-time. The document provides an example of how predictive analytics could be used in customer intelligence. It also notes challenges in detecting meaningful signals in big data and applying predictive algorithms, and how analytics needs to bridge skills gaps.
The New Enterprise is adopting
new tools and technology that
utilize data, mobilize their
workforce, and increase
collaboration throughout the
organization. In this new report, SVB Analytics examines the underlying industry sectors supporting this new business environment and offers data on venture funding, revenue models and valuations.
This white paper discusses how organizations can transform big data into business value by connecting various data sources, analyzing data at scale, and taking action. It outlines the challenges of dealing with exponentially growing data in today's digital world. The paper introduces Actian's solutions for enabling an "action-driven enterprise" through its DataCloud Platform for invisible integration and ParAccel Platform for unconstrained analytics. These platforms allow organizations to connect diverse data, analyze it without constraints, and automate actions based on insights gleaned from big data analytics. Use cases demonstrate how companies are leveraging Actian's technology to gain competitive advantages.
Big data offers companies a big advantage if they can harness enormous data sets that were previously impossible to process. The document discusses how big data is transforming business models through creative destruction, as more data is created every day from various sources. It provides examples of how companies in various industries like retail, banking, and manufacturing are using big data for customer intimacy, product innovation, and improving operations. Specifically, companies are able to better customize products and services, improve supply chain management, and gain real-time insights from vast amounts of structured and unstructured data.
Keeping pace with technology and big data.pdfClaire D'Costa
How IT companies can bridge the gap between ever-increasing talent needs and ever-changing technology?
In this pdf, you will get to know:
1- The technology's part in the play
2- The widening skills gap
3- Ways to fill up the void
4- Future of Big Data
5- Other useful insights
1) Big data is becoming economically relevant as the volume of data generated and stored grows exponentially. It will transform our lives and become the basis of competition as companies use it to enhance productivity.
2) Leading companies are leveraging big data through advanced analytics to innovate, compete, and capture value across industries like healthcare, manufacturing, and retail. This will create new opportunities and categories of data-focused companies.
3) Consumers stand to significantly benefit from big data applications like smart routing which could save drivers over $500 billion annually through time and fuel savings by 2020.
Big data offers opportunities for companies to gain competitive advantages through improved customer intimacy, product innovation, and operations. The document discusses how various companies are leveraging big data across industries. It notes that 45% of companies have implemented big data initiatives in the past two years and over 90% of Fortune 500 companies will have initiatives underway soon. Harnessing big data's potential requires understanding where it can create value within a company and having the right organizational structure, technology investments, and plan to capture those benefits.
Keynote presentation from IBM Solutions Connect 2013 covering topics such as changing business world today and how technologies can help organisations cope with this change and move forward.
In this white paper, we’ll spread the light on such issues as:
- What big data is
- How data science creates a real value in retail
- 5 big data use-cases revealing how retail companies can turn their customers’ data in action
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.
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.
The document discusses how digital technologies are transforming core company operations across four key areas: manufacturing, capital asset management, supply chains, and product development. It outlines an evolving ecosystem of digital solutions emerging in these areas, with over 40 use cases identified in manufacturing alone. Companies must understand where value lies for their specific needs to develop a roadmap for digital operations that maximizes business benefits.
Primend Ärikonverents - Keynote: Surviving, Differentiating and Dominating on...Primend
Socrates once said “The secret of change is to focus all your energy, not on fighting the old, but on building the new”. Organizations throughout the world must think about the new digital world and evaluate how to get from where they are today to where they need to be in the future! In this presentation we will look into the changing landscape which is driving this 4th Industrial Revolution and present some areas you might like to focus on as you reposition your organization to compete in an increasingly digital world.
Esineja: Mark Torr (Microsoft)
Apigee CEO Chet Kapoor and four great keynote speakers and "CDOs" (Carole McCluskey (Outerwall), Michael Redding (Accenture), Abhi Ingle (AT&T), Aneesh Chopra (Former CTO - USA)) kicked off I ♥ APIs, Apigee’s first user and industry conference in inspiring fashion describing how every business is a digital business and how Chief Digital Officers (whether they have CDO as title on their business card or not) are leading enterprise innovation and transforming physical value networks with digital value networks.
This document discusses the role of data science in digital transformation. It defines digital transformation as applying digital technology to all aspects of society. Data science helps drive digital transformation by analyzing patterns in big data to build models and insights that can transform industries. As sensors and IoT devices proliferate, generating massive amounts of new data, data science is key to extracting value from this data through predictive analytics, customer insights, and other techniques. The document provides examples of how data science helps various industries and business functions like manufacturing, retail, healthcare, and customer experience through real-time insights, forecasting, and other analytics.
Wall Street Tech Conference_2015_Pooneh Mohazzabipooneh mohazzabi
Digital technologies are significantly impacting customer expectations in the banking industry. Customers now expect services to be available anytime, anywhere, personalized, accurate, seamless and continuously innovative. This has disrupted traditional banking models and forced banks to rethink their value proposition and competitive advantage. Additionally, digital natives entering the market will require products tailored to their needs and preferences. Banks must embrace digital innovation to both drive revenue growth and reduce costs in order to remain competitive in this new environment.
Why Big Data is a Top Priority for Enterprises - Infographics by RapidValueRapidValue
This is an info-graphics which tells why Big Data is a top priority for enterprises. It also predicts the size of the Big Data industry By 2017 and the industries most likely to invest in Big Data. The info-graphic also talks about the most important areas of Big Data usage and use cases for various industries.
Unlocking Value of Data in a Digital AgeRuud Brink
InfoGraphic about Intelligence Hubs as accelerator of the Digital organisation. Five steps how you could think big, and act small to unlock value of Data in your organisation. Contact me for the office A0 poster.
Manufacturing is changing at a rapid pace and Industrial Tech startups are popping up everywhere.
What do you need to benefit from these developments and to ride the wave of change in manufacturing.
Similar to The State of Big Data Adoption: A Glance at Top Industries Adopting Big Data & Top Use Cases (20)
Analyzing Unstructured Data in Hadoop WebinarDatameer
Unstructured data is growing 62% per year faster than structured data. According to Gartner, data volumes are set to grow 800% in aggregate over the next 5 years, and 80% of it will be unstructured data.
This on-demand webinar will highlight and discuss:
How applying big data analytics to unstructured data can help you gain richer, deeper and more accurate insights to gain competitive advantages
The sources of unstructured data which include email, social media platforms, CRM systems, call center platforms (including notes and speech-to-text transcripts), and web scrapes
How monitoring the communications of your customers and prospects enables you to make time-sensitive decisions and jump on new business opportunities
In Big Data projects, analysts often spend 80% of their time preparing data for analysis. In addition, users don’t have a good understanding of their data quality.
Today there are multiple tools that assist with integration, data preparation, analysis and visualization. However, data quality continues to be one of the biggest challenges businesses face when deploying big data analytics.
People need to profile their data and ensure data quality to get accurate insights and make informed business decisions. Join Datameer as we address these pain points with visualizations at every step.
This webinar will highlight and showcase:
-How visual data profiling reduces the guesswork in the data wrangling process
-Enhanced interactive data mining capabilities reduce time to insight
-A demonstration of the new 4.0 features and functions
Datameer offers the first data analytics solution built on Hadoop that helps business users access, analyze and use massive amounts of data. Hadoop provides key breakthroughs over traditional solutions by enabling linear scalability from 1 to 4000 servers, overcoming limitations of storage and compute through use of commodity hardware and open source software, and not requiring rigid data models or specialized hardware or software.
Datameer offers the first data analytics solution built on Hadoop that helps business users access, analyze and use massive amounts of data. Hadoop provides key breakthroughs over traditional solutions by enabling linear scalability from 1 to 4000 servers, overcoming limitations of storage and compute through use of commodity hardware and open source software, and not requiring rigid data models or specialized hardware or software.
Online Fraud Detection Using Big Data Analytics WebinarDatameer
With the ease and convenience of the internet, shopping online has never been faster and simpler than with a click of a button. But with this convenience, lurks the consequence for online fraud.
Companies and merchants lose valuable time and money to online thieves scamming the web. Learn how to identify patterns with Datameer and Trustev as they demonstrate how to take control of the situation and combat combat against suspicious activity by using big data analytics.
In this webinar, you will take away:
*An understanding of the complexities and challenges of online fraud today
*Best practices for merchants and companies to protect themselves from fraud
*A demonstration of fraud reporting, prevention and prediction
Instant Visualizations in Every Step of AnalysisDatameer
Surveys reveal that concerns about data quality can create barriers for companies deploying Analytics and BI initiatives.
How can you readily identify and correct data quality issues at every step of your big data analysis to ensure accurate insights into customer behavior? In this webcast, we'll discuss how IT and business users can leverage self-service visualizations to quickly spot and correct data anomalies throughout the analytic process.
In this webinar, you will learn how to:
-Continuously visualize a profile of your data to identify inconsistencies, incompleteness and duplicates in your data
-Visualize machine learning and data mining, including clustering, decision tree analysis, column correlations and recommendations
-Create self-service visualizations for business and IT users
Customer Case Studies of Self-Service Big Data AnalyticsDatameer
Self-service tools empowers business users to rapidly gather, analyze and visualize data from broad, diverse data sources. Analyzing these sources provides new answers and new business opportunities for those smart enough to answer the new questions.
Free your IT staff from the need to respond to routine report requests. Business users can now rely on the rapid delivery of advanced self-service BI and data visualization capabilities to solve complex problems and capitalize on new opportunities.
From these slides, you will learn:
-Customer examples and return on investment from self-service big data analytics
-How business analysts can take advantage of Machine Learning
-Best practices in self-service big data analytics
The document describes a proof of concept (POC) technical solution for a real estate company to analyze large amounts of web activity and customer data. The POC proposed loading one year of data from six tables into an Amazon cloud Hadoop environment and using Datameer for data discovery and analytics. The goals were to set up the cloud environment, load the search analytics data, and allow the business to perform analytics with acceptable performance and gain new insights. High-level and detailed descriptions of the technical solution are provided.
How do you protect the data in big data analytics projects?
As big data initiatives focus on volume, velocity or variety of data, often overlooked in the big data project is the security of the data. This is especially important for financial services, healthcare and government or anytime sensitive data is analyzed.
This webinar highlights:
*Hadoop security landscape
*Hadoop encryption, masking, and access control
*Customer examples of securing hadoop environments
You can view the full presentation of this webinar here: http://info.datameer.com/Slideshare-Fighting-Fraud-this-Holiday-Season.html
In 2012, retailers lost $3.5 billion in revenue to online fraud. These losses spike by a substantial estimated 20% during the holiday season.
Join Datameer and Hortonworks in this webinar to learn how Big Data Analytics can be used to identify new fraud schemes during peak fraud season.
In this webinar, you will learn about:
current challenges in identifying fraud
what to look for in a big data solution addressing fraud
how big data analytics can identify credit card fraud
best practices
Complement Your Existing Data Warehouse with Big Data & HadoopDatameer
To view the full webinar, please go to: http://info.datameer.com/Slideshare-Complement-Your-Existing-EDW-with-Hadoop-OnDemand.html
With 40% yearly growth in data volumes, traditional data warehouses have become increasingly expensive and challenging.
Much of today’s new data sources are unstructured, making the structured data warehouse an unsuitable platform for analyses. As a result, organizations now look at Hadoop as a data platform to complement existing BI data warehouses, and a scalable, flexible and cost-effective solution for data storage and analysis.
Join Datameer and Cloudera in this webinar to discuss how Hadoop and big data analytics can help to:
-Get all the data your business needs quickly into one environment
Shorten the time to insight from months to days
Extend the life of your existing data warehouse investments
Enable your business analysts to ask and answer bigger questions
Lean Production Meets Big Data: A Next Generation Use CaseDatameer
The document discusses how big data and analytics can help optimize business processes using lean principles. It provides an overview of lean production concepts like identifying and eliminating waste. Big data is presented as a new approach to gain insights from process data that can help pinpoint improvement opportunities. The speaker demonstrates how Datameer's software allows users to easily analyze data from multiple sources and measure key performance indicators to drive continuous process improvements.
Watch the recorded event at: http://info.datameer.com/Slideshare-
Economics-SQL-Hadoop.html
As organizations clamor to utilize their new investments in Hadoop ecosystems AND leverage their existing analytical infrastructures, many rush to integrate SQL as a data access layer to leverage existing skill sets and get started faster.
However, this approach relegates Hadoop to a data management and processing platform rather than the storage and compute engine optimized for analytical workloads it was purpose-built to be.
These slides by EMA and Datameer, will discuss the technical limitations of SQL on Hadoop and propose alternative ways to fully maximize Hadoop investments.
You will understanding:
*how SQL negates the inherent benefits of Hadoop
*why technological paradigm changes can sometimes be good
*use cases when SQL on Hadoop makes sense
Top 3 Considerations for Machine Learning on Big DataDatameer
This document discusses considerations for machine learning on big data. It provides background on speakers Karen Hsu and Elliott Cordo. It then covers drivers and challenges of big data, including how companies like Amazon and Netflix have leveraged big data analytics. Alternatives to machine learning on big data like data mining, traditional BI, and visualization are discussed. Example use cases and key criteria around ease of use and quality for algorithms like clustering, column dependencies, and decision trees are presented. Best practices for machine learning on big data are provided for clustering, recommendations, and overall analytics processes. The document concludes with a polling question and call to action.
Best Practices for Big Data Analytics with Machine Learning by DatameerDatameer
Don't forget! You can watch the full Datameer recording here:
http://info.datameer.com/Online-Slideshare-Big-Data-Analytics-Machine-Learning-OnDemand.html
Learn through industry use cases, how to empower users to identify patterns & relationships for recommendations using big data analytics.
How to do Data Science Without the ScientistDatameer
1. The document discusses making data science and analytics easier and more accessible for non-experts. It outlines challenges with traditional tools like requiring coding skills, long cycle times, and siloed data and applications.
2. The author proposes simplifying data integration and preparation, generating rather than writing code, and moving computation to the data to address these challenges.
3. A demo is presented of Datameer's product which aims to simplify and speed up the analytics process for users without advanced data science skills.
How to do Predictive Analytics with Limited DataDatameer
http://www.datameer.com It is frustrating: even with petabytes of data on a Hadoop cluster, one still encounters situations where there’s a lack of key data for a wide variety of big data analytic use cases. You might have billions of clicks on your web site, but only a few users choose to rate a product. There might be millions of text documents on your cluster, but it is too expensive to have someone categorize more than a tiny fraction of them. In principle, this is where predictive modeling could help. For instance, one could learn a model to predict user ratings so you can better serve product recommendations based on those expected ratings. Or, one could create a model to automatically categorize text documents, saving countless hours and dollars. The main problem is that there is only a limited amount of training material (i.e. user ratings, categorized documents) and it is thus hard to generate good models.
As it turns out, recent research on machine learning techniques has found a way to deal effectively with such situations with a technique called semi-supervised learning. These techniques are often able to leverage the vast amount of related, but unlabeled data to generate accurate models. In this talk, we will give an overview of the most common techniques including co-training regularization. We first explain the principles and underlying assumptions of semi-supervised learning and then show how to implement such methods with Hadoop.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
The State of Big Data Adoption: A Glance at Top Industries Adopting Big Data & Top Use Cases
1. Companies across a variety of industries are leveraging Big Data as a competitive advantage, with
Financial Services taking the lead followed by Technology, Telecommunications, and Retail.
0 25 50 75 100
18.5%
17.5%
27.3%
11.1%
0 25 50 75 100
Big Data enables data discovery, helping you find new insights and ask questions you never
knew to ask. Integrate and analyze structured and unstructured data across all channels to
better understand and discover the customer journey, find new revenue streams, gain huge
operational efficiencies, and more.
BIG DATA “USE CASES” WITHIN BUSINESSES
Around the world and across industries,
tech advancements are enabling companies
to take advantage of Big Data’s offerings.
BUSINESSES INVESTING IN BIG DATA, BY REGION
BIG DATA USAGE, BY INDUSTRY
Currently
Investing
Planning to Invest
within a Year
Investment
Increase
Total
Investing Financial Services
Government 7%
Health Care 7%
Advertising & Entertainment 6%
3%
Technology
3%
Telecommunications
22%
Retail
6%
Gaming
9%
Data Services
1%
Energy & Utilities
3%
SI Consulting
16%
Shipping
14%
Transportation 1%
NORTH AMERICA
EUROPE, MIDDLE
EAST, AND AFRICA
ASIA/PACIFIC
LATIN AMERICA
CUSTOMER ANALYTICS
• Increase customer acquisition
• Reduce churn
• Increase revenue per customer
• Improve existing products
OPERATIONAL ANALYTICS
• Industrial monitoring and optimization
• Supply chain efficiency
• IT operation analytics
• Network planning and optimization
NEW PRODUCT &
SERVICE INNOVATION
• Integrated analytics
• Data-driven new products
• Data-improved service offerings
48%
*Adds to 101% due to rounding
37.8%
26.8%
25.6%
17.8%
Customer Analytics
21% Operational Analytics
12% Fraud & Compliance
10% New Product & Service Innovation
10% Enterprise Data Warehouse Optimization
WHO’S USING IT?
TOP HIGH-IMPACT USE CASES
48.9%
65.2%
106.6%
62.3%
0 25 50 75 100
56.3%
44.3%
52.9%
28.9%
0 25 50 75 100
DATA
BIG
A COMPETITIVE WEAPON
FOR THE ENTERPRISE
Big Data is all around us, produced across all digital processes. Every
day, the number of exabytes (1 billion gigabytes) created grows
exponentially. In fact, 90% of the world’s data has been generated in
just the past 2 years.
The Global Hadoop* Market
$50.2B
by 2020
$1.5B
in 2012
Petabytes of data are generated every minute through mobile,
digital ads, social media, Web logs, electronic devices, and sensors
that can be combined and analyzed to give companies insights
never before revealed in siloed reports. Enterprises can use this
available data to gain new understandings of their customers’
behavior and internal operations.
IS BIG DATA RIGHT FOR YOU?
As technology grows, Big Data will become crucial in understanding your customer, company, and
industry. Enterprises that leverage and combine data from multiple sources will gain a deeper
understanding of their customer interactions, including what gets them to spend more time with their
product, what causes churn and, ultimately, how to improve purchasing decisions. Companies that
*Hadoop is an open source, low-cost storage and compute
platform for Big Data.
properly interpret their Big Data will get faster insight to boost production efficiency and develop new
data-driven products and services, which will help them gain a competitive edge over their peers. SOURCES: Datameer, sciencedaily.com, thegovlab.org,
experfy.com, alliedmarketresearch.com