Big Data Predictive Analytics in Trading & Asset Management - slides and key take-aways from keynote speech at Big Data Day AIM/KTH Royal Institute of Technology, Stockholm
The term Big Data is commonly associated with the three Vs that define properties or dimensions, Volume, Variety and Velocity. Volume refers to the amount of data; variety relates to the number of types of data and velocity refers to the speed of data processing.
How Big Data technology affects businesses?NexSoftsys
Top 5 big data technology for a modern business to manage supply chains, quickly understand the business customer’s requirements and identify the fake transaction.
Personalized News and Video Recomendation System at LinkSureLeanne Hwee
In recent years, the Internet industry has shifted more and more towards digital content distribution through online services. This presentation provides an overview of the overall system design and architecture of LinkSure News and Video Recommendations, the challenges encountered in practice, and the lessons learned from the production deployment of these systems at LinkSure. Specifically, we will highlight how news selection and personalisation of recommendations are formulated and addressed at LinkSure. By presenting our experiences in applying techniques at the intersection of recommender systems, information retrieval, machine learning, and statistical modelling in a large-scale industrial setting and highlighting the open problems, we hope to stimulate further research and collaborations.
This document discusses how big data and data science are transforming marketing. It defines big data as methods and technologies for integrating, storing, and analyzing poly-structured data at large scale. It notes that big data solutions address gaps in the market and enable next-generation retailers to track individual customer behavior in real time. The document promotes an upcoming seminar on data science and the growing demand for data scientists.
Generating actionable consumer insights from analytics - Telekom R&DMerlien Institute
This document provides information about a presentation on generating actionable consumer insights from analytics given at Insight Valley Asia 2013 in Bangkok, Thailand. The presentation discusses trends in information and data sources, challenges in implementing big data and analytics, opportunities in using richer data sources, and use cases in healthcare and telecommunications including customer care, genomic analysis, and high performance analytics. The presentation also reviews security challenges in big data implementation.
Business Intelligence, where is the innovation?ALTIC Altic
Business Intelligence trends, innovation, feedback of our R&D project. Serious matters like Big Data, GPU, Datavisualization! OW2 Con'. Altic presentation;
Annual Big Data Landscape prepared by FIrstMark. Check out full blog post: "Is Big Data Still a Thing"? at http://mattturck.com/2016/02/01/big-data-landscape/
The term Big Data is commonly associated with the three Vs that define properties or dimensions, Volume, Variety and Velocity. Volume refers to the amount of data; variety relates to the number of types of data and velocity refers to the speed of data processing.
How Big Data technology affects businesses?NexSoftsys
Top 5 big data technology for a modern business to manage supply chains, quickly understand the business customer’s requirements and identify the fake transaction.
Personalized News and Video Recomendation System at LinkSureLeanne Hwee
In recent years, the Internet industry has shifted more and more towards digital content distribution through online services. This presentation provides an overview of the overall system design and architecture of LinkSure News and Video Recommendations, the challenges encountered in practice, and the lessons learned from the production deployment of these systems at LinkSure. Specifically, we will highlight how news selection and personalisation of recommendations are formulated and addressed at LinkSure. By presenting our experiences in applying techniques at the intersection of recommender systems, information retrieval, machine learning, and statistical modelling in a large-scale industrial setting and highlighting the open problems, we hope to stimulate further research and collaborations.
This document discusses how big data and data science are transforming marketing. It defines big data as methods and technologies for integrating, storing, and analyzing poly-structured data at large scale. It notes that big data solutions address gaps in the market and enable next-generation retailers to track individual customer behavior in real time. The document promotes an upcoming seminar on data science and the growing demand for data scientists.
Generating actionable consumer insights from analytics - Telekom R&DMerlien Institute
This document provides information about a presentation on generating actionable consumer insights from analytics given at Insight Valley Asia 2013 in Bangkok, Thailand. The presentation discusses trends in information and data sources, challenges in implementing big data and analytics, opportunities in using richer data sources, and use cases in healthcare and telecommunications including customer care, genomic analysis, and high performance analytics. The presentation also reviews security challenges in big data implementation.
Business Intelligence, where is the innovation?ALTIC Altic
Business Intelligence trends, innovation, feedback of our R&D project. Serious matters like Big Data, GPU, Datavisualization! OW2 Con'. Altic presentation;
Annual Big Data Landscape prepared by FIrstMark. Check out full blog post: "Is Big Data Still a Thing"? at http://mattturck.com/2016/02/01/big-data-landscape/
This document discusses big data and how it is used in modern marketing. It defines big data as large, diverse, and unstructured data that requires new technologies and specialists to analyze. These specialists, called data scientists, use skills such as domain knowledge, problem solving, communication, curiosity, technology expertise, and analytics to understand business goals and improve processes. The document also gives examples of how big data is used in marketing, such as tracking consumer behavior across devices and channels to optimize campaigns. Real-time bidding systems that target ads are mentioned as one application.
Big data landscape v 3.0 - Matt Turck (FirstMark) Matt Turck
This document provides an overview of the big data landscape, covering infrastructure, databases, analytics platforms, applications, industries utilizing big data, and areas of the big data field like machine learning, data visualization, and artificial intelligence. It was created by Matt Turck, Sutian Dong, and FirstMark Capital to map the current state of big data in version 3.0.
Bigdata Landscape and Competitive IntelligenceJithin S L
The big data market is expected to grow from $28.65 billion in 2016 to $66.79 billion by 2021, attaining a CAGR of 18.45%. Several leading consulting firms offer big data services including data management, analytics, infrastructure setup, and case studies in industries like financial services, healthcare, and telecommunications. Success stories demonstrate improved insights, fraud detection, and optimization through big data transformations.
Building Innovative Data Products in a Banking EnvironmentBig-Data-Summit
En esta sesión se explicarán algunos de los retos y amenazas a los que se enfrentan el entorno financiero derivados de la necesaria transformación digital. Durante la conferencia se expondrán casos de uso reales de proyectos desarrollados por los equipos de analítica de BBVA que demuestran el potencial de los datos para generar productos que agregan valor a la relación con los clientes y contribuyen a solventar sus necesidades.
Mona Vernon - Using big data to crack B2B marketsStartupfest
Startupfest 2014 - "Techcrunch's Alex Williams once said, ""While the enterprise can be as boring as hell, the whole goddamn thing is paved with gold."" But how should an aspiring young startup crack open business-to-business markets that are insular, uncomfortable with experimentation, and intolerant of the kind of rapid iteration that fuels innovation?
As it turns out, there are plenty of ways for start ups to innovate in the business to business space, and many of them come from the world of Big Data. In this session, Thomson Reuters' Mona Vernon will look at how organizations of all sizes can leverage abundant data to transform markets. You'll learn:
- how big companies innovate
- why working with large corporations is different than selling directly to the consumers
- The advantages of working with large companies to solve their Big Data challenges
If you're a B2B-focused startup, you can't afford to miss this session."
This document discusses big data and the growing need for professionals with big data skills. It defines big data as large in size, quantity, and variety. The 4Vs of big data are described as volume, velocity, variety, and veracity. Organizations can take data from any source, analyze it to find answers, and make smarter business decisions through cost reductions, time reductions, new product development, and optimized offerings. In-demand big data skills include SQL, business intelligence, data analysis, data warehousing, data management, and extract-transform-load developers. The role of data scientist has seen a 15,000% increase in job postings from 2011-2012 due to the need to extract meaningful insights from raw data
Check out viewpoints from industry experts about what innovations are driving change in retail. Get insights into how you can capitalize on these new technologies.
For more from SAP Hybris and Retail, please see: https://hybris.com/en/solutions/industries/retail
Big Data and Mobile Recruitment - Irish Recruiters Conf Dec 2012James Mailley
The document discusses big data and mobile recruitment. It outlines that big data is characterized by volume, variety, velocity, and veracity. It is growing exponentially, with 5 quintillion bytes of data created every 2 days from various sources like social media, sensors, and business. This huge amount of diverse data in many formats requires improved technology to process and store it quickly. However, a challenge is that only 2 in 3 business leaders trust the data they use for decisions due to issues of data quality as the number of sources grows. The document then discusses how big data and mobile can be leveraged for recruitment, with most companies not having mobile-enabled career sections on their websites. It poses questions for discussion on how big data and
[Keynote HP] Guido Pezzin - Big Data - from theory to practice with the simpl...Codemotion
HP Haven OnDemand provide you with options to explore and analyze massive volumes of business data, machine and sensor data, as well as unstructured rich human information including text, audio, and even video. It includes HP Haven OnHadoop the most comprehensive array of SQL functions with any Hadoop distribution.
Learning Analytics Medea Webinar, part 1erikwoning
This document provides an introduction to big data and learning analytics. It discusses how big data involves high-volume, high-velocity, and high-variety information assets that require cost-effective forms of information processing to provide insights. It also explains that big data can be used for learning analytics to create analytics and provide insights from educational data, while acknowledging challenges around privacy and surveillance.
Check out how big data is proving invaluable to finance. Here is the top 10 big data trends in finance. Big data place a vital role in analysing the feeds, Predictive models, forecasts & assess trading impacts
Gianluigi Viganò - How to use HP HEAVEN-on-demand functions for Big Data appsCodemotion
HP Haven, the industry’s first comprehensive, scalable, open, and secure platform for Big Data analytics enables you to deliver actionable insight where and when it is needed to drive superior business outcomes and gain competitive advantage. It includes HP Haven OnHadoop the most comprehensive array of SQL functions with any Hadoop distribution.
This document provides an overview of big data, including its definition, sources, databases, and analytics. It defines big data as large datasets greater than terabytes in size that are increasingly being collected from various sources such as science, social media, government and more. It notes that most data is unstructured. It also discusses the evolution of databases from relational SQL databases to non-relational NoSQL databases and Hadoop. Finally, it outlines the major tools and technologies used for big data analytics, including MapReduce, Hadoop, and machine learning.
What is big data ? | Big Data ApplicationsShilpaKrishna6
Big data is similar to ‘small data’ but bigger in size. It is a term that describes the large volume of data both structured and unstructured. Big data generates value from the storage and processing of very large quantities of digital information that cannot be analyzed with traditional computing techniques
Big data analytics involves analyzing large volumes of data from multiple sources that are dynamically linked. It provides opportunities for better business and healthcare intelligence through targeted efforts. However, it also poses risks such as potential data breaches and loss. Controls like access logging and monitoring, encryption, and automated scanning are important to manage these risks. Analytics approaches include descriptive, diagnostic, predictive, and prescriptive methods. Police departments are starting to use predictive analytics software to generate individual and area threat scores based on various data sources, which raises privacy concerns. Staffing specialist skills and ensuring data quality are important for organizations using big data analytics.
Big data refers to large and complex data sets that are difficult to manage and analyze using traditional data management tools. It is generated from various sources like social media, scientific instruments, mobile devices, and sensor technology. Big data provides opportunities for insights and smart solutions but also poses challenges in processing, analyzing, and gaining insights from such large volumes of data. For managers in India, big data is highly relevant as the Indian analytics industry is growing rapidly and is expected to reach $16 billion by 2025, with big data being a major driver of growth in industries. Digitalization is also expanding the big data market in India as internet and smartphone usage increases across more regions of the country.
Organizations that are most likely to need big data management and analytical tools include:
- Government agencies that collect and store large amounts of data on citizens like national libraries, DMVs, tax authorities, etc. They need to efficiently manage and analyze this data.
- Security and law enforcement agencies that collect criminal records, complaints, public records etc. Analyzing these large datasets can help detect patterns and trends.
- Industries that generate massive amounts of operational data like utilities, telecom, transportation, retail etc. This data if analyzed can help optimize operations and improve customer experience.
- Market research and customer analytics companies that collect consumer surveys, website usage data etc. from many countries. Big data tools help analyze customer sentiment and
Dokumen tersebut memberikan penjelasan singkat tentang data analytics untuk game, termasuk pengertian data analytics, kegunaan dan manfaatnya untuk game, cara mengumpulkan dan mengolah data, serta studi kasus yang pernah dilakukan oleh tim Publishing Agate Studio.
This document discusses big data and how it is used in modern marketing. It defines big data as large, diverse, and unstructured data that requires new technologies and specialists to analyze. These specialists, called data scientists, use skills such as domain knowledge, problem solving, communication, curiosity, technology expertise, and analytics to understand business goals and improve processes. The document also gives examples of how big data is used in marketing, such as tracking consumer behavior across devices and channels to optimize campaigns. Real-time bidding systems that target ads are mentioned as one application.
Big data landscape v 3.0 - Matt Turck (FirstMark) Matt Turck
This document provides an overview of the big data landscape, covering infrastructure, databases, analytics platforms, applications, industries utilizing big data, and areas of the big data field like machine learning, data visualization, and artificial intelligence. It was created by Matt Turck, Sutian Dong, and FirstMark Capital to map the current state of big data in version 3.0.
Bigdata Landscape and Competitive IntelligenceJithin S L
The big data market is expected to grow from $28.65 billion in 2016 to $66.79 billion by 2021, attaining a CAGR of 18.45%. Several leading consulting firms offer big data services including data management, analytics, infrastructure setup, and case studies in industries like financial services, healthcare, and telecommunications. Success stories demonstrate improved insights, fraud detection, and optimization through big data transformations.
Building Innovative Data Products in a Banking EnvironmentBig-Data-Summit
En esta sesión se explicarán algunos de los retos y amenazas a los que se enfrentan el entorno financiero derivados de la necesaria transformación digital. Durante la conferencia se expondrán casos de uso reales de proyectos desarrollados por los equipos de analítica de BBVA que demuestran el potencial de los datos para generar productos que agregan valor a la relación con los clientes y contribuyen a solventar sus necesidades.
Mona Vernon - Using big data to crack B2B marketsStartupfest
Startupfest 2014 - "Techcrunch's Alex Williams once said, ""While the enterprise can be as boring as hell, the whole goddamn thing is paved with gold."" But how should an aspiring young startup crack open business-to-business markets that are insular, uncomfortable with experimentation, and intolerant of the kind of rapid iteration that fuels innovation?
As it turns out, there are plenty of ways for start ups to innovate in the business to business space, and many of them come from the world of Big Data. In this session, Thomson Reuters' Mona Vernon will look at how organizations of all sizes can leverage abundant data to transform markets. You'll learn:
- how big companies innovate
- why working with large corporations is different than selling directly to the consumers
- The advantages of working with large companies to solve their Big Data challenges
If you're a B2B-focused startup, you can't afford to miss this session."
This document discusses big data and the growing need for professionals with big data skills. It defines big data as large in size, quantity, and variety. The 4Vs of big data are described as volume, velocity, variety, and veracity. Organizations can take data from any source, analyze it to find answers, and make smarter business decisions through cost reductions, time reductions, new product development, and optimized offerings. In-demand big data skills include SQL, business intelligence, data analysis, data warehousing, data management, and extract-transform-load developers. The role of data scientist has seen a 15,000% increase in job postings from 2011-2012 due to the need to extract meaningful insights from raw data
Check out viewpoints from industry experts about what innovations are driving change in retail. Get insights into how you can capitalize on these new technologies.
For more from SAP Hybris and Retail, please see: https://hybris.com/en/solutions/industries/retail
Big Data and Mobile Recruitment - Irish Recruiters Conf Dec 2012James Mailley
The document discusses big data and mobile recruitment. It outlines that big data is characterized by volume, variety, velocity, and veracity. It is growing exponentially, with 5 quintillion bytes of data created every 2 days from various sources like social media, sensors, and business. This huge amount of diverse data in many formats requires improved technology to process and store it quickly. However, a challenge is that only 2 in 3 business leaders trust the data they use for decisions due to issues of data quality as the number of sources grows. The document then discusses how big data and mobile can be leveraged for recruitment, with most companies not having mobile-enabled career sections on their websites. It poses questions for discussion on how big data and
[Keynote HP] Guido Pezzin - Big Data - from theory to practice with the simpl...Codemotion
HP Haven OnDemand provide you with options to explore and analyze massive volumes of business data, machine and sensor data, as well as unstructured rich human information including text, audio, and even video. It includes HP Haven OnHadoop the most comprehensive array of SQL functions with any Hadoop distribution.
Learning Analytics Medea Webinar, part 1erikwoning
This document provides an introduction to big data and learning analytics. It discusses how big data involves high-volume, high-velocity, and high-variety information assets that require cost-effective forms of information processing to provide insights. It also explains that big data can be used for learning analytics to create analytics and provide insights from educational data, while acknowledging challenges around privacy and surveillance.
Check out how big data is proving invaluable to finance. Here is the top 10 big data trends in finance. Big data place a vital role in analysing the feeds, Predictive models, forecasts & assess trading impacts
Gianluigi Viganò - How to use HP HEAVEN-on-demand functions for Big Data appsCodemotion
HP Haven, the industry’s first comprehensive, scalable, open, and secure platform for Big Data analytics enables you to deliver actionable insight where and when it is needed to drive superior business outcomes and gain competitive advantage. It includes HP Haven OnHadoop the most comprehensive array of SQL functions with any Hadoop distribution.
This document provides an overview of big data, including its definition, sources, databases, and analytics. It defines big data as large datasets greater than terabytes in size that are increasingly being collected from various sources such as science, social media, government and more. It notes that most data is unstructured. It also discusses the evolution of databases from relational SQL databases to non-relational NoSQL databases and Hadoop. Finally, it outlines the major tools and technologies used for big data analytics, including MapReduce, Hadoop, and machine learning.
What is big data ? | Big Data ApplicationsShilpaKrishna6
Big data is similar to ‘small data’ but bigger in size. It is a term that describes the large volume of data both structured and unstructured. Big data generates value from the storage and processing of very large quantities of digital information that cannot be analyzed with traditional computing techniques
Big data analytics involves analyzing large volumes of data from multiple sources that are dynamically linked. It provides opportunities for better business and healthcare intelligence through targeted efforts. However, it also poses risks such as potential data breaches and loss. Controls like access logging and monitoring, encryption, and automated scanning are important to manage these risks. Analytics approaches include descriptive, diagnostic, predictive, and prescriptive methods. Police departments are starting to use predictive analytics software to generate individual and area threat scores based on various data sources, which raises privacy concerns. Staffing specialist skills and ensuring data quality are important for organizations using big data analytics.
Big data refers to large and complex data sets that are difficult to manage and analyze using traditional data management tools. It is generated from various sources like social media, scientific instruments, mobile devices, and sensor technology. Big data provides opportunities for insights and smart solutions but also poses challenges in processing, analyzing, and gaining insights from such large volumes of data. For managers in India, big data is highly relevant as the Indian analytics industry is growing rapidly and is expected to reach $16 billion by 2025, with big data being a major driver of growth in industries. Digitalization is also expanding the big data market in India as internet and smartphone usage increases across more regions of the country.
Organizations that are most likely to need big data management and analytical tools include:
- Government agencies that collect and store large amounts of data on citizens like national libraries, DMVs, tax authorities, etc. They need to efficiently manage and analyze this data.
- Security and law enforcement agencies that collect criminal records, complaints, public records etc. Analyzing these large datasets can help detect patterns and trends.
- Industries that generate massive amounts of operational data like utilities, telecom, transportation, retail etc. This data if analyzed can help optimize operations and improve customer experience.
- Market research and customer analytics companies that collect consumer surveys, website usage data etc. from many countries. Big data tools help analyze customer sentiment and
Dokumen tersebut memberikan penjelasan singkat tentang data analytics untuk game, termasuk pengertian data analytics, kegunaan dan manfaatnya untuk game, cara mengumpulkan dan mengolah data, serta studi kasus yang pernah dilakukan oleh tim Publishing Agate Studio.
This presentation by Think Big principal Matt Cooke and Martin Oberhuber, Senior Data Scientist, discusses high frequency trading, requirements for success, and underlying architectures which may include Apache Spark.
This document provides an overview of big data. It defines big data as large volumes of diverse data that are growing rapidly and require new techniques to capture, store, distribute, manage, and analyze. The key characteristics of big data are volume, velocity, and variety. Common sources of big data include sensors, mobile devices, social media, and business transactions. Tools like Hadoop and MapReduce are used to store and process big data across distributed systems. Applications of big data include smarter healthcare, traffic control, and personalized marketing. The future of big data is promising with the market expected to grow substantially in the coming years.
Each month, join us as we highlight and discuss hot topics ranging from the future of higher education to wearable technology, best productivity hacks and secrets to hiring top talent. Upload your SlideShares, and share your expertise with the world!
Not sure what to share on SlideShare?
SlideShares that inform, inspire and educate attract the most views. Beyond that, ideas for what you can upload are limitless. We’ve selected a few popular examples to get your creative juices flowing.
SlideShare is a global platform for sharing presentations, infographics, videos and documents. It has over 18 million pieces of professional content uploaded by experts like Eric Schmidt and Guy Kawasaki. The document provides tips for setting up an account on SlideShare, uploading content, optimizing it for searchability, and sharing it on social media to build an audience and reputation as a subject matter expert.
History of Data Mining and Big Data …
What is the Big Data ?
What are the real life dimensions for Big Data ?
How to use Big Data for STEM and INFONOMICS?
Analytical Case studies and tools using Big Data fintech examples
What is the future of Data Science ?
This document provides an introduction to a training course on big data analytics. It discusses why big data has become important due to the exponential growth in data volume, velocity, and variety. The course aims to focus on cloud-based storage and processing of big data using systems like HDFS, MapReduce, HBase and Storm. It emphasizes that learning involves actively asking questions. Big data is introduced by explaining the three V's of volume, velocity and variety. Examples of big data usage are given in areas like baseball analytics, political campaigns and election predictions. Challenges of big data integration and processing large volumes of heterogeneous data are also covered.
This document discusses big data and data mining. It defines big data as large volumes of structured and unstructured data that are difficult to process using traditional techniques due to their size. It outlines the 4 Vs of big data: volume, velocity, variety, and veracity. The proposed system would use distributed parallel computing with Hadoop to identify relationships in huge amounts of data from different sources and dimensions. It discusses challenges of big data like data location, volume, privacy, and gaining insights. Solutions involve parallel programming, distributed storage, and access restrictions.
SAP Forum Ankara 2017 - "Verinin Merkezine Seyahat"MDS ap
The document discusses digital transformation and the journey to data-driven insights. It provides an overview of data types and how data has grown exponentially over time. Both structured and unstructured data are discussed, with examples of semi-structured data like emails and reports. The value of understanding all data sources is emphasized for gaining competitive advantages through analytics. New technologies like complex event processing are enabling lightning-fast action based on diverse data. Finally, the presentation introduces SAP HANA Vora for bridging the divide between enterprise and big data systems to facilitate precision decision making.
Mastering Your Customer Data on Apache Spark by Elliott CordoSpark Summit
This document discusses how Caserta Concepts used Apache Spark to help a customer master their customer data by cleaning, standardizing, matching, and linking over 6 million customer records and hundreds of millions of data points. Traditional customer data integration approaches were prohibitively expensive and slow for this volume of data. Spark enabled the data to be processed 10x faster by parallelizing data cleansing and transformation. GraphX was also used to model the data as a graph and identify linked customer records, reducing survivorship processing from 2 hours to under 5 minutes.
Operational Analytics Using Spark and NoSQL Data StoresDATAVERSITY
NoSQL data stores have emerged for scalable capture and real-time analysis of data. Apache Spark and Hadoop provide additional scalable analytics processing. This session looks at these technologies and how they can be used to support operational analytics to improve operational effectiveness. It also looks at an example of how operational analytics can be implemented in NoSQL environments using the Basho Data Platform with Apache Spark:
•The emergence of NoSQL, Hadoop and Apache Spark
•NoSQL Use Cases
•The need for operational analytics
•Types of operational analysis
•Key requirements for operational analytics
•Operational analytics using the Basho Data Platform with Apache Spark.
March Towards Big Data - Big Data Implementation, Migration, Ingestion, Manag...Experfy
Gartner, IBM, Accenture and many others have asserted that 80% or more of the world’s information is unstructured – and inherently hard to analyze. What does that mean? And what is required to extract insight from unstructured data?
Unstructured data is infinitely variable in quality and format, because it is produced by humans who can be fastidious, unpredictable, ill-informed, or even cynical, but always unique, not standard in any way. Recent advances in natural language processing provides the notion that unstructured content can be included in data analysis.
Serious growth and value companies are committed to data. The exponential growth of Big Data has posed major challenges in data governance and data analysis. Good data governance is pivotal for business growth.
Therefore, it is of paramount importance to slice and dice Big Data that addresses data governance and data analysis issues. In order to support high quality business decision making, it is important to fully harness the potential of Big Data by implementing proper Data Migration, Data Ingestion, Data Management, Data Analysis, Data Visualization and Data Virtualization tools.
Check it out: https://www.experfy.com/training/courses/march-towards-big-data-big-data-implementation-migration-ingestion-management-visualization
This document provides an overview of big data and how to manage large amounts of data. It defines big data, discusses the characteristics of big data including volume, variety and velocity. It describes who generates big data and technologies that can be used to analyze big data like Hadoop, data warehousing and stream computing. The challenges of handling big data are also mentioned.
This document discusses big data and provides motivation for why companies need to learn how to manage large amounts of data. It defines big data as data that exceeds the processing capacity of traditional databases due to its large volume, velocity, or variety. The document outlines the characteristics of big data using the "5 Vs" - volume, velocity, variety, viability and value. It then provides an overview of how big data works by presenting the big data supply chain. The key points are that big data enables new insights but also poses challenges for storage, analysis and use due to its scale and complexity.
This document provides an introduction to big data, including definitions of big data and its key characteristics of volume, variety, velocity, variability, and veracity. It discusses big data analysis and how it differs from traditional analytics by examining large, diverse datasets. Hadoop is presented as a popular open-source framework for managing and analyzing big data, and its use by companies like Facebook, LinkedIn, Walmart, and Twitter is described. The document also briefly outlines Hadoop's history and architecture, common Hadoop variants, skills needed to work with Hadoop, and examples of big data case studies.
This document discusses the big data analytics market opportunity. It notes that the volume of data from various sources is growing exponentially. It then outlines the life cycle of big data, reference architectures, and characteristics of big data. It discusses drivers of big data, pain points for enterprises, and the market opportunity for big data analytics. It predicts strong growth in spending on big data analytics and outlines types of analytics initiatives and trends in big data technology.
Big data refers to large datasets that are too complex for traditional data processing applications. Examples include Wikipedia which contains terabytes of text and images. Big data is characterized by being automatically generated, from new sources like the internet, and not designed for easy use. Analyzing big data can provide competitive advantages through insights from hidden patterns. Tools used for big data include distributed servers, cloud computing, distributed storage, distributed processing, and high performance databases. Data mining of big data helps businesses make better decisions by discovering patterns and relationships. Applications of big data include smarter healthcare, homeland security, traffic control, and more. Risks include being overwhelmed by data, escalating costs, and privacy issues. Big data impacts IT through new job opportunities in
Using Machine Learning to Understand and Predict Marketing ROIDATAVERSITY
Marketing is all about attracting, retaining and building profitable relationships with your customers, but how do you know which customers to target, which campaigns to run, and which marketing programs to invest in, to get most return for your dollar?
Join Alteryx and Keyrus as we demonstrate how to combine all relevant marketing, sales and customer data, and perform sophisticated analytics to deepen customer insight and calculate ROI of marketing programs.
You’ll walk away knowing how to:
Segment and profile your customers – take that raw data and translate it into real value
Build a marketing attribution model within Alteryx, creating a personal answer engine for your company.
Leverage R or Python code in an Alteryx workflow so data scientists can collaborate with non-coding stake holders in a code-friendly and code-free environment.
Join Alteryx and Keyrus and get the actionable insights you need to drive marketing ROI analytics, and answer million-dollar questions without spending millions of dollars on standardized solutions.
This document discusses data mining with big data. It defines big data and data mining. Big data is characterized by its volume, variety, and velocity. The amount of data in the world is growing exponentially with 2.5 quintillion bytes created daily. The proposed system would use distributed parallel computing with Hadoop to handle large volumes of varied data types. It would provide a platform to process data across dimensions and summarize results while addressing challenges such as data location, privacy, and hardware resources.
This document compares and contrasts data warehouses and big data. It discusses how big data has evolved from data warehousing technologies and involves new technologies like Hadoop and MapReduce. While data warehouses ensure consistent decision making using prior hypotheses, big data uses statistics to extract new hypotheses from very large and diverse datasets, including clickstream logs, sensor data, social media, and more. Both hard and soft data sources are important for businesses to analyze and extract value from immense and growing amounts of information.
Slides used for the keynote at the even Big Data & Data Science http://eventos.citius.usc.es/bigdata/
Some slides are borrowed from random hadoop/big data presentations
Similar to Big data predictive analytics in trading & asset management lars hamberg (20)
"Does Foreign Direct Investment Negatively Affect Preservation of Culture in the Global South? Case Studies in Thailand and Cambodia."
Do elements of globalization, such as Foreign Direct Investment (FDI), negatively affect the ability of countries in the Global South to preserve their culture? This research aims to answer this question by employing a cross-sectional comparative case study analysis utilizing methods of difference. Thailand and Cambodia are compared as they are in the same region and have a similar culture. The metric of difference between Thailand and Cambodia is their ability to preserve their culture. This ability is operationalized by their respective attitudes towards FDI; Thailand imposes stringent regulations and limitations on FDI while Cambodia does not hesitate to accept most FDI and imposes fewer limitations. The evidence from this study suggests that FDI from globally influential countries with high gross domestic products (GDPs) (e.g. China, U.S.) challenges the ability of countries with lower GDPs (e.g. Cambodia) to protect their culture. Furthermore, the ability, or lack thereof, of the receiving countries to protect their culture is amplified by the existence and implementation of restrictive FDI policies imposed by their governments.
My study abroad in Bali, Indonesia, inspired this research topic as I noticed how globalization is changing the culture of its people. I learned their language and way of life which helped me understand the beauty and importance of cultural preservation. I believe we could all benefit from learning new perspectives as they could help us ideate solutions to contemporary issues and empathize with others.
New Visa Rules for Tourists and Students in Thailand | Amit Kakkar Easy VisaAmit Kakkar
Discover essential details about Thailand's recent visa policy changes, tailored for tourists and students. Amit Kakkar Easy Visa provides a comprehensive overview of new requirements, application processes, and tips to ensure a smooth transition for all travelers.
TEST BANK Principles of cost accounting 17th edition edward j vanderbeck mari...Donc Test
TEST BANK Principles of cost accounting 17th edition edward j vanderbeck maria r mitchell.docx
TEST BANK Principles of cost accounting 17th edition edward j vanderbeck maria r mitchell.docx
TEST BANK Principles of cost accounting 17th edition edward j vanderbeck maria r mitchell.docx
Dr. Alyce Su Cover Story - China's Investment Leadermsthrill
In World Expo 2010 Shanghai – the most visited Expo in the World History
https://www.britannica.com/event/Expo-Shanghai-2010
China’s official organizer of the Expo, CCPIT (China Council for the Promotion of International Trade https://en.ccpit.org/) has chosen Dr. Alyce Su as the Cover Person with Cover Story, in the Expo’s official magazine distributed throughout the Expo, showcasing China’s New Generation of Leaders to the World.
University of North Carolina at Charlotte degree offer diploma Transcripttscdzuip
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Independent Study - College of Wooster Research (2023-2024) FDI, Culture, Glo...AntoniaOwensDetwiler
"Does Foreign Direct Investment Negatively Affect Preservation of Culture in the Global South? Case Studies in Thailand and Cambodia."
Do elements of globalization, such as Foreign Direct Investment (FDI), negatively affect the ability of countries in the Global South to preserve their culture? This research aims to answer this question by employing a cross-sectional comparative case study analysis utilizing methods of difference. Thailand and Cambodia are compared as they are in the same region and have a similar culture. The metric of difference between Thailand and Cambodia is their ability to preserve their culture. This ability is operationalized by their respective attitudes towards FDI; Thailand imposes stringent regulations and limitations on FDI while Cambodia does not hesitate to accept most FDI and imposes fewer limitations. The evidence from this study suggests that FDI from globally influential countries with high gross domestic products (GDPs) (e.g. China, U.S.) challenges the ability of countries with lower GDPs (e.g. Cambodia) to protect their culture. Furthermore, the ability, or lack thereof, of the receiving countries to protect their culture is amplified by the existence and implementation of restrictive FDI policies imposed by their governments.
My study abroad in Bali, Indonesia, inspired this research topic as I noticed how globalization is changing the culture of its people. I learned their language and way of life which helped me understand the beauty and importance of cultural preservation. I believe we could all benefit from learning new perspectives as they could help us ideate solutions to contemporary issues and empathize with others.
STREETONOMICS: Exploring the Uncharted Territories of Informal Markets throug...sameer shah
Delve into the world of STREETONOMICS, where a team of 7 enthusiasts embarks on a journey to understand unorganized markets. By engaging with a coffee street vendor and crafting questionnaires, this project uncovers valuable insights into consumer behavior and market dynamics in informal settings."
[4:55 p.m.] Bryan Oates
OJPs are becoming a critical resource for policy-makers and researchers who study the labour market. LMIC continues to work with Vicinity Jobs’ data on OJPs, which can be explored in our Canadian Job Trends Dashboard. Valuable insights have been gained through our analysis of OJP data, including LMIC research lead
Suzanne Spiteri’s recent report on improving the quality and accessibility of job postings to reduce employment barriers for neurodivergent people.
Decoding job postings: Improving accessibility for neurodivergent job seekers
Improving the quality and accessibility of job postings is one way to reduce employment barriers for neurodivergent people.
Economic Risk Factor Update: June 2024 [SlideShare]Commonwealth
May’s reports showed signs of continued economic growth, said Sam Millette, director, fixed income, in his latest Economic Risk Factor Update.
For more market updates, subscribe to The Independent Market Observer at https://blog.commonwealth.com/independent-market-observer.
Falcon stands out as a top-tier P2P Invoice Discounting platform in India, bridging esteemed blue-chip companies and eager investors. Our goal is to transform the investment landscape in India by establishing a comprehensive destination for borrowers and investors with diverse profiles and needs, all while minimizing risk. What sets Falcon apart is the elimination of intermediaries such as commercial banks and depository institutions, allowing investors to enjoy higher yields.
Enhancing Asset Quality: Strategies for Financial Institutionsshruti1menon2
Ensuring robust asset quality is not just a mere aspect but a critical cornerstone for the stability and success of financial institutions worldwide. It serves as the bedrock upon which profitability is built and investor confidence is sustained. Therefore, in this presentation, we delve into a comprehensive exploration of strategies that can aid financial institutions in achieving and maintaining superior asset quality.
Big data predictive analytics in trading & asset management lars hamberg
1. Big
data
predic,ve
analy,cs
in
trading
&
asset
management:
An
Industry
Game
Changer
• OSINT
&
The
Holy
Graal:
“understanding
streaming
unstructured
language
data
on
an
internet
scale”
• Capabili@es
and
limita@ons
of
big
data
predic@on
tools
in
trading
&
asset
management
• Superior
informa@on
&
tech
giants
moving
into
asset
management?
Implica@ons
for
compe@@on
Lars
Hamberg
+46721774450
2. Key
take-‐aways:
• Informa,on
-‐
the
most
valuable
commodity?
• Drama,c
changes
in
compe,,ve
landscape
• Public
long-‐term
prospec,ve
study
of
Big
Data
Predic,on
tools
for
trading:
-‐ Size
MaPers:
Longer,
FaPer,
Richer
-‐ Granularity
&
Versa@lity:
Sen@ment
Analysis
2.0
-‐ Scalability:
Learning
&
Seman@c
Memory
Models