...amount of data being handled and processed has increased tremendously. Big Data analytics plays a very significant part in reducing the size of the data as well as the complexity in applications that are being used for Big Data. Big Data Visualization is an important approach in creating meaningful visuals and graphical representations from the Big Data that help in better decision making and that give a clear insight into the data. Visualization, Big Data, Big Data Visualization, data visualization techniques are some of the topics that are discussed in this paper and examples for visualizations have been presented as well. Keywords— Visualization, Data processing, Data analytics, Big Data, Interactive visualizations. I. VISUALIZATION...
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...The Impact of Big Data By Ijaaz Lagardien Group 3A 214167542 1|Page Contents Plagiarism Declaration ....................................................................................................................................... 3 Abstract ............................................................................................................................................................. 4 Keywords ........................................................................................................................................................... 4 Introduction ....................................................................................................................................................... 4 Semi-structured data ......................................................................................................................................... 5 What is structured data ................................................................................................................................. 5 What is semi-structured data ........................................................................................................................ 5 Types of semi-structured data....................................................................................................................... 5 Unstructured data ......................................................................................................
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...Big Data is a type of new era that will help the competition of companies to capture and analyze huge volumes of data. Big data can come in many forms. For example, the data can be transactions for online stores. Online buying has been a big hit over the last few years, and people have begun to find it easier to buy their resources. When the tractions go through, the company is collecting logs of data to help the company increase their marketing production line. These logs help predict buying patterns, age of the buyer, and when to have a product go on sale. According to Martin Courtney, “there are three V’s of big data which are: high volume, high variety, high velocity and high veracity. There are other sites that use big volumes of data as well. Social networking sites such as Facebook, Twitter, and Youtube are among the few. There are many sites that you can share objects to various sources. On Facebook we can post audio, video, and photos to share amongst our friends. To get the best out of these sites, the companies are always doing some type of updating to keep users wanting to use their network to interact with their friends or community. Data is changing all the time. Developers for these companies and other software have to come up with new ways of how to support new hardware to adapt. With all the data in the world, there is a better chance to help make decision making better. More and more information is becoming available at the click of a mouse, which can help...
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...Big Data and Data Analytics for Managers Q1. What is meant by Big Data? How is it characterized? Give examples of Big Data. Ans. Big data applies to information that can’t be processed or analysed using traditional processes or tools or software techniques. The data which is massive in volume and can be both structured or unstructured data. Though, it is a bit challenging for enterprises to handle such huge amount fast moving data or one which exceeds the current processing capacity, still there lies a great potential to help companies to take faster and intelligent decisions and improve operations. There are three characteristics that define big data, which are: 1. Volume 2. Velocity 3. Variety * Volume: The volume of data under analysis is large. Many factors contribute to the increase in data volume, for example, * Transaction-based data stored through the years. * Unstructured data streaming in social media. * Such data are bank data (details of the bank account holders) or data in e-commerce wherein customers data is required for a transaction. Earlier there used to data storage issues, but with big data analytics this problem has been solved. Big data stores data in clusters across machines, also helping the user on how to access and analyse that data. * Velocity: Data is streaming in at unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with...
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...groups or interviews, Entravision slowly but surely fell behind in the ever more digitalized broadcasting market. Having found a market niche in the Hispanic market segment, Entravision set out to create a new Data analytics department called Luminar to utilize and profit from information gathered about the Hispanic core customership. The first segment argues whether and how Luminar is able to create value by implementing Big Data analysis. Consequently, this paper tries to clarify whether the obtained advantage can indeed be of a sustainable nature and thus allow for an independent and successful department within the Entravision construct. Furthermore, the data gathering capabilities are being analyzed to inquire into whether there is viable competitive power to benefit from the advantages mentioned priorly. Finally, having established the background for the undertaking, the paper will shed a light on how exactly the department would fit into the organizational structure and what benefits and pitfalls the embedment or independent venturing of Luminar would have. 2. Value Creation In order to asses the added value of Luminar to the mother enterprise Entravision this paper will provide a first outlook of the Latino segment in the US and then conclude with the Big Data analytics aspect, which comprises additional organizational and strategic issues. Latinos are the largest and fastest-growing minority group in the United States. The Latino population is forecasted to expand...
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...Although the vital purpose of big data management is similar to, and a much better understanding of the issues, the values sought, and also the challenges that involved differing significantly between business companies and healthcare organizations. Business companies use big data to influence customers' desires and behavior patterns, develop distinctive core competencies, and build innovative products or services, whereas governments and healthcare stakeholders use big data and predictive analytics to look for sustainable solutions to such problems as pursuit public health, deciding and implementing more appropriate treatment methods for patients, supporting clinical enhancements, monitoring the protection of healthcare systems, reassuring...
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...Big Data is a massive volume of data. It's usually so massive that it becomes complicated to comprehend using tools such as on-hand database, and traditional data processing applications. Some problems that come up are storage, sharing, analysis, and search.Even though these problems do occur it still can be helpful in business operations, and better business decisions. This data can also help give companies informations which can increase profit, bring more customers, and overall increase the business's value. Characteristics of Big Data include the five V’s. The first one is volume, which is the quantity of data. The second is Variety, which the type of Data. The third is velocity, which is the speed of the data is gathered. The fourth one Variability, which is inconsistency of data can hamper processes to manage it. The final one is Veracity, which is the quality of data captured can vary. These data sets are growing rapidly mainly because they are gathered at a fairly cheap. The world's technological per-capita are doubling every 40 months. Business intelligence with data with high information density to look for trends. Big Data also increased information management specialist. Some of the largest companies like IBM and Microsoft spent over 15 billion dollars on software firms which specialize in data analytics. Governments use big data because it's efficient in terms of productivity and innovation. While gathering big data is a big benefit there are also some issues...
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...6 4.0 Data…………………..………………………………………………….............. 7 5.0 Main Outcomes.......................................................................16 6.0 Reccomendations....................................................................17 7.0 References………………………………………………………………….………18 1.0 Executive Summary The purpose of this report is to illustrate the aspects of the two individual staff members and how they are likely to relate to one another given their unique combination of characteristics and behaviours. There is a literature review on the use of psychometric testing and collecting the data from psychometric testing on two staff members. This report will introduce advantages and disadvantages of psychometric testing in HR. There are some results of the indicator instruments between the two staff members. This report will give some recommendations on how to best utilise two staff members and some suggestions on how to ensure teamwork between the two staff members. Ella Bryant an architect and Lisa Mcune a. Have not worked with one another before due to 2.0 Introduction 2.0 Introduction This section provides the purpose, background, scope, research methods, and indicates how the manager will be able to utilise the report finding, and mention limitations of the report. 2.1 Purpose The purpose of this report is to analyse strengths and weakness of two staff members and how they are likely to relate to one another given their unique characteristics and behaviours...
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...Data collection method: Secondary data: Secondary data can be collected through the Internet. At first, we search on the internet the information of Vietmac restaurants to know the information available or not. Thanks to secondary data, we can investigate that Vietmac restaurants also has many competitors such as KFC, Lotteria and Jollibee so we compare core competence of Vietmac restaurants to them and we find out that Vietmac restaurants bring more convenient to customers with shops integrated divers marts. Besides, we can know the Measurement of customer satisfaction about fast food product, especially, satisfaction measurement of Vietmac fast food product; Information about customer’s habits, needs; Defining the characteristics of product and answering what characteristics are most important and comments for Vietmac through secondary data. Primary data: Firstly, Primary research entails the use of immediate data in determining the survival of the market. The popular way to collect primary data is interviews, which shows that direct relationship between potential customers and the shops. Therefore, after we have the basic knowledge and information about Vietmac restaurants, we use primary data to collect the information. With Vietmac restaurants, our purpose is to find out the satisfaction of the customers with the services of the company. Firstly, with the interview, we interview the staffs of Vietmac restaurants to know exactly all the services they are serving...
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...Media communication is the process in which data is transferred from one computer to another. This involves transmission of digital of information to different devices through wireless or cabled connections. The data transmitted over networks could be either digital or analog. * Analog signals are continuous signals that vary in strength. Sound is an example of an analog signal. Telephones have transmitters that encode sound waves into electromagnetic waves, which then travel over wires toward their destination. The receiving telephone decodes the electromagnetic waves back into sound waves. Our brains then decode the sound waves into the words we hear. Computer modems use the same principle. Analog signals can be represented digitally. For instance, a high electromagnetic voltage could be interpreted as 1 and low voltage as 0. * Digital signals are discrete rather than continuous. Either there is a signal or there isn't a signal. Telegraphs transmit data with discrete signals. You either hear a tap or you do not hear a tap. Discrete signals can be represented by on and off pulses. The duration of a discrete signal can be varied, as with dots and dashes in Morse Code. To explain how this data is transmitted over the network, first I had to explain the mediums. Mediums are ways, which the data use to travel from one place to another. These mediums may vary depending the environment or type of network. The most commonly used data communication media include: * Wire...
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...6, June 2013 ISSN 2250-3153 1 Big Data Landscape Shubham Sharma Banking Product Development Division, Oracle Financial Services Software Ltd. Bachelor of Technology Information Technology, Maharishi Markandeshwar Engineering College Abstract- “Big Data” has become a major source of innovation across enterprises of all sizes .Data is being produced at an ever increasing rate. This growth in data production is driven by increased use of media, fast developing organizations, proliferation of web and systems connected to it. Having a lot of data is one thing, being able to store it, analyze it and visualize it in real time environment is a whole different ball game. New technologies are accumulating more data than ever; therefore many organizations are looking forward to optimal ways to make better use of their data. In a broader sense, organizations analyzing big data need to view data management, analysis, and decision-making in terms of “industrialized” flows and processes rather than discrete stocks of data or events. To handle these aspects of large quantities of data various open platforms had been developed. Index Terms- Big Technologies,Tools Data, Landscape,Open Platforms, nearly 500 exabytes per day .To put the numbers in perspective this is equivalent to 5×1020 bytes per day. Almost 200 times higher than all the sources combined together in the world. To handle this huge chunk of data will be hard with the existing data management technologies. Hence the technology...
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...* Why Big Data analytics is important? Big data is a term that refers to data sets or combinations of data sets whose size (volume), complexity (variability), and rate of growth (velocity) make them difficult to be captured, managed, processed by conventional technologies and tools, within certain time period to make them useful. Big data is vital in fact that when huge information is successfully and effectively caught, prepared organizations can pick up a more finish comprehension of their business, clients, items, contenders, and so on. This can prompt effectiveness enhancements, expanded deals, lower costs, better client benefit, or enhanced items and administrations. Following are some of the examples of big data in different fields: Utilizing information technology (IT) logs to enhance IT investigating and security rupture discovery, pace, viability, and future event avoidance. Use of voluminous call focus data all the more rapidly, keeps in mind the end goal to enhance client association and fulfilment. Use of online networking content keeping in mind the end goal to better and more rapidly client feeling about you/your clients, and enhance items, administrations, and client association. Fraud detection and prevention in any industry that procedures budgetary exchanges on-line, for example, shopping, keeping money, contributing, protection and medicinal services claims. Use of money related business sector exchange data to all the more rapidly evaluate...
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...AND ANALYTICS: FROM BIG DATA TO BIG IMPACT Hsinchun Chen Eller College of Management, University of Arizona, Tucson, AZ 85721 U.S.A. {hchen@eller.arizona.edu} Roger H. L. Chiang Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, OH 45221-0211 U.S.A. {chianghl@ucmail.uc.edu} Veda C. Storey J. Mack Robinson College of Business, Georgia State University, Atlanta, GA 30302-4015 U.S.A. {vstorey@gsu.edu} Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework. Keywords: Business intelligence and analytics, big data analytics, Web 2.0 ...
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...VI. COMPARATIVE STUDY ON BIG DATA AND TRADITIONAL DATABASE IN HEALTHCARE Features Traditional database Big data Data architecture It uses centralized database architecture in which large and complex problems are solved by a single computer system. It is based on distributed database architecture where a large block of data is solved by dividing it into several smaller sizes. Cost To manage large amount of data traditional database requires complex and expensive hardware and software devices. Whereas in big data as the massive amount of data is segregated between various systems, the amount of data decreases. So use of big data is quite simple and less cost. Performance Performance is low Performance is high Computational power Computational...
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...ANALYTICS: FROM BIG DATA TO BIG IMPACT Hsinchun Chen Eller College of Management, University of Arizona, Tucson, AZ 85721 U.S.A. {hchen@eller.arizona.edu} Roger H. L. Chiang Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, OH 45221-0211 U.S.A. {chianghl@ucmail.uc.edu} Veda C. Storey J. Mack Robinson College of Business, Georgia State University, Atlanta, GA 30302-4015 U.S.A. {vstorey@gsu.edu} Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework. Keywords: Business intelligence and analytics, big data analytics...
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