...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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...the need to collect and analyze data? Organization | Drivers for Data Collection and Analysis | Amazon.com | * Customer information * Website personalization * Supply chain management * Employee training and incentivization * Email/newsletter personalization * Website traffic patterns to improve customer experience * Demographic information for marketing * Information about supplier and for supplier about their products * Error rates and how to fix errors * Tracking new and popular items that amazon might want to stock * Warehouse data and error rates * Shipping data, time it takes to complete orders * Tracking app orders and glitches * Improving relationships with suppliers | Wal-Mart | * Supply chain management for internal and external (supplier) use * Shelf stocking information, were shelves stocked in time? * Sales data information to improve shopping experience * Forecasting demand for products * Competitor pricing information * Optimizing factory production for suppliers * Error rates * Time it takes to complete and ship online orders * Employee information such as payroll * New product information and forecasting * Employee training * Demographic information for marketing * Website data for optimization * Tracking app purchases and orders and glitches * Improving relationships with suppliers | Brose | * Raw material procurement and forecasting * Manufacturing data and error rates * Making sure...
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...Week 3 - The Big Data Challenges May 2014 Case Study: The Big Data Challenges Glendoria Early Strayer University CIS 500 – Information Systems for Decision Making Dr. Vince Osisek May 12, 2014 Case Study: The Big Data Challenges Week 3 - The Big Data Challenges Page 2 What if your car could talk? Well Volvo CIO's had to wonder the same thing as well. Volvo, a Swedish multinational manufacturing company, that not only produce cars, but light and heavy weight trucks as well as buses. Volvo Car Corporation has a history of using innovation to produce premium automobiles. The manufacture chose to rely on information technology to gain the innovative edge to help the company grow by integrating the cloud infrastructure into their networks. The Volvo Data Warehousing Program, started in 2006 (Data in the Driver's Seat), it draws insight from this multi-terabyte resource to create clear business advantage by integrating information from four primary sources: a system for managing vehicle and hardware specifications, one for managing on-board software specifications, the system that collects vehicle diagnostic data from service centers worldwide, and the warranty administration system. (Tobey, 2010) Joining all the various pieces of data together, Volvo can be warned about potential mechanical issues that may show up in the early part of a car's lifecycle. Volvo can spot patterns from the data that may indicate a potential flaw in a particular part well before a...
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...INTRODUCTION TO BIG DATA IN RETAIL Big Data is a massive pool of data (both structured and unstructured) that cannot be processed using traditional database and software techniques. When any particular organization uses this catch phrase they refer to the technology that can be used to channelize this huge pool of data into some useful information. This channelization includes modification, creation, manipulation, storage, transfer, sharing and analysis of the data. Big data in Retail Consider a situation in which it is the busiest shopping time of the year and only one particular product is flying off the shelf. In the past, the stores who reacted to this demand forecast or had good amount of inventory of that product had an advantage....
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...Case Study 1: The Big Data Challenges James (JT) Clark Professor Elhag Ibrahim Information System for Decision-Making 10/31/2012 . Case Study 1: The Big Data Challenges Introduction I feel as though Volvo integrated the cloud infrastructure into its networks very well. It took the proactive approach instead of being reactive. It applied this to all aspects to their vehicles. Volvo chooses to have the cloud work for them and it saved them money in the end. They had a centralized data base in which all their information was gathered and stored and from there it was turned from raw knowledge into useful information. Volvo Car Corporation Integrates the Cloud into Its Networks Volvo had the ability to collectively harness the information that they collected information from the hundreds of sensors that were embedded throughout the cars. This data was being captured for use from the vehicle and transmitted via the cloud back to the manufacture. This data was streamed into a centralized analysis hub, where they were being used by the customer relationship systems, dealership systems, and product development and designs systems. This way they could share information about any particular vehicle and any problem that they may be experiencing and stop an issues before it gets out of hand. How Volvo Car Corporation Transforms Data into Knowledge They are taking the data that they are receiving from the vehicles and putting it all together. This way they are pre-warned...
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...“Converting data into business value at Volvo” Dianne Allamani Dr. Paulette Stephens CIS 500 - Information Friday, July 13, 2012 Volvo Car Corporation is a great example of a Corporation that has been able to integrate the cloud infrastructure into its networks. Using the cloud, Volvo is able to convey and collect huge volume of Data “Big Data” to its Volvo Data Warehouse where it can be stored and analyzed for future record usage and recovery or restoration by Volvo’s employees. Asthana (2011) He states that: The Swedish car maker collects terabytes of data from embedded sensors in their cars—from their customer relationship management (CRM) systems, from dealerships, and from their factory floors. These data streams are combined and analyzed to yield early predictive information on matters like manufacturing defects that haven’t occurred yet. Using Saas, software applications are run from one centralized location. The software can be accessed from any location over the internet. Volvo uses on demand computing via the clouds, to link their business units and support areas and production plants all over the world. Their cloud computing systems functions as an entry point for their web users. Connecting employees, suppliers and vendors and therefore improving their operations making them more efficient and more competitive. This allows Volvo to optimize their IT infrastructure cost effectively and still be technologically advanced giving them the competitive advantage...
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...WHITE P APER Big Data: Trends, Strategies, and S AP Technology Sponsored by: SAP Carl W. Olofson August 2012 Dan Vesset THE DAWN OF THE INTE LLIGENT ECONOMY The intelligent economy has arrived. The convergence of intelligent devices, social networking, pervasive broadband communications, and analytics is redefining relationships among producers, distributors, and consumers of goods and services. The growth in volume, variety, and velocity of data has created new challenges and opportunities. The information access, analysis, and management challenges of the intelligent economy can overwhelm organizations unprepared for the emerging changes. In this environment, it is not only access to data but the ability to analyze and act upon it that creates competitive advantage in commercial transactions, enables sustainable and secure management of communities, and promotes appropriate distribution of social, healthcare, and educational services. It is not only access to data but the ability to analyze and act upon it that creates competitive advantage. www.idc.com P.508.872.8200 F.508.935.4015 In This White Paper This IDC white paper discusses the emerging technologies of the Big Data movement. It breaks out these technologies according to their most effective roles and use cases. It also discusses why Big Data has become so important at this time and how Big Data can help enterprises reach their business goals. It considers the challenges created by Big Data and how they can...
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...Does Big Data Bring Big Rewards? 1.) Describe the kinds of “big data” collected by the organizations described in this case. The organizations that were mentioned in this case study are The US State Federal Law Enforcement Agencies, Real Time Crime Center NYC, Vestas, AutoZone, Hertz, and Sears Holding. The kind of data the U.S. State and Federal law enforcement agencies are dealing with are to discover hidden patterns in a criminal activity such as correlations between time, opportunity, and organizations. The real time crime centers uses data warehouses dealing with city crime and criminals. They use a system whose search capabilities allow the NYPD to quickly obtain data from any of data source. Vestas are using big data to allow there company to go green. Vestas use data so that it can accurately places it turbines for optimal wind power generations. Autozone uses big data to help it adjust inventory and product prices for thousands of stores whereas for Hertz a car rental giant it allows data to be gathered from web surveys, e-mails, and even text messages. 2.) List and describe the business intelligence technologies described in this case. The business intelligence technologies that were described in this case are hidden patterns in criminal activity, information on criminals, location based data, types of cars driven by people in an area, data to analyze consumer sentiment, and data to enhance customer retailer relationship. 3.) Why did the companies described...
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...BIG DATA - THE MANAGEMENT REVOLUTION Summary The general theme of the article is to proof how data-driven decisions are better for businesses as data enables the managers to base their decision on evidence rather than intuition. The idea behind big data is to collect all kinds of data from various sources and to effectively utilize this data to improve the financial and operational aspects of the business. Companies that operate on digital platforms like Amazon are already experts at big data and are using the predictions based on data in a deft manner. The practice of big data should not be confined to companies that operate digitally but should be implied by other businesses as well, as big data is a revolutionary practice that provides data in larger volumes with higher velocity with which data is complied and entailed and the variety in which data is available. Furthermore, modifying a company to be data-driven is not only technologically challenging but poses a copious amount of managerial challenges as well. The decision is usually based on the senior manager who has to know how to answer questions and how to embrace evidence based decision. For a company to re-organize itself to become data-driven, the manager should concentrate on improvising five areas that include, leadership, talent management, technology, decision making and company culture. The author cities instances of big data using examples of airports where PASSUR Aerospace provided a service called...
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...I. Big data emerging factor in IT area A. World’s notice for big data An appearance of tablet PC and social media was the hottest issue in IT market in last year. There are some successful global companies that go along the trends although it is not that long period since they appeared in the world, such as Apple, Google, Facebook, and Twitter. They have something in common. That is, they are based on ‘Big Data’ technology. As a result of using ‘big data’, the amount of stored data by their big data system during 2012 is much more than that of data which had been produced and stored until 2011. It helps to solve several problems in the company. Due to the geometrical increase of the amount of data, the important of big data will be continuous. Big data is selected as one of noticeable keyword in 2013 IT area with mobility, social, and cloud. It will be main factor of growth of IT infrastructure in the medium to longer term and is expected to provide new strategic superiority for many companies. It is highly acclaimed at the domestic market and also the foreign market. Several successful cases of applying big data shows that it can be positive factor helping to recover global economy. Moreover, it is not limited to IT-related business but the introduction in various areas will create value. B. Background of emerging big data In fact, there are many efforts to extract meaningful information through collection and analysis of huge amount of data. Through this effort...
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...BUS211f(2) ANALYZING BIG DATA I1 Spring 2014—MW 8:00–9:20 am Location: Sachar 116 (International Hall) Prof. Bharatendra Rai 313-282-8309 (mobile) brai@brandeis.edu Office: Sachar 1C Hours: MW, 9:30 – 10:15 and by appointment TA: TBD This is a two credit module that examines the opportunities and industry disruption in an era of massive, high velocity, unstructured data and new developments in data analytic. We treat some strategic, ethical, and technical dimensions of big data. The technical foci of the course include data structures, data warehousing, Structured Query Language (SQL), and high-impact visual displays. The principal objective of the course is to help students build understanding of data as an essential competitive resource, and acquire advanced computer skills through cases and hands-on applications. Assignments and classroom time will be devoted to both to analysis of current developments in analytics and to gaining experience with current tools. Davenport , Thomas H. and Harris, Jeanne G. Competing on Analytics: The New Science of Winning. Cambridge: Harvard Business School Press, 2007. ISBN 978-1422103326. Available for purchase at the bookstore. There is a required on-line course pack available for purchase at the Harvard Business Publishing website at this URL: https://cb.hbsp.harvard.edu/cbmp/access/23455671 This link is also available on LATTE . See last page of Syllabus for course pack contents. Other readings as posted on LATTE site. Learning Goals...
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...BUS211f(2) ANALYZING BIG DATA I1 Spring 2014—MW 8:00–9:20 am Location: Sachar 116 (International Hall) Prof. Bharatendra Rai 313-282-8309 (mobile) brai@brandeis.edu Office: Sachar 1C Hours: MW, 9:30 – 10:15 and by appointment TA: TBD This is a two credit module that examines the opportunities and industry disruption in an era of massive, high velocity, unstructured data and new developments in data analytic. We treat some strategic, ethical, and technical dimensions of big data. The technical foci of the course include data structures, data warehousing, Structured Query Language (SQL), and high-impact visual displays. The principal objective of the course is to help students build understanding of data as an essential competitive resource, and acquire advanced computer skills through cases and hands-on applications. Assignments and classroom time will be devoted to both to analysis of current developments in analytics and to gaining experience with current tools. Davenport , Thomas H. and Harris, Jeanne G. Competing on Analytics: The New Science of Winning. Cambridge: Harvard Business School Press, 2007. ISBN 978-1422103326. Available for purchase at the bookstore. There is a required on-line course pack available for purchase at the Harvard Business Publishing website at this URL: https://cb.hbsp.harvard.edu/cbmp/access/23455671 This link is also available on LATTE . See last page of Syllabus for course pack contents. Other readings as posted on LATTE site. Learning Goals...
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...Big data analytics is projected to change the way companies manage and analyze large information set and how people produce massive amounts of data. A recent findings produced by few Internet and Online Business Degree looked at the future of this trend sweeping through the IT industry. This concept is up-growing one as the current data storage pattern utilized by the companies is not as productive as plotted. It is refers to following type of data 1) Traditional Enterprise Data:- includes customer related data ERP, CRM, web transaction 2) Machine Generated Data:- weblogs, Trading Systems etc 3) Social Data: - data of facebook, twitter, google etc. Big Data can be seen in the finance and business where enormous amount of stock exchange, banking, online and onsite purchasing data flows through computerized systems every day and are then captured and stored for inventory monitoring, customer behavior and market behavior. Day by day the capacity of data is increasing & many of industries are not able to manage it efficiently. By 2020, a total of 35 zeta-bytes of data will be produced as the average annual generation of information grows 43,000 percent, according to Computer Sciences Corporation. Big data may still be a relatively new phenomenon, but its impact is already being felt throughout various industries. Organizations that can effectively store, manage and analyze this information may set themselves apart from their competitors or, even better, make key advancements...
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...Cisco IT Case Study – August 2013 Big Data Analytics How Cisco IT Built Big Data Platform to Transform Data Management EXECUTIVE SUMMARY CHALLENGE ● Unlock the business value of large data sets, including structured and unstructured information ● Provide service-level agreements (SLAs) for internal customers using big data analytics services ● Support multiple internal users on same platform SOLUTION ● Implemented enterprise Hadoop platform on Cisco UCS CPA for Big Data - a complete infrastructure solution including compute, storage, connectivity and unified management ● Automated job scheduling and process orchestration using Cisco Tidal Enterprise Scheduler as alternative to Oozie RESULTS ● Analyzed service sales opportunities in one-tenth the time, at one-tenth the cost ● $40 million in incremental service bookings in the current fiscal year as a result of this initiative ● Implemented a multi-tenant enterprise platform while delivering immediate business value LESSONS LEARNED ● Cisco UCS can reduce complexity, improves agility, and radically improves cost of ownership for Hadoop based applications ● Library of Hive and Pig user-defined functions (UDF) increases developer productivity. ● Cisco TES simplifies job scheduling and process orchestration ● Build internal Hadoop skills ● Educate internal users about opportunities to use big data analytics to improve data processing and decision making NEXT STEPS ● Enable NoSQL Database and advanced...
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...Bad Data AMU DEFM420 Big Data refers to volume, variety and velocity of available data. The issue with that is that any emphasis is put on volume or quantity of data. The quantity is a very vague element of Big Data, there is no precise requirements to purely volume based data. What should be considered in big data, the complexity and depth of the data? If the content of data is deep and containing detailed information it holds more purpose. When we analyzes data we as a culture prefer less to review but of more importance. I would rather read two pages of relevant data then read one hundred pages that contain 3 pages of data. This is a factor of human nature but also a business factor. The majority of what we do in government work is time sensitive. We operate on a system of end dates. With time being a factor wasting time on Big Data that isn’t always pertinent is a waste. While in cases of no time limit, having the full three V’s of big data is acceptable and may in the end give more accurate information after spending excessive time sorting through the information mainly the volume portions. Is the system of Big Data wrong? No it is not wrong but the concept is too vague. For different situations data needs to be limited. Others not so much so it gives us a system and collection of information that is in some cases excessive for the need. It is a double edged sword. There are other aspects of Big Data collections useful in contracting offices...
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