...companies in every industry are using analytics to replace intuition and guesswork in their decision-making. As a result, managers are collecting and analyzing enormous data sets to discover new patterns and insights and running controlled experiments to test hypotheses. This course prepares students to understand structured data and business analytics and become leaders in these areas in business organizations. This course teaches the scientific process of transforming data into insights for making better business decisions. It covers the methodologies, algorithms, issues, and challenges related to analyzing business data. It will illustrate the processes of analytics by allowing students to apply business analytics algorithms and methodologies to real-world business datasets from finance, marketing, and operations. The use of real-world examples and cases places business analytics techniques in context and teaches students how to avoid the common pitfalls, emphasizing the importance of applying proper business analytics techniques. In addition to cases, this course features hands-on experiences with data collection using Python programs and analytics software such as SAS Enterprise Guide. Throughout the semester, each team works to frame a variety of business issues as an analytics problem, analyze data provided by the company, and generate applicable business insights as a secondary objective, while also learning essential business analytics techniques. Students benefit...
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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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...Challenges and Opportunities with Big Data A community white paper developed by leading researchers across the United States Executive Summary The promise of data-driven decision-making is now being recognized broadly, and there is growing enthusiasm for the notion of ``Big Data.’’ While the promise of Big Data is real -- for example, it is estimated that Google alone contributed 54 billion dollars to the US economy in 2009 -- there is currently a wide gap between its potential and its realization. Heterogeneity, scale, timeliness, complexity, and privacy problems with Big Data impede progress at all phases of the pipeline that can create value from data. The problems start right away during data acquisition, when the data tsunami requires us to make decisions, currently in an ad hoc manner, about what data to keep and what to discard, and how to store what we keep reliably with the right metadata. Much data today is not natively in structured format; for example, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search: transforming such content into a structured format for later analysis is a major challenge. The value of data explodes when it can be linked with other data, thus data integration is a major creator of value. Since most data is directly generated in digital format today, we have the opportunity and the challenge both to influence the creation to facilitate...
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...Ahmed Mansour Big Data and Analytics Developer at OMS ahmedelmasry_60311@hotmail.com Summary Working in Big Data & Analytics (2014 - Present). Working in Business Intelligence (IBM Cognos) (2013 - Present). Working in ERP & Data manipulation (Oracle & Asp.net) (2011 - 2013). Skills (Pivotal HD (Hadoop),Oracle, Sql Server, MongoDB, Asp.net, JavaScript, Node.js, C#). Training (Pivotal HD Hadoop training). Master's Degree in Informatics at Nile University (2014-2016) Graduated from Faculty of Science, Cairo University (2011). Awarded (YIA) The Young Innovator Award (2010). Experience Big Data and Analytics Developer at OMS April 2015 - Present (1 month) Developing and analysis Big Data using Hadoop framework (Pivotal HD & Hawq), Hadoop Eco-System Co-Founder and Data Analyst at AlliSootak September 2010 - Present (4 years 8 months) Developing and Researcher Senior Software Developer at Fifth Dimension (5d) October 2014 - April 2015 (7 months) Senior Software Developer at Bizware August 2013 - October 2014 (1 year 3 months) Developing 2 recommendations available upon request Director of Special Projects at CIT Support May 2012 - January 2014 (1 year 9 months) Ensure that the client's requirements are met, the project is completed on time and within budget and that everyone else is doing their job properly. Senior Software Developer at I-Axiom Cloud ERP Solutions November 2011 - August 2013 (1 year 10 months) Developing Certifications The Data Scientist’s...
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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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...Date: 04-19-2015 The New Frontier: Data Analytics (Course title: Info System Decision Making) Professor: Clifton Howell Student: Deep Ajabani Data analysis is the process of finding the right data to answer your question, understanding the processes underlying the data, discovering the important patterns in the data, and then communicating your results to have the biggest possible impact. Analytics have been used in business since the management exercises were put into place by Frederick Winslow Taylor in the late 19th century. Henry Ford measured the time of each component in his newly established assembly line. But analytics began to command more attention in the late 1960s when computers were used in decision support systems. Since then, analytics have changed and formed with the development of enterprise resource planning (ERP) systems, data warehouses, and a large number of other software tools and processes. In later years the business analytics have exploded with the introduction to computers. This change has brought analytics to a whole new level and has made the possibilities endless. As far as analytics has come in history, and what the current field of analytics is today many people would never think that analytics started in the early 1900s with Mr. Ford. We are going to have a look on Big Data Analytics. Let’s have a look on advantages of big data analytics. It helps marketing companies build models based on historical data to predict...
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...the era of ‘big data’? Brad Brown, Michael Chui, and James Manyika Radical customization, constant experimentation, and novel business models will be new hallmarks of competition as companies capture and analyze huge volumes of data. Here’s what you should know. The top marketing executive at a sizable US retailer recently found herself perplexed by the sales reports she was getting. A major competitor was steadily gaining market share across a range of profitable segments. Despite a counterpunch that combined online promotions with merchandizing improvements, her company kept losing ground. When the executive convened a group of senior leaders to dig into the competitor’s practices, they found that the challenge ran deeper than they had imagined. The competitor had made massive investments in its ability to collect, integrate, and analyze data from each store and every sales unit and had used this ability to run myriad real-world experiments. At the same time, it had linked this information to suppliers’ databases, making it possible to adjust prices in real time, to reorder hot-selling items automatically, and to shift items from store to store easily. By constantly testing, bundling, synthesizing, and making information instantly available across the organization— from the store floor to the CFO’s office—the rival company had become a different, far nimbler type of business. What this executive team had witnessed first hand was the gamechanging effects of big data. Of course...
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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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...Definition IoT is one of the fastest growing technologies in computing. It is an environment where people, animals, or objects are presented with unique identifiers and the ability to transfer data over a network (Rouse, 2014). It has emerged from combining wireless technologies, micro-electromechanical systems, and the internet (Rouse, 2014). See Figure 1. These wireless technologies are equipped with, or connected to a smart device allowing data collection and communication through the internet (Caron, Bosua, Maynard, & Ahmad, 2016). Figure 1. IoT Ecosystem (Medici, 2015) Benefits * Tracking behavior for real-time marketing (Borne, 2014). * Enhanced situational awareness (Borne, 2014). * Sensor-driven decision analytics (Borne, 2014). * Process optimization (Borne, 2014). * Optimized resource consumption (Borne, 2014). * Instantaneous control and response in complex autonomous systems (Borne, 2014). * Increase operational efficiency, power new business models, and improve quality of life (Harrell, 2015). * Provide an accurate analysis of customer data (Medici, 2015). Some Applications of IoF Business intelligence (BI). “The BI application ensures the analysis and measurement of the consumer’s thoughts, behaviors, relationships, buying attitudes, choices, and many more parameters that form the backbone of effective strategy building, business operations management, customer relationship management, and other business operations” ...
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...Disruptive Innovation: A new era of Crowdsourced Data Analytics! Abstract: The existing business paradigm of data analytics is set for a transformation. Today, companies are experimenting to replicate the “Outsourced data analytics” model to “Crowdsourced data analytics”. Companies like Kaggle, Crowdanalytix and others are hitting the headlines of top analytics blogs across the globe. The reason is that the new business model promises a drastic decrease in the cost of analytics for companies long with the flexibility to get the problem solved anytime with much less effort. In short, it’s not just crowdsourcing that is the novelty of the concept, but the manner in which it is put to use that steals the show. Abstract: The existing business paradigm of data analytics is set for a transformation. Today, companies are experimenting to replicate the “Outsourced data analytics” model to “Crowdsourced data analytics”. Companies like Kaggle, Crowdanalytix and others are hitting the headlines of top analytics blogs across the globe. The reason is that the new business model promises a drastic decrease in the cost of analytics for companies long with the flexibility to get the problem solved anytime with much less effort. In short, it’s not just crowdsourcing that is the novelty of the concept, but the manner in which it is put to use that steals the show. General Management General Management MBA Core, 2nd Year MBA Core, 2nd Year Ayush Malhotra NMIMS,Mumbai Ayush Malhotra ...
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...national boundary, complex interdependencies are built into it. As the power base continues to shift from companies towards customers, customer demands have gotten more complex. Companies are looking at Big Data analytics to revamp their supply chain, thereby using Big Data Analytics as a strategic lever. Companies are collecting vast amount of supply chain related data with help of technologies such as sensors, Barcode and GPS, Jacob House (2014). Big Data Analytics offers companies the ability to leverage on the enormous amounts of information driving their global supply chains, Harvard Business review, (2013). Companies are aware that Big Data can be leveraged at various levels of a business. This holds true for supply chain management also. The combination of large, high velocity and varied structure of big data and advanced analytics tools and techniques represents the next frontier of supply chain innovation, Libor K, Christian G, and Michele B...
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...The New Frontier: Data Analytics Phylicia Marie Phillips Professor Progress Mtshali, Ph. D. Information Systems Decision-Making April 17, 2016 In the past, analytics was reserved for back-room debates by data geeks producing monthly reports on how things are going. Today, analytics make a difference in how a company does business, day to day, and even minute by minute; more specifically how Walmart does business. As many know, Walmart is an American based multinational retail corporation that operates a chain of hypermarkets, grocery stores and discount stores. With over eleven thousand stores and clubs in 27 countries, information technology and data analytics play a major role in Walmart’s survival and helps maintain its competitive advantage. Data Analytics Overview The business intelligence and analytic technologies and applications currently adopted in industry can be considered as BI&A 1.0, where data are mostly structured, collected by companies through various legacy systems, and often stored in commercial relational database management systems (Bottles and Begoli, 2014). The analytical techniques most commonly used in these systems, popularized in the 1990s, are mainly grounded in statistical methods developed in the 1970s and data mining techniques developed in the 1980s (Chiang, 2012). The digitalization of information has created more data and the development of cloud computing, and faster and faster computers has made the increased data more accessible...
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...of my company, I am not familiar with the names or metrics used to evaluate important data, but I do know from experience and part of my job function, reports and data gathered are used to make judgments and decisions about new products and constant improvements for existing services we currently provide. Surveys are completed by our travel partners and guests, and even employees. We compile reports and present them to management electronically. Our research, experience, and use of different applications, along with our IT departments, helps management and executives determine which direction to move forward. Feedback from our travel partners and guests are both direct and indirect. Upon learning about programs such as Google Analytics, the importance of webpage layout, the amount of time spent on our site, as well, as how often individuals contact our chat system and sales automation for assistance with our product gives insight to “how we are doing” as a company. Constant looping and revisiting certain pages...
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...biggest retailer in the world and handles more than one million customer transactions every hour and generates more than 2.5 petabytes of data storage (Venkatraman & Brooks, 2012). To put this into perspective, this data is equivalent to 167 times the number of books in America’s Library of Congress (Venkatraman & Brooks, 2012). So how can Wal-Mart use this massive amount of data and what useful information can this data provide? This paper will provide a brief overview of the importance of Business Intelligence (BI) and how the largest retailer in world, Walmart, is using it. BI platforms help management to truly understand its customer base and deliver individualized products and services (Brannon, 2010). When BI tools and analytics are used effectively, managers and decision makers can yield an all-encompassing view of the company, its position in the market, and its potential and perspectives (Albescu, and Pugna 2014). BI is best explained as a systematic process not found in a magazine, online or in a knowledge database. An organization that doesn’t have a viable BI capacity is making decisions without key information in this competitive market (Thomas, 2001). Walmart has more customer connections than any retailer in the world, from online activity to in-store purchases, and even social mentions (300,000 social mentions per week) (SAS Institute Inc.). Due to the abundance of information requiring analysis, Walmart created Walmart Labs after the company took...
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...Introduction Sprint has nearly 54 million customers and offers a host of products for consumers, businesses and government. The company recently began using analytics tools to try to make sense of the mountains of data created by Sprint network users on a daily basis. With approximately 70,000 employees worldwide and nearly $27 billion in annual revenues, Sprint is widely recognized for developing, engineering and deploying state-of-the-art network technologies, including the United States' first nationwide all-digital, fiber-optic network and an award-winning Tier 1 Internet backbone. Sprint provides local voice and data services in 18 states and operates the largest 100-percent digital, nationwide wireless network in the United States. The decision to focus our project on Sprint was based on a couple of factors. Sprint is currently the 3rd largest telecom operator in USA and with the recent take over by SoftBank the prospects to grow look promising. This provides a clear opportunity to help the business grow using analytics. Secondly because of our connections in the company we were able to get more information on Sprint’s analytics strategy and future plans. Recommendations Sprint Telecom is part of an industry, which is one of the largest providers of data in the world. Sprint’s initial big data steps have been in the right direction. They have successfully used their current data sets for quick profits and short-term results. It is now time to take the jump...
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