...The New Frontier: Data Analytics CIS 500: Information Systems For Decision-Making Anywhere we travel on the internet we have been bombarded with seemingly codependent requests to, “Like US!”, “Follow US!”, “Watch US!”, “Share US!” from businesses of all types. If you think about it, this is exactly the dynamic. Businesses are in a dependent relationship, relying on the consumers for their survival. If a business wants to engage and remain in a viable relationship, it has to have a product/service the consumer desires and be able to deliver it in a manner that the consumer perceives as valuable. Even if a business has the “next greatest thing”, the consumer will not engage/continue in the relationship if the business does not or cannot provide it in a valuable way to the consumer. Amazon is one company that has been able to successfully develop this dynamic relationship. So then how does a business become and remain successful in the relationship? They need a better understanding of what the consumer wants by improving their Business Intelligence (BI). For decades, businesses have used statistics to analyze data to obtain information and insights for improving BI. With the advent of the computer, data analysis moved from statistical based inferences about the data to a more scientific method based on empirical results. This was the foundational begins of Data Analytics. Data Analytics, as defined in our course text is, specialized software, capabilities, and...
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...Evolution of Big Data Defining Big Data “Data-driven decision making, popularized in the 1980s and 1990s, has evolved into a vastly more sophisticated concept known as big data that relies on software approaches generally referred to as analytics”(Picciano, 2012). Big data is one of the great new frontiers of IT. Data is exploding so fast and the promise of deeper insights is so compelling that IT managers are highly motivated to turn big data into an asset that can be managed and exploited for their organization. In information technology-big data refers to the extremely large volume of data that needs to be captured, stored, searched, shared, analyzed and visualized. Big Data is not just about lots of data, it is actually a concept providing an opportunity to find new insight into and organizations existing data as well guidelines to capture and analyze future data. It makes any business more agile and robust so it can adapt and overcome business challenges. It is an overall measurement of large complex data, specifically data that falls into the “3V’s model”: high volume, high velocity, and high variety. For example, big data can be applied to the wealth of information derived from social media or data obtained from the billions of mobile phones in use daily. In a business sense, big data is a part of analytics that relies on analytic application to confirm anticipated information and trends about...
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...white paper | 2012 The New Frontier in Telecom Analytics: Get Better Insight Faster The dynamic nature of today’s telecom customers requires an equally dynamic use of analytics to understand customers and make decisions. ©2012 Peppers & Rogers Group. All rights protected and reserved. 2 Telecom operators are under pressure to deliver, and deliver fast. There is tremendous focus on always-on mobile connectivity and access to information when and where we want it, both in the consumer and business worlds. Telecom operators see this new normal firsthand, providing data networks and technology convergence to bring information and access to their customers in the moment. As a result, it’s become critical for operators to have the same type of fast access and interaction with their customer data and analytics. The current competitive landscape demands it. Are traditional analytical models enough? No. If operators are changing the way they do business, this shift must apply to analytics as well. It’s become imperative to monitor the instant changes in customers’ behaviors and match them with the most relevant offer as soon as the customer needs it. This is achieved with a sophisticated blend of analytics and business sense. This white paper explores the opportunities of two dynamic analytical capabilities: transactional behavioral analysis and capturing data potential. These tools give operators real-time insight about their customer activity so they can take action to be as...
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...Financial Services Practice Journey III: The Next Frontier in Property and Casualty Insurance The Challenge of Profitable Growth Journey III: The Next Frontier in Property and Casualty Insurance The Challenge of Profitable Growth Contents Introduction Dramatic improvement and a paradigm shift 1 A Paradigm Shift The exit of undisciplined capacity Strengthened risk management skills Stronger foundational pillars The Implications The risk journey continues – with profitable growth as the new frontier 7 9 10 13 17 Continuous improvement of risk management Growing profitably in a competitive industry Lessons and barriers Levers to drive growth Upgrading the foundational pillars Conclusion Looking forward 17 21 28 38 43 Journey III: The Next Frontier in Property and Casualty Insurance The Challenge of Profitable Growth 1 Introduction Dramatic improvement and a paradigm shift In our first Journey report, covering 1979 to 1993, we reached three main conclusions about performance in the property and casualty insurance industry: 1. Risk management is the primary driver of success, not investment performance, expense management or financial leverage. We suggested that risk management had four components: enterprise risk management, capital management, product market management, and transaction excellence. 2. Performance is remarkably consistent and selfreinforcing. Winners keep winning, while persistent underperformers tend to be acquired...
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...------------------------------------------------- Chapter 2—Production Possibilities Frontier Framework MULTIPLE CHOICE 1. Points outside (or beyond) the PPF are a. | attainable. | b. | unattainable. | c. | efficient. | d. | inefficient. | ANS: B PTS: 1 DIF: Difficulty: Easy NAT: BUSPROG: Analytic LOC: DISC: Scarcity, tradeoffs, and opportunity cost KEY: Bloom's: Comprehension 2. Which of the following statements is true? a. | In a world of efficiently used scarce resources, more of one good necessarily means less of some other good. | b. | The law of increasing opportunity costs assumes that all people have the same ability to produce goods. | c. | Efficiency implies that it is impossible to get more of one good without getting less of another. | d. | Even if a country has unemployed resources, it can still be operating on its production possibilities frontier (PPF). | e. | a and c | ANS: E PTS: 1 DIF: Difficulty: Challenging NAT: BUSPROG: Analytic LOC: DISC: Efficiency and equity KEY: Bloom's: Comprehension 3. Through war, many of the factories in country 1 are destroyed and many of its people are killed. As a result, the country's a. | production possibilities frontier (PPF) after the war has probably shifted to the right compared to its PPF prior to the war. | b. | PPF after the war has probably shifted to the left compared to its PPF prior to the war. | c. | PPF after...
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...Brief Overview of Business Intelligence and How Walmart Uses It Walmart is the 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...
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...Cloud computing and analytics Name: Course: Tutor: Date: 1. Introduction Organizations and individuals collect vast amounts of data in their day to day operations. Data growth comes from multiple sources: From the use of computing within an enterprise for business functions such as commerce, customer service, and resource management. Use of communication devices, including computers, tablets, and mobile phone by individuals for both work and personal use also increases the daily data collected. The instrumentation of physical infrastructure, such as electrical grids, highways, and buildings for more efficient monitoring and management, opens the opportunity to collect a lot of data. It is expected that the trend will continue as both enterprises and individuals find value in access to information and communication. The challenge lies in managing the dynamic nature of the data, keeping the data secure and applying the right analytic technique to use the information most effectively. New advances in computing technologies make it possible for organizations to take full advantage of the vast amount of data they collect. 2. Architecture Design Cloud computing is an umbrella term. It encompasses many types of services. What cloud computing does is take a process anchored to one company, one data center and one facility. It enables businesses to move from working within their own IT bubble and use the cloud to access technologies they need, when they need them, at the scale they...
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...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 who...
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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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...REVOLUTION ANALYTICS WHITE PAPER Advanced ‘Big Data’ Analytics with R and Hadoop 'Big Data' Analytics as a Competitive Advantage Big Analytics delivers competitive advantage in two ways compared to the traditional analytical model. First, Big Analytics describes the efficient use of a simple model applied to volumes of data that would be too large for the traditional analytical environment. Research suggests that a simple algorithm with a large volume of data is more accurate than a sophisticated algorithm with little data. The algorithm is not the competitive advantage; the ability to apply it to huge amounts of data—without compromising performance—generates the competitive edge. Second, Big Analytics refers to the sophistication of the model itself. Increasingly, analysis algorithms are provided directly by database management system (DBMS) vendors. To pull away from the pack, companies must go well beyond what is provided and innovate by using newer, more sophisticated statistical analysis. Revolution Analytics addresses both of these opportunities in Big Analytics while supporting the following objectives for working with Big Data Analytics: 1. 2. 3. 4. Avoid sampling / aggregation; Reduce data movement and replication; Bring the analytics as close as possible to the data and; Optimize computation speed. First, Revolution Analytics delivers optimized statistical algorithms for the three primary data management paradigms being employed to address...
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...c.|the value of her nap less the value of attending class.| d.|nothing, since she would valued sleep more than attendance at class.| ____ 3. An increase in the overall level of prices in an economy is referred to as a.|the income effect.| b.|inflation.| c.|deflation.| d.|the substitution effect.| ____ 4. An economic outcome is said to be efficient if the economy is a.|using all of the scarce resources it has available.| b.|conserving on resources, rather than using all available resources.| c.|getting all it can get from the scarce resources it has available.| d.|able to produce more than what is currently being produced without additional resources.| ____ 5. When an economy is operating at a point on its production possibilities frontier, then a.|consumers are content with the mix of goods and services that is being produced.| b.|there is no way to produce more of one good without producing less of the other.| c.|equal amounts of the two goods are being produced.| d.|All of the above are correct.| Figure 2-8 |Panel (a)||Panel (b)| || ____ 6. Refer to Figure 2-8, Panel (a). The opportunity cost of moving from point J to point L is a.|2 donuts.| b.|2 donuts and 2 cups of coffee.| c.|2 cups of coffee.| d.|6 cups of coffee.| ____ 7. If an...
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...head: Analytic Competitors Analytic Competitors Lev Mallinger Grand Canyon University BUS 606: Quantitative Methods August 24, 2012 “A wise man is strong and a knowledgeable man increases in his strength” (Proverbs 24:5) Introduction An analytic competitor may be described as an organization engaged in the committed activities of accumulating data, organizing and analyzing it into meaningful information, and following through with business decisions and activities informed by that information. An uninformed layperson may assume most businesses operate as an analytic competitor. This, however, may not be so. Many time business decisions are made through anecdotal or subjective non-quantitative means. An example of this might be a person wanting to open up a restaurant in town. He sees there are no 24 hour diners in the town, so he figures that since he likes diner food, other people must also and without competition he will have a good chance to succeed. It is very possible that he will succeed, but his chances of success might improve if he were to do some research beforehand. Perhaps he could gather data that informed him more about his potential customer base. How interested are they in diner food? Do the town’s people engage in night time activity that would have them out of the house during the night and early morning hours? Buy collecting data and analyzing it the restaurateur can better plan his offerings and hours of operations. While being an analytic competitor...
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...Internet is the major source which has resulted in the tsunami of data in the past few years. Big data is too big, it moves too fast, and doesn’t fit the structures of our existing database architectures. It is like an ocean of data in which we people swim in every day with an effort to come on the surface, but every day the level of data increases tremendously. Gone are the days when memory was used to be measured in Gigabytes or Terabytes or Petabytes, today it is measured in exabytes, zettabytes or yottabytes. With Big Data solutions, organizations can dive into all data and gain valuable insights that were previously unimaginable. The term “big data” can be pretty nebulous, in the same way that the term “cloud” covers diverse technologies....
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...high quality. As noted in the Graduate School requirements, "Graduate programs leading to the Master of Arts, Master of Science, or Doctor of Philosophy degrees emphasize the development of the student's ability for independent scholarly work and the creation of new knowledge through research. Practice-oriented programs, which ordinarily lead to the degree of master or doctor in a particular professional field, emphasize preparation of the student for professional practice at the frontiers of existing knowledge." Both capstone projects and theses should have a clear statement of the problem or issue to be addressed; a literature review which covers the important work related to the problem, with content clearly relating to the statement of problem; analysis of results; and statement of conclusions. When there is a question as to whether the proposal is a thesis or a capstone project, the proposal shall be submitted to the EDP Program Director for a decision. This must be done prior to registering for thesis or capstone project credits. The thesis should answer a question which contributes to new knowledge and is generalizable beyond a single setting. The thesis should be analytic, should systematically analyze data, and should develop and make appropriate inferences based on the analysis. The capstone project may answer a question of practical importance, or develop, test, and evaluate an intervention, curriculum, or protocol for application within a particular agency...
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...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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