...Data Mining Jenna Walker Dr. Emmanuel Nyeanchi Information Systems Decision Making May 30, 2012 Abstract Businesses are utilizing techniques such as data mining to create a competitive advantage customer loyalty. Data mining allows business to analyze customer information, such as demographics and purchase history for a better understanding of what the customers need and what they will respond to. Data mining currently takes place in several industries, and will only become even more widespread as the benefits are endless. The purpose of this paper is to gain research and examine data mining, its benefits to businesses, and issues or concerns it will need to overcome. Real world case studies of how data mining is used will also be presented for a deeper understanding. This study will show that despite its disadvantages, data mining is an important step for a business to better understand its customers, and is the future of business marking and operational planning. Tools and Benefits of data mining Before examining the benefits of data mining, it is important to understand what data mining is exactly. Data mining is defined as “a process that uses statistical, mathematical, artificial intelligence, and machine-learning techniques to extract and identify useful information and subsequent knowledge from large databases, including data warehouses” (Turban & Volonino, 2011). The information identified using data mining includes patterns indicating trends...
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...Data Mining Prepared by: Kirsten Sullivan Strayer University CIS 500 Dr. Baab September 9, 2012 Data mining is a concept that companies use to gain new customers or clients in an effort to make their business and profits grow. The ability to use data mining can result in the accrual of new customers by taking the new information and advertising to customers who are either not currently utilizing the business's product or also in winning additional customers that may be purchasing from the competitor. Generally, data are any “facts, numbers, or text that can be processed by a computer.”1 Today, organizations are accumulating vast and growing amounts of data in different formats and different databases. This includes operational or transactional data such as, sales, cost, inventory, payroll, and accounting. Data mining also known as “knowledge discovery”, is the process of analyzing data from different perspectives and summarizing it into useful information- information that can then be used to increase revenue, cuts costs, and continue the goals outlined for the company. Data mining consists of five major elements: “Extract, transform, and load transaction data onto the data warehouse system, store and manage the data in a multidimensional database system, provide data access to business analysts and information technology professionals, analyze the data by application software, present the data in a useful format, such as a graph or table.”2...
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...Information Analysis and Data Management Trends in Information Analysis and Data Management Over the last decade, advancements in digital technology have enabled companies to collect huge amounts of new information. This data is so large in scope, it has traditionally been difficult to process and analyze this information using standard database management systems such as SQL. The commoditization of computer technology has created a new paradigm in which data can be analyzed more efficiently and effectively than ever before. This report analyzes the some of the most important changes that are currently taking place within this new paradigm. The first part of this report covers trends in database analysis by analyzing the field of data mining. The report covers the topic of data mining by providing an explanation of it, and then by providing examples of real-world examples of data mining technology. Benefits and challenges of data mining are then provided. The second part of the report outlines an even more recent trend in data science, which is the increasing usage of noSQL databases to analyze “big data,” also referred to web-scale datasets. The most recent and major technological developments in the industry are then provided and described. Data Mining Background & Definition Data mining involves the process of discovering and extracting new knowledge from the analysis of large data sets. This is most often done through the use of data mining software, which identifies...
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...importance of data, the management issues that relate to it, and its life cycle. Other objectives include relating data management to multimedia and document management, explaining the concept of data warehousing, data mining, analytical processing, and knowledge discovery management. An Overview Section 12.1 – The Need for Business Intelligence – The section serves as an overview of Business Intelligence and its use in business. It discusses the problems associated with disparate data stores where data are not integrated into a single reporting system. The section discusses the technologies involved in Business Intelligence and the vendors involved. It also talks about predictive analytics, alerts and decision support. Section 12.2 – BI Architecture, Reporting and Performance Management – This section discusses the modes of data extraction and integration into a standardized, usable and trustworthy one. It also discusses the different types of reporting systems available to organizations, data mining, query and analysis. The section provides an insight into Business Performance Management (BPM) as a way for business managers to know if their organizations are achieving their strategic goals Section 12.3 – Data, Text and Web Mining and BI Search – This section discusses data mining technology, tools, and techniques. Information types, data mining applications, text mining, and web mining are explored. There is also a discussion of the failures of data mining. Section 12...
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...Data Mining/Data Warehousing Matthew P Bartman Strayer University Ibrahim Elhag CIS 111– Intro to Relational Database Management June 9, 2013 Data Mining/Data Warehousing When it comes to technology especially in terms of storing data there are two ways that it can be done and that is through data mining and data warehousing. With each type of storage there are trends and benefits. In terms of data warehousing there are 5 key benefits one of them being that it enhance business intelligence. What this means is that business processes can be applied directly instead of things having to be done with limited information or on gut instinct. Another benefit of data warehousing is that it can also save time meaning that if a decision has to be made the data can be retrieved quickly instead of having to find data from multiple sources. Not only does data warehousing enhance business intelligence and save time but it can also enchance data quality and consistency.This is accomplished by converting all data into one common format and will make it consistent with all departments which ensures accuracy with the data as well. While these key benefits another one is that it can provide historical intelligence which means that analayze different time periods and trends to make future predictions. One other key benefit is that it provides a great return on investment. The reason being that a data warehouse generates more revenue...
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...HaylesStudentFernando del Rio PerezSID: 40201587/27/2012 | | Contents Contents 2 List of Tables 2 Reflective Piece1: SAP and Article Review 3 SAP- Company ,products/services and benefits 3 Article review: Enterprize resource planing in reengineering business…………….....6 Reflective piece 2: Impact of Internet Technology on ERP 7 Benefits and adrawbacks of Web - based ERP systems 8 Reflective Piece 3: Understanding of ERPsim Game 10 Reflective Piece 4: Data mining and competitive advantage 12 Applications of Data mining 13 Reflective Work 5: ERP Sim Experience 15 List of References 17 List of Tables Table 1. SAP Business suite applications 3 Table 2. List of benefits and drawback of ERP systems 5 Table 3. Benefits and drawbacks of web - based ERP systems 9 Table 4. Individual roles of the group members 10 REFLECTIVE PIECE 1 – SAP and Article Review SAP- SAP – company, products/service and benefits Formed in 1972, SAP (Systems, Applications, and Products in Data Processing) is seen today as the largest inter-enterprise software firm in the global market. Due to the wide range of products that they have, SAP has become the third largest independent software solutions supplier for businesses. The headquarter of the company is located In Walldorf, Germany, However, they have subsidies in over 50 countries and thousands of customers in more than 150 different countries. SAP is present in the America's, Asia and Europe (SAP 2012). ...
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...In today’s business environment, businesses must be able to sift through and analyze massive amounts of data to gain a competitive edge over their competition. Utilizing data mining techniques, businesses are given the ability to analyze data from different points of view and turn it into useful information that can be used to increase revenue, cut costs, or both (Jason.Frand, n.d.). In today’s environment, competitive businesses use what is known as “Predictive Analytics” to perform mining and analysis of their data. In fact, predictive analytics is a form of data mining that if used properly can automatically sort and index a company database to create a predictive model based off corporate knowledge (Eric Siegel, 2005). Predictive Analytics use business intelligence technology to produce a score known as a predictor, which is a measurable value for every customer or organizational element. Once data records such as where, when, and how purchases are made are correlated, a predictive predictor or score is created. This predictor, in conjunction with other information, can assist in informing businesses what actions to take in order to get the consumer to purchase the goods they are offering. In fact, the proper utilization of predictive analytics can optimize marketing campaigns, improve web site behavior, reduce customer response times, increase revenue, and cut costs. The way companies and customers interact and perform their daily business has changed throughout the years...
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........................................................................................................................... 2 INTRODUCTION ................................................................................................................................... 2 DATABASE MANAGEMENT SYSTEMS .................................................................................................. 2 Database ......................................................................................................................................... 2 Database Management System (DBMS) ......................................................................................... 2 Schemas, Instances and Data Independence.................................................................................. 3 DATA MODELS..................................................................................................................................... 3 Hierarchical Model .......................................................................................................................... 3 Network Model ............................................................................................................................... 4 Relational Model ............................................................................................................................. 5 CHAPTER 2 ..................................................................................................................
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...The five steps include Defining the problem, developing the research plan, collecting relevant information, developing findings, and taking marketing actions. Constraints in a decision are the restrictions placed on potential solutions to a problem such as limitations on time and money. The difference between primary and secondary data is that primary data are facts and figures that are newly collected for the project, while secondary data are facts and figures that have previously been recorded. Some advantages of secondary data are the time savings, and the low cost. disadvantages of secondary data include that the secondary data may be out of date and the categories might not be right for the researchers project. The difference between observational and questionnaire data are that observational data can be collected by mechanical, personal, or neuromarketing methods, while questionnaire data are facts and figures that are obtained by asking people. Personal Interview provides the greatest flexibility. The difference between a panel and experiment is that a panel is a sample of consumers or stores from which researches take a series of measurements, while an experiment obtains data by manipulating factors under controlled conditions to test cause and effect. Data mining differs from traditional marketing research because data mining is extracting information about someone through large databases, while traditional marketing research is analyzing information and recommending...
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........................................ 3 2.1 -‐ Business Intelligence -‐ Overview ............................................................................................... 3 2.2 -‐ Business Intelligence Tools ........................................................................................................ 4 2.2.1 -‐ On-‐line Analytical Processing .............................................................................................. 4 2.2.2 -‐ Data Mining ........................................................................................................................ 5 2.2.3 – Dashboards ........................................................................................................................ 6 2.2.4 -‐ Data Visualisation ............................................................................................................... 6 3 -‐ Role of Business...
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...DATA MINING FOR INTELIIGENCE LED POLICING The paper concentrates on use of data mining techniques in police domain using associative memory being the main technique. The author talks of constructing the data being easier and thus giving effective decision making. The author does mention making the process as simple as possible since there are not enough technically sound people into databases. The process involves a step procedural method. Further the author does explain the advantages of this system in police environment. The author mentions use of data mining activities by Dutch forces and how it makes the work easier to predict and analyze the scenario. The author talks about the tool and name given to it as Data detective. This tool involved a chunk of data stored in data warehouse. There has been a continuous development in the tool used here throughout the years making it more efficient than before. The data mining tool automatically predicts the trend and the lays down all the statistical report. This tool makes it easier for the police to pin out criminals and their trends easily. The process raises a challenge so that a predictive modeling can be developed better than before. The author talks about understanding the links and then predicting is important. The author also mentions that this involves pattern matching which is achieved by data mining. The tool also helps in automatic prediction of criminal nature matches a profile and this leads to be quite...
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...com/locate/techsoc Data mining techniques for customer relationship management Chris Rygielski a, Jyun-Cheng Wang b, David C. Yen a,∗ a Department of DSC & MIS, Miami University, Oxford, OH, USA b Department of Information Management, National Chung-Cheng University, Taiwan, ROC Abstract Advancements in technology have made relationship marketing a reality in recent years. Technologies such as data warehousing, data mining, and campaign management software have made customer relationship management a new area where firms can gain a competitive advantage. Particularly through data mining—the extraction of hidden predictive information from large databases—organizations can identify valuable customers, predict future behaviors, and enable firms to make proactive, knowledge-driven decisions. The automated, future-ori- ented analyses made possible by data mining move beyond the analyses of past events typically provided by history-oriented tools such as decision support systems. Data mining tools answer business questions that in the past were too time-consuming to pursue. Yet, it is the answers to these questions make customer relationship management possible. Various techniques exist among data mining software, each with their own advantages and challenges for different types of applications. A particular dichotomy exists between neural networks and chi-square automated interaction detection (CHAID). While differing approaches abound in the realm of data mining, the...
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...Big Data versus Big Dilemmas With the Relationship to Marketing Ethics MK 351 Abstract This paper reviews the relationship in current marketing strategies with data collections and their relationship to marketing ethics. By examining methods of data collections and uses of these practices it will show the allure to these marketing strategies. Advantages and disadvantages will be displayed in regards to the strategy of data collecting as part of a marketing process. This paper will additionally review the potential ethical dilemmas and concerns that can arise from companies holding this much personal data, which could lead to misuse, fraud and unethical practices. Big Data versus Big Dilemmas With the Relationship to Marketing Ethics In today’s technically advanced world, a door of data collection and marketing research has opened business around the world to learn the habits and trends of their customers. There are many advantages and disadvantages to collecting consumer’s information and using it as part of a marketing strategy. Companies have found great advantages to analyzing the information that they obtain from data collections to help better serve target market consumers while achieving a greater profit. While other firms misuse, take advantage or become victims themselves to the negative side of data gathering. At the expense of the consumers privacy some companies are making profits from tracking and selling their information to numerous buyers. As companies...
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...Business Intelligence: The term Business Intelligence was coined by Hans Peter Luhn of IBM wherein he describes the Business Intelligences as ability to find the interrelationships among the available data and guide the set of actions to reach the desired goal. What all an organization needs to be a leader in the market is information. Information can be available in large forms like web resources, text data, graphs and statistics. The more information a firm has the more powerful it is getting on. Firms need to assess the future market condition with the available previous and present data so as to be a leader. The major goal of Business Intelligence is to dwell in all the available information, refine it and organize it in such a way that right information is passed to right people through the right way. Now, data can be in vast amounts, of which some might be useful and some might not be useful. Business intelligence tools like reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics help the firms in sorting out the useful data. Business intelligence systems help the firms in taking decisions based up on the dwelled data. Thereof Business Intelligence Systems can also be called as Decision Support Systems. Business Intelligence uses technologies, processes, and applications to analyze mostly...
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...Data Mining for Fraud Detection: Toward an Improvement on Internal Control Systems? Mieke Jans, Nadine Lybaert, Koen Vanhoof Abstract Fraud is a million dollar business and it’s increasing every year. The numbers are shocking, all the more because over one third of all frauds are detected by ’chance’ means. The second best detection method is internal control. As a result, it would be advisable to search for improvement of internal control systems. Taking into consideration the promising success stories of companies selling data mining software, along with the positive results of research in this area, we evaluate the use of data mining techniques for the purpose of fraud detection. Are we talking about real success stories, or salesmanship? For answering this, first a theoretical background is given about fraud, internal control, data mining and supervised versus unsupervised learning. Starting from this background, it is interesting to investigate the use of data mining techniques for detection of asset misappropriation, starting from unsupervised data. In this study, procurement fraud stands as an example of asset misappropriation. Data are provided by an international service-sector company. After mapping out the purchasing process, ’hot spots’ are identified, resulting in a series of known frauds and unknown frauds as object of the study. 1 Introduction Fraud is a million dollar business and it is increasing every year. ”45% of companies worldwide have fallen victim...
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