...Problem 1: Data-Based Decision Making Supermarket Product Placement Suppose that we are responsible for managing product placement within a local supermarket. Our shelving units have 6 shelves each and are numbered from 1 to 6—with 1 being the lowest shelf and proceeding upward until the highest shelf is assigned the number 6. While there are many placement options that we should consider, we decide to look for any correlations between the row a product is placed on and its sales. Since we have our data stored in a data warehouse, it is easily accessible and responds quickly to our data request. Consider each of the following: · What judgments can you make regarding the placement of each type of product being considered? Answer - I think that we are more likely to place those items that are in higher demand by customers and those items that the company wants to generate the greatest profit from on the shelves that have the best sales · What is the consequence of making the wrong choice? Answer - Profit decreases, inventory doesn’t turn over · What types of products do you think each of the product groupings represent? Answer - Most likely to sell/greatest profitability to least likely to sell/lowest profitability · What target markets can you associate with each product group? Answer - ? Problem 2: Market Basket Analysis: Association Analysis Example 1: Our data mining program has performed association analysis and has generated...
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...USE OF DATA ANALYSIS IN MODELING Use of Data Analysis in Modeling Michael Matthews CIS 331: System Modeling Theory Strayer University Mark O’Connell, PHD March 5, 2013 The term “model” refers to a process of creating a representation of reality and working with this simplified representation in order to understand or control some part of the world (Barker, Powell 2004 pg. 11). A model can be used in varies ways such as business plains, forming a database, or building a structure. It can also be formed mentally, visually, and mathematically especially by data analysis. Data analysis is the process of raw data measured in order to determine the means based on that data. Although, data is relevant in producing a model, it is only used to provide general perspective of information, not to form a solution. By determining this objective, I will demonstrate the use of data analysis to form a model and the advantages and disadvantages that come with it. The techniques of a model are used constantly to understand the world and to predict the outcomes and actions. For example, a mental model come into play when one manager has to decide is hiring an older worker beneficial to the company. Another manager suggests that hiring older workers is a good idea because they bring valuable experience to the job. This mental model is the basis of decision making, one action forming an outcome (Barker, Powell 2004). The decision that is made, the advantage and disadvantage of hiring...
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...education Use of constant comparative analysis in qualitative research Hewitt-Taylor J (2001) Use of constant comparative analysis in qualitative research. Nursing Standard. 15, 42, 39-42. Date of acceptance: March 19 2001. Summary This article describes the application of constant comparative analysis, which is one method that can be used to analyse qualitative data. The need for data analysis to be congruent with the overall research design is highlighted. T HE AIM OF qualitative research is to portray the reality of the area under investigation, and to enhance understanding of the situation and the meanings and values attributed to this by individuals; it does not involve the quantification of facts (Rose 1994). Qualitative methods emphasise the value of individual experiences and views, as encountered in real-life situations. This type of investigation is often useful in nursing, as many issues concern the quality of the lived experience of individuals, which cannot be reduced to numerical values using statistical analysis. Sometimes a mixed methodology might be adopted, with elements of qualitative and quantitative enquiry being included in a study. The nature of qualitative enquiry means that volumes of ‘rich’, ‘deep’ data are produced, often from a variety of sources. While not seeking to reduce data to statistical evidence, qualitative data nevertheless requires systematic analysis. Given the volume of data produced, the practicalities of analysing...
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...Business AnAlytics And intelligence Course Starting: 29 June 2014 Application Deadline: Early Decision 28 February 2014 Regular Decision 15 April 2014 (Batch 5) (Classes conducted on-campus as well as off-campus) Certificate Programme on Business Analytics and Intelligence BATCH 5 in god We trust, All Others Must Bring data - W edwards deming he theory of bounded rationality proposed by nobel laureate Herbert Simon is evermore significant today with increasing complexity of the business problems; limited ability of human mind to analyze alternative solutions and the limited time available for decision making. introduction of enterprise resource planning (eRP) systems has ensured availability of data in many organizations; however, traditional eRP systems lacked data analysis capabilities that can assist the management in decision making. Business Analytics is a set of techniques and processes that can be used to analyse data to improve business performance through fact-based decision making. Business Analytics is the subset of Business intelligence, which creates capabilities for companies to compete in the market effectively. Business Analytics is likely to become one of the main functional areas in most companies. Analytics companies develop the ability to support their decisions through analytic reasoning using a variety of statistical and mathematical techniques. thomas devonport in his book titled, “competing on analytics: the new science of winning”, claims...
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...Question 1: What is the nature of Information? Explain how individuals interpret data to become information. Answer: Data that is (1) accurate and timely, (2) specific and organized for a purpose, (3) presented within a context that gives it meaning and relevance, and (4) can lead to an increase in understanding and decrease in uncertainty, is called an information. Information is valuable because it can affect behavior, a decision, or an outcome. For example, if a manager is told his/her company's net profit decreased in the past month, he/she may use this information as a reason to cut financial spending for the next month. A piece of information is considered valueless if, after receiving it, things remain unchanged. For a technical definition of information see information theory. Data analysis is a practice in which raw data is ordered and organized so that useful information can be extracted from it. The process of organizing and thinking about data is a key to understanding what the data does and does not contain. There are a variety of ways in which people can approach data analysis, and it is notoriously easy to manipulate data during the analysis phase to push certain conclusions or agendas. For this reason, it is important to pay attention when data analysis is presented, and to think critically about the data and the conclusions which were drawn. Raw data can take a variety of forms, including measurements, survey responses, and observations...
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...9 Data Collection 10 Demographic Details 11 Data Analysis 11 Conclusion 13 Recommendation 14 References 15 Appendices 17 APPENDIX 1: Ethics Form 17 APPENDIX 2: Preliminary Data Analysis 17 Interview Questions 17 APPENDIX 3: Thematic Data Analysis 17 Interview Questions 17 Abstract This study attempted to qualitatively investigate Australian student’s perception of the impact of excessive consumption of fast food on their health, studying in Flinders University. The sample consisted of University students with a diversity of demographic details, including sex, age, and the level of education. The researcher, a 27-year old student, used a purposeful sampling strategy to select respondents for the study. Four participants were convenient in making the results of the study dependable. He used four qualitative interviews as a method of data collection. While carrying out the interviews, the researcher used both open and closed ended questions. In order to ascertain that all the data was recorded, notes taking and audio recordings were used at every stage of the interview. Preliminary data analysis and thematic data analysis were used to analyze the overall recorded information. The purpose of the study is to offer viable recommendations that would assist in alleviating the underlying problem of fast foods among Australian students studying in Flinders University. Key Words: demographic details, Preliminary data analysis, thematic data analysis, viable...
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...expeetations of the customer. But how do you know what the customer wants; According to the textbooks, market research ought to provide the answer. Unfortunately, heeause of the way most traditional marketing research is conducted, it has fallen short of this important objective. At the core of the problem is the practice of using marketing research to confirm that a decision already made is the right decision rather than using market research to identify alternative choices and to support the process hy which the hest alternative is ehosen. In this series of foldout tables, I present a guide for managers who want to use market research to develop and support market-based decisions. The tables form a decision-support framework that uses research tools to help companies develop a balanced approach to the "technology push/demand pull" product development process. The structural components of the framework are accountable management, the company's decision process, marketplace reality, and the market research function. The five sections of the gateiold identify the steps researchers must take: (1) Assess market information needs; (2) Measure the marketplace; (3) Store, retrieve, and display the data; (4) Descrihe and analyze HARVARD BUSINESS REVTEW laiuuirv-Febiiiatv 1 ^ market information; (5) Evaluate the research and assess its usefulness. The reference material here is encyclopedic-the product of a great deal of thought by colleagues in business, market research and advertising agencies...
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...express their heartfelt gratitude appreciation to those who in one way or the other contributed to the possibility and fulfillment of the study; First and foremost, to the Almighty God, who bestowed His ample blessings and graces, and who continues to direct and assist us from the very beginning until the completion of the study. To the researcher’s parents, for giving them inspiration and all their needed support, whether financially, emotionally, and morally. To Dr. Evelyn Sorolla and Mrs. Iva Claire Latonero research teachers, for the professional guidance, excellent ideas and matchless patients that made this study complete. The researcher’s statistician, Ms. Michelle P. Bales for analyzing the statistical data, supporting their in all aspects and for sharing valuable knowledge. The researcher’s grammarian, Mr. John Martin P. Capicio for reviewing his/her English. And for always being there for the researchers. Mr. Edwin F. Mosura, for all the...
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...1.3 Problem Gartner research on Green 6 1.4 Background and Motivation 7 1.5 Audience 8 1.6 Outcome of Study 8 2 Introduction 10 2.1 Green IT 10 2.2 Why IT Need to be Green 12 2.3 Benefits of Green IT 13 2.4 Companies Interest in Green IT 14 2.5 Green IT and Information Assurance and Security 14 2.6 Information Assurance and Security 15 2.7 Green IT from Information Assurance viewpoint 16 2.8 Dimension of Green IT 18 2.9 Green IT Initiatives 19 3 Introduction 21 3.1 Research design 21 3.2 Justification of paradigm and methodology 22 3.3 Data Collection Methods 23 3.3.1 Questionnaires 23 3.3.2 Interview 24 3.4 Data Analysis and Interpretation 25 3.5 Ethical Considerations 25 3.6 Chapter Summary 26 4 Introduction 27 4.1 Presentation and Analysis of data 28 4.1.1 Quantitative data analysis 29 4.1.2 Demographic Questions: 29 4.2 Technical question 32 4.2.1 Quantitative analysis 46 4.3 Quantitative analysis 46 4.4 Chapter summary: 47 4.5 Recommendation and suggestions 48 5 Introduction 49 5.1 Security assurance in cloud computing 50 5.1.1 Confidentiality 51 5.1.2 Correctness Assurance 51 5.1.3 Availability 51 5.1.4 Data Integrity 52 5.2 Security guideline 52 5.2.1 Cloud Service Provider Agent (CSPA) 54 5.2.2 Cloud Data Confidentiality Agent 55 5.2.3 Cloud Data Correctness Agent (CDCorA) 55 5.2.4 Cloud Data Availability Agent (CDAA) 56 5.2.5 Cloud Data Integrity...
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...internationally. The occurrence of childhood obesity has increased rapidly over the years. This paper will show how the results of the data collection method, the data analysis procedure, and conclusion of applying the background and methodology of the research process to problems in health care with an emphasis on childhood obesity. The following questions will be answered from the Syllabus University of Phoenix (2010): Data Collection In what way are the data collection procedures appropriate for this study? In what way were appropriate steps taken to protect the rights of subjects? In what way is the data collection tool used to support the reliability and validity of the study? Data Analysis In what ways are the data analysis procedures appropriate for the data collected? In what ways are the data analysis procedures appropriate for answering the research question or questions, for testing the study hypothesis or hypotheses, or both? What are the key distinctions between qualitative and quantitative data? Conclusion Summarize the findings of the study. Identify the strengths of the scientific merit of this study. What are weaknesses? Identify the major limitations of the scientific merit of this study? What are its weaknesses? Explain if the findings support the hypotheses (Week 3)? Data Collection Method The data collection method was appropriate for this study because children were involved and the research was based on previous studies already...
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...SKETCH OF SPECIALIZATION REPORT & EVALUATION FORMATS - 2014 S.No Content Page No. 1 Sketch of Report on Specialization Project 2-3 2 Comprehensive comparative table (Review of Literature) - Annexure -I 4 3 Sequence of the Report- Annexure-II 5 4 Evaluation weightage-Annexure -III 5 5 EVALUATION FORMAT- SP-2014 - At the end of 1st Week 6-7 6 EVALUATION FORMAT - SP-2014 - At the end of 2nd Week 7-8 7 EVALUATION FORMAT - SP-2014 - At the end of 3rd Week 8-9 8 EVALUATION FORMAT - SP -2014 - At the end of 4th Week 9-10 Sketch of Report on Specialization Project Objective The objective of Specialization Project is to provide of in depth learning in the area of specialization. This project is more focused on the students chosen area as a specialization and wants to have career. It should facilitate the student to have a deeper understanding in his/her specialization. Details of Specialization Project The Specialization project should be done by every student as a part of their course which carries 100 marks and will be included in the marks sheet. This project provides an opportunity to get more insights on the topic chosen by the student. In the process, the student will get a chance to (a) evaluate the applicability of the textbook theories in real time situations and (b) to analyze the performance of entities and markets using statistical and analytical methods...
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...the true value of information and how it can affect the bottom line. The majority of successful organizations will employ business analysis staff to ensure that the data they get is properly interpreted to help support business-critical choices and strategies. By making use of the swathes of data available about any number of metrics within a company, clear and relevant insight into issues can be translated into more effective decisions and outcomes, based on verifiable knowledge. But these decisions often need to be taken quickly, across multiple departments and divisions, and this need for agility is only going to increase. Customers are only one half of the key to the survival and eventual prosperity of any business, and so it is to the data they provide which organizations must turn if they are to uncover the key themes which require immediate action. Genuinely actionable customer intelligence is the other side of the equation – information which metaphorically ‘gathers dust on a shelf’ is completely pointless. Gathering data must be done in conjunction with a proper strategy for analysis in mind, otherwise it simply becomes a number-gathering exercise in futility. In this paper, we look at the opportunities and limitations offered by current models of data analysis, and how adopting a strategy of combining both statistical and contextual analysis can provide the extra edge needed in an environment where decisions must be made quickly. READ ON... Teneo Insight Delivering...
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...INTRODUCTION Big data is described as complex data collections that has very large size beyond the capability of traditional tools to capture, manage and process it efficiently. It is also use in many ways where it usually refers to both of data that being managed and used to store and process it by the technology which it helps to deduce different types of results using the same data that being stored intentionally or unintentionally. There are three characteristics of big data which are volume, velocity and variety. When it comes to volume, the name of big data itself indicates that it is related to its enormous size. Nowadays, data is being produced by networks, machine as well as system such as social media which this is use by human...
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...Research Methodology Name Institutional Affiliations Chapter III: Research Methodology 3.1 Introduction The methodology chapter will identify and discuss the methods of research applied in the current study and justify their ability to achieve the predetermined objectives and aims. The principal concepts for discussion in the chapter will include the type of research, time and location of research, sampling and data collection, measures of variables, data analysis, and the ethical consideration in the order. The selection of the research methodology is crucial in the achievement of the aims and objectives and, as a result, it should have a significant level of priority and consideration of the expectations and most viable options. A reflection of the reality and practicability of abstract ideas are key concepts in the development of a realistic and highly performing approach to research that will reach the laid expectations through the use of the available instruments and knowledge (Creswell, 2014). The methodology adopted for a study should always be the one with the potential to provide the best results with the input of the least resources, especially with the consideration of the value of time. The introduction and literature review chapters of the dissertation provide the foundation for the current section as they provide the definitions and relationships of the subjects. The hypotheses developed from the cumulated knowledge are the principal measurements necessary...
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...Introduction to Data Warehousing and Business Intelligence Slides kindly borrowed from the course “Data Warehousing and Machine Learning” Aalborg University, Denmark Christian S. Jensen Torben Bach Pedersen Christian Thomsen {csj,tbp,chr}@cs.aau.dk Course Structure • Business intelligence Extract knowledge from large amounts of data collected in a modern enterprise Data warehousing, machine learning Acquire theoretical background in lectures and literature studies Obtain practical experience on (industrial) tools in practical exercises Data warehousing: construction of a database with only data analysis purpose • Purpose Business Intelligence (BI) Machine learning: find patterns automatically in databases 2 •1 Literature • Multidimensional Databases and Data Warehousing, Christian S. Jensen, Torben Bach Pedersen, Christian Thomsen, Morgan & Claypool Publishers, 2010 • Data Warehouse Design: Modern Principles and Methodologies, Golfarelli and Rizzi, McGraw-Hill, 2009 • Advanced Data Warehouse Design: From Conventional to Spatial and Temporal Applications, Elzbieta Malinowski, Esteban Zimányi, Springer, 2008 • The Data Warehouse Lifecycle Toolkit, Kimball et al., Wiley 1998 • The Data Warehouse Toolkit, 2nd Ed., Kimball and Ross, Wiley, 2002 3 Overview • • • • Why Business Intelligence? Data analysis problems Data Warehouse (DW) introduction DW topics Multidimensional modeling ETL Performance optimization 4 •2 What is Business Intelligence (BI)? • From...
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