Business Problems Solved By Analytics Examples

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  business problems solved by analytics examples: Business Analytics Walter R. Paczkowski, 2022-01-03 This book focuses on three core knowledge requirements for effective and thorough data analysis for solving business problems. These are a foundational understanding of: 1. statistical, econometric, and machine learning techniques; 2. data handling capabilities; 3. at least one programming language. Practical in orientation, the volume offers illustrative case studies throughout and examples using Python in the context of Jupyter notebooks. Covered topics include demand measurement and forecasting, predictive modeling, pricing analytics, customer satisfaction assessment, market and advertising research, and new product development and research. This volume will be useful to business data analysts, data scientists, and market research professionals, as well as aspiring practitioners in business data analytics. It can also be used in colleges and universities offering courses and certifications in business data analytics, data science, and market research.
  business problems solved by analytics examples: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates
  business problems solved by analytics examples: Business Analytics for Decision Making Steven Orla Kimbrough, Hoong Chuin Lau, 2018-09-03 Business Analytics for Decision Making, the first complete text suitable for use in introductory Business Analytics courses, establishes a national syllabus for an emerging first course at an MBA or upper undergraduate level. This timely text is mainly about model analytics, particularly analytics for constrained optimization. It uses implementations that allow students to explore models and data for the sake of discovery, understanding, and decision making. Business analytics is about using data and models to solve various kinds of decision problems. There are three aspects for those who want to make the most of their analytics: encoding, solution design, and post-solution analysis. This textbook addresses all three. Emphasizing the use of constrained optimization models for decision making, the book concentrates on post-solution analysis of models. The text focuses on computationally challenging problems that commonly arise in business environments. Unique among business analytics texts, it emphasizes using heuristics for solving difficult optimization problems important in business practice by making best use of methods from Computer Science and Operations Research. Furthermore, case studies and examples illustrate the real-world applications of these methods. The authors supply examples in Excel®, GAMS, MATLAB®, and OPL. The metaheuristics code is also made available at the book's website in a documented library of Python modules, along with data and material for homework exercises. From the beginning, the authors emphasize analytics and de-emphasize representation and encoding so students will have plenty to sink their teeth into regardless of their computer programming experience.
  business problems solved by analytics examples: Behind Every Good Decision Piyanka Jain, Puneet Sharma, 2014-11-05 There is a misconception in business that the only data that matters is BIG data, and that elaborate tools and data scientists are required to extract any practical information. However, nothing could be further from the truth. If you feel that you can’t understand how to read, let alone implement, these complex software programs that crunch the data and spit out more data, that will no longer be a problem! Authors and analytics experts Piyanka Jain and Puneet Sharma demystify the process of business analytics and demonstrate how professionals at any level can take the information at their disposal and in only five simple steps--using only Excel as a tool--make the decision necessary to increase revenue, decrease costs, improve product, or whatever else is being asked of them at that time. In Behind Every Good Decision, you will learn how to: Clarify the business question Lay out a hypothesis-driven plan Pull relevant data Convert it to insights Make decisions that make an impact Packed with examples and exercises, this refreshingly accessible book explains the four fundamental analytic techniques that can help solve a surprising 80 percent of all business problems. It doesn’t take a numbers person to know that is a formula you need!
  business problems solved by analytics examples: BASIC BUSINESS ANALYTICS USING R Dr. Mahavir M. Shetiya, Prof. Snehal V. Bhambure, 2023-11-10 Buy BASIC BUSINESS ANALYTICS USING R e-Book for Mba 2nd Semester in English language specially designed for SPPU ( Savitribai Phule Pune University ,Maharashtra) By Thakur publication.
  business problems solved by analytics examples: Applied Business Analytics Nathaniel Lin, 2015 Now that you've collected the data and crunched the numbers, what do you do with all this information? How do you take the fruit of your analytics labor and apply it to business decision making? How do you actually apply the information gleaned from quants and tech teams? Applied Business Analytics will help you find optimal answers to these questions, and bridge the gap between analytics and execution in your organization. Nathaniel Lin explains why analytics value chains often break due to organizational and cultural issues, and offers in the trenches guidance for overcoming these obstacles. You'll learn why a special breed of analytics deciders is indispensable for any organization that seeks to compete on analytics; how to become one of those deciders; and how to identify, foster, support, empower, and reward others who join you. Lin draws on actual cases and examples from his own experience, augmenting them with hands-on examples and exercises to integrate analytics at every level: from top-level business questions to low-level technical details. Along the way, you'll learn how to bring together analytics team members with widely diverse goals, knowledge, and backgrounds. Coverage includes: How analytical and conventional decision making differ -- and the challenging implications How to determine who your analytics deciders are, and ought to be Proven best practices for actually applying analytics to decision-making How to optimize your use of analytics as an analyst, manager, executive, or C-level officer
  business problems solved by analytics examples: Competing on Analytics Thomas H. Davenport, Jeanne G. Harris, 2007-03-06 You have more information at hand about your business environment than ever before. But are you using it to “out-think” your rivals? If not, you may be missing out on a potent competitive tool. In Competing on Analytics: The New Science of Winning, Thomas H. Davenport and Jeanne G. Harris argue that the frontier for using data to make decisions has shifted dramatically. Certain high-performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. Their secret weapon? Analytics: sophisticated quantitative and statistical analysis and predictive modeling. Exemplars of analytics are using new tools to identify their most profitable customers and offer them the right price, to accelerate product innovation, to optimize supply chains, and to identify the true drivers of financial performance. A wealth of examples—from organizations as diverse as Amazon, Barclay’s, Capital One, Harrah’s, Procter & Gamble, Wachovia, and the Boston Red Sox—illuminate how to leverage the power of analytics.
  business problems solved by analytics examples: Business Analytics Principles, Concepts, and Applications with SAS Marc J. Schniederjans, Dara G. Schniederjans, Christopher M. Starkey, 2014-10-07 Responding to a shortage of effective content for teaching business analytics, this text offers a complete, integrated package of knowledge for newcomers to the subject. The authors present an up-to-date view of what business analytics is, why it is so valuable, and most importantly, how it is used. They combine essential conceptual content with clear explanations of the tools, techniques, and methodologies actually used to implement modern business analytics initiatives. Business Analytics Principles, Concepts, and Applications with SAS offers a proven step-wise approach to designing an analytics program, and successfully integrating it into your organization, so it effectively provides intelligence for competitive advantage in decision making. Using step-by-step examples, the authors identify common challenges that can be addressed by business analytics, illustrate each type of analytics (descriptive, prescriptive, and predictive), and guide users in undertaking their own projects. Illustrating the real-world use of statistical, information systems, and management science methodologies, these examples help readers successfully apply the methods they are learning. Unlike most competitive guides, Business Analytics Principles, Concepts, and Applications with SAS demonstrates the use of SAS software, permitting instructors to spend less time teaching software and more time focusing on business analytics itself.
  business problems solved by analytics examples: Business Analytics Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, 2020-03-10 Present the full range of analytics -- from descriptive and predictive to prescriptive analytics -- with Camm/Cochran/Fry/Ohlmann's market-leading BUSINESS ANALYTICS, 4E. Clear, step-by-step instructions teach students how to use Excel, Tableau, R and JMP Pro to solve more advanced analytics concepts. As instructor, you have the flexibility to choose your preferred software for teaching concepts. Extensive solutions to problems and cases save grading time, while providing students with critical practice. This edition covers topics beyond the traditional quantitative concepts, such as data visualization and data mining, which are increasingly important in today's analytical problem solving. In addition, MindTap and WebAssign customizable digital course solutions offer an interactive eBook, auto-graded exercises from the printed book, algorithmic practice problems with solutions and Exploring Analytics visualizations to strengthen students' understanding of course concepts.
  business problems solved by analytics examples: An Introduction to Business Analytics Ger Koole, 2019 Business Analytics (BA) is about turning data into decisions. This book covers the full range of BA topics, including statistics, machine learning and optimization, in a way that makes them accessible to a broader audience. Decision makers will gain enough insight into the subject to have meaningful discussions with machine learning specialists, and those starting out as data scientists will benefit from an overview of the field and take their first steps as business analytics specialist. Through this book and the various exercises included, you will be equipped with an understanding of BA, while learning R, a popular tool for statistics and machine learning.
  business problems solved by analytics examples: Predictive Business Analytics Lawrence Maisel, Gary Cokins, 2013-09-26 Discover the breakthrough tool your company can use to make winning decisions This forward-thinking book addresses the emergence of predictive business analytics, how it can help redefine the way your organization operates, and many of the misconceptions that impede the adoption of this new management capability. Filled with case examples, Predictive Business Analytics defines ways in which specific industries have applied these techniques and tools and how predictive business analytics can complement other financial applications such as budgeting, forecasting, and performance reporting. Examines how predictive business analytics can help your organization understand its various drivers of performance, their relationship to future outcomes, and improve managerial decision-making Looks at how to develop new insights and understand business performance based on extensive use of data, statistical and quantitative analysis, and explanatory and predictive modeling Written for senior financial professionals, as well as general and divisional senior management Visionary and effective, Predictive Business Analytics reveals how you can use your business's skills, technologies, tools, and processes for continuous analysis of past business performance to gain forward-looking insight and drive business decisions and actions.
  business problems solved by analytics examples: Data Science and Big Data Analytics EMC Education Services, 2014-12-19 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!
  business problems solved by analytics examples: Analytics at Work Thomas H. Davenport, Jeanne G. Harris, Robert Morison, 2010 As a follow-up to the successful Competing on Analytics, authors Tom Davenport, Jeanne Harris, and Robert Morison provide practical frameworks and tools for all companies that want to use analytics as a basis for more effective and more profitable decision making. Regardless of your company's strategy, and whether or not analytics are your company's primary source of competitive differentiation, this book is designed to help you assess your organization's analytical capabilities, provide the tools to build these capabilities, and put analytics to work. The book helps you answer these pressing questions: What assets do I need in place in my organization in order to use analytics to run my business? Once I have these assets, how do I deploy them to get the most from an analytic approach? How do I get an analytic initiative off the ground in the first place, and then how do I sustain analytics in my organization over time? Packed with tools, frameworks, and all new examples, Analytics at Work makes analytics understandable and accessible and teaches you how to make your company more analytical.
  business problems solved by analytics examples: Introduction to Business Analytics Using Simulation Jonathan P. Pinder, 2022-02-06 Introduction to Business Analytics Using Simulation, Second Edition employs an innovative strategy to teach business analytics. The book uses simulation modeling and analysis as mechanisms to introduce and link predictive and prescriptive modeling. Because managers can't fully assess what will happen in the future, but must still make decisions, the book treats uncertainty as an essential element in decision-making. Its use of simulation gives readers a superior way of analyzing past data, understanding an uncertain future, and optimizing results to select the best decision. With its focus on uncertainty and variability, this book provides a comprehensive foundation for business analytics. Students will gain a better understanding of fundamental statistical concepts that are essential to marketing research, Six-Sigma, financial analysis, and business analytics. - Teaches managers how they can use business analytics to formulate and solve business problems to enhance managerial decision-making - Explains the processes needed to develop, report and analyze business data - Describes how to use and apply business analytics software - Offers expanded coverage on the value and application of prescriptive analytics - Includes a wealth of illustrative exercises that are newly organized by difficulty level - Winner of the 2017 Textbook and Academic Authors Association's (TAA) Most Promising New Textbook Award in the prior edition
  business problems solved by analytics examples: Fundamentals of Machine Learning for Predictive Data Analytics, second edition John D. Kelleher, Brian Mac Namee, Aoife D'Arcy, 2020-10-20 The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.
  business problems solved by analytics examples: Guide to Business Data Analytics Iiba, 2020-08-07 The Guide to Business Data Analytics provides a foundational understanding of business data analytics concepts and includes how to develop a framework; key techniques and application; how to identify, communicate and integrate results; and more. This guide acts as a reference for the practice of business data analytics and is a companion resource for the Certification in Business Data Analytics (IIBA(R)- CBDA). Explore more information about the Certification in Business Data Analytics at IIBA.org/CBDA. About International Institute of Business Analysis International Institute of Business Analysis(TM) (IIBA(R)) is a professional association dedicated to supporting business analysis professionals deliver better business outcomes. IIBA connects almost 30,000 Members, over 100 Chapters, and more than 500 training, academic, and corporate partners around the world. As the global voice of the business analysis community, IIBA supports recognition of the profession, networking and community engagement, standards and resource development, and comprehensive certification programs. IIBA Publications IIBA publications offer a wide variety of knowledge and insights into the profession and practice of business analysis for the entire business community. Standards such as A Guide to the Business Analysis Body of Knowledge(R) (BABOK(R) Guide), the Agile Extension to the BABOK(R) Guide, and the Global Business Analysis Core Standard represent the most commonly accepted practices of business analysis around the globe. IIBA's reports, research, whitepapers, and studies provide guidance and best practices information to address the practice of business analysis beyond the global standards and explore new and evolving areas of practice to deliver better business outcomes. Learn more at iiba.org.
  business problems solved by analytics examples: Business Analytics Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson, 2018-03-08 Build valuable skills that are in high demand in today’s businesses with Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' market-leading BUSINESS ANALYTICS, 3E. Readers master the full range of analytics while strengthening descriptive, predictive and prescriptive analytic skills. Real-world examples and visuals help illustrate data and results for each topic. Clear, step-by-step instructions guide readers through using various software programs, including Microsoft Excel, Analytic Solver, and JMP Pro, to perform the analyses discussed. Practical, relevant problems at all levels of difficulty reinforce and teach readers to apply the concepts learned. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
  business problems solved by analytics examples: Business Analytics Arul Mishra, Himanshu Mishra, 2024-02-27 Business Analytics: Solving Business Problems with R offers a practical, hands-on introduction to analytical methods, including machine learning in real-world business scenarios. Connecting business decisions and analytical methods across multiple fields, this book guides readers through a wide range of business problems and their fitting analytical solutions, offering examples and implementation using R.
  business problems solved by analytics examples: Modeling Techniques in Predictive Analytics Thomas W. Miller, 2015 Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you're new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you're already a modeler, programmer, or manager, it will teach you crucial skills you don't yet have. This guide illuminates the discipline through realistic vignettes and intuitive data visualizations-not complex math. Thomas W. Miller, leader of Northwestern University's pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more. Every chapter focuses on one of today's key applications for predictive analytics, delivering skills and knowledge to put models to work-and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively.
  business problems solved by analytics examples: The Analytics Lifecycle Toolkit Gregory S. Nelson, 2018-03-07 An evidence-based organizational framework for exceptional analytics team results The Analytics Lifecycle Toolkit provides managers with a practical manual for integrating data management and analytic technologies into their organization. Author Gregory Nelson has encountered hundreds of unique perspectives on analytics optimization from across industries; over the years, successful strategies have proven to share certain practices, skillsets, expertise, and structural traits. In this book, he details the concepts, people and processes that contribute to exemplary results, and shares an organizational framework for analytics team functions and roles. By merging analytic culture with data and technology strategies, this framework creates understanding for analytics leaders and a toolbox for practitioners. Focused on team effectiveness and the design thinking surrounding product creation, the framework is illustrated by real-world case studies to show how effective analytics team leadership works on the ground. Tools and templates include best practices for process improvement, workforce enablement, and leadership support, while guidance includes both conceptual discussion of the analytics life cycle and detailed process descriptions. Readers will be equipped to: Master fundamental concepts and practices of the analytics life cycle Understand the knowledge domains and best practices for each stage Delve into the details of analytical team processes and process optimization Utilize a robust toolkit designed to support analytic team effectiveness The analytics life cycle includes a diverse set of considerations involving the people, processes, culture, data, and technology, and managers needing stellar analytics performance must understand their unique role in the process of winnowing the big picture down to meaningful action. The Analytics Lifecycle Toolkit provides expert perspective and much-needed insight to managers, while providing practitioners with a new set of tools for optimizing results.
  business problems solved by analytics examples: Financial Accounting Paul D. Kimmel, Jerry J. Weygandt, Jill E. Mitchell, Barbara Trenholm, Wayne Irvine, Christopher D. Burnley, 2023-05-15 Provide future business professionals with a practical introduction to financial accounting. With its unique focus on building students' decision-making skills and emphasis on financial statements, Financial Accounting, 9th Canadian Edition meaningfully integrates data analytics and the importance of using accounting information in real-world decision-making. Adaptive practice opportunities and engaging real-world industry examples strengthen student understanding of accounting concepts and illustrate how these are relevant to their everyday lives and future careers in business and accounting. An increased emphasis on Indigenous perspectives and environmental, social, and governance (ESG) issues serves as a first step in urging students to acknowledge their role as individuals, students, and aspiring professionals in addressing societal inequities.
  business problems solved by analytics examples: Business challenge and solve business analyst Praveshofficial, 2022-09-30 Business challenge and solve business analyst business regarding
  business problems solved by analytics examples: "ECEG2016-Proceedings of 16th European Conference on e-Government ECEG 2016 " Mitja De Man, Tina Juki, 2016-06-02
  business problems solved by analytics examples: Analytics Across the Enterprise Brenda L. Dietrich, Emily C. Plachy, Maureen F. Norton, 2014-05-15 How to Transform Your Organization with Analytics: Insider Lessons from IBM’s Pioneering Experience Analytics is not just a technology: It is a better way to do business. Using analytics, you can systematically inform human judgment with data-driven insight. This doesn’t just improve decision-making: It also enables greater innovation and creativity in support of strategy. Your transformation won’t happen overnight; however, it is absolutely achievable, and the rewards are immense. This book demystifies your analytics journey by showing you how IBM has successfully leveraged analytics across the enterprise, worldwide. Three of IBM’s pioneering analytics practitioners share invaluable real-world perspectives on what does and doesn’t work and how you can start or accelerate your own transformation. This book provides an essential framework for becoming a smarter enterprise and shows through 31 case studies how IBM has derived value from analytics throughout its business. Coverage Includes Creating a smarter workforce through big data and analytics More effectively optimizing supply chain processes Systematically improving financial forecasting Managing financial risk, increasing operational efficiency, and creating business value Reaching more B2B or B2C customers and deepening their engagement Optimizing manufacturing and product management processes Deploying your sales organization to increase revenue and effectiveness Achieving new levels of excellence in services delivery and reducing risk Transforming IT to enable wider use of analytics “Measuring the immeasurable” and filling gaps in imperfect data Whatever your industry or role, whether a current or future leader, analytics can make you smarter and more competitive. Analytics Across the Enterprise shows how IBM did it--and how you can, too. Learn more about IBM Analytics
  business problems solved by analytics examples: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Annotation This broad, deep, but not-too-technical guide introduces you to the fundamental principles of data science and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. By learning data science principles, you will understand the many data-mining techniques in use today. More importantly, these principles underpin the processes and strategies necessary to solve business problems through data mining techniques.
  business problems solved by analytics examples: Professional Hadoop Solutions Boris Lublinsky, Kevin T. Smith, Alexey Yakubovich, 2013-09-12 The go-to guidebook for deploying Big Data solutions with Hadoop Today's enterprise architects need to understand how the Hadoop frameworks and APIs fit together, and how they can be integrated to deliver real-world solutions. This book is a practical, detailed guide to building and implementing those solutions, with code-level instruction in the popular Wrox tradition. It covers storing data with HDFS and Hbase, processing data with MapReduce, and automating data processing with Oozie. Hadoop security, running Hadoop with Amazon Web Services, best practices, and automating Hadoop processes in real time are also covered in depth. With in-depth code examples in Java and XML and the latest on recent additions to the Hadoop ecosystem, this complete resource also covers the use of APIs, exposing their inner workings and allowing architects and developers to better leverage and customize them. The ultimate guide for developers, designers, and architects who need to build and deploy Hadoop applications Covers storing and processing data with various technologies, automating data processing, Hadoop security, and delivering real-time solutions Includes detailed, real-world examples and code-level guidelines Explains when, why, and how to use these tools effectively Written by a team of Hadoop experts in the programmer-to-programmer Wrox style Professional Hadoop Solutions is the reference enterprise architects and developers need to maximize the power of Hadoop.
  business problems solved by analytics examples: Augmented Analytics Willi Weber, Tobias Zwingmann, 2024-05-31 Augmented Analytics isn't just another book on data and analytics; it's a holistic resource for reimagining the way your entire organization interacts with information to become insight-driven. Moving beyond traditional, limited ways of making sense of data, Augmented Analytics provides a dynamic, actionable strategy for improving your organization's analytical capabilities. With this book, you can infuse your workflows with intelligent automation and modern artificial intelligence, empowering more team members to make better decisions. You'll find more in these pages than just how to add another forecast to your dashboard; you'll discover a complete approach to achieving analytical excellence in your organization. You'll explore: Key elements and building blocks of augmented analytics, including its benefits, potential challenges, and relevance in today's business landscape Best practices for preparing and implementing augmented analytics in your organization, including analytics roles, workflows, mindsets, tool sets, and skill sets Best practices for data enablement, liberalization, trust, and accessibility How to apply a use-case approach to drive business value and use augmented analytics as an enabler, with selected case studies This book provide a clear, actionable path to accelerate your journey to analytical excellence.
  business problems solved by analytics examples: Management, International Adaptation John R. Schermerhorn, Jr., Daniel G. Bachrach, 2023-11-20
  business problems solved by analytics examples: Data Driven Thomas C. Redman, 2008-09-22 Your company's data has the potential to add enormous value to every facet of the organization -- from marketing and new product development to strategy to financial management. Yet if your company is like most, it's not using its data to create strategic advantage. Data sits around unused -- or incorrect data fouls up operations and decision making. In Data Driven, Thomas Redman, the Data Doc, shows how to leverage and deploy data to sharpen your company's competitive edge and enhance its profitability. The author reveals: · The special properties that make data such a powerful asset · The hidden costs of flawed, outdated, or otherwise poor-quality data · How to improve data quality for competitive advantage · Strategies for exploiting your data to make better business decisions · The many ways to bring data to market · Ideas for dealing with political struggles over data and concerns about privacy rights Your company's data is a key business asset, and you need to manage it aggressively and professionally. Whether you're a top executive, an aspiring leader, or a product-line manager, this eye-opening book provides the tools and thinking you need to do that.
  business problems solved by analytics examples: Getting Started with Data Science Murtaza Haider, 2015-12-14 Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions, such as: • Are religious individuals more or less likely to have extramarital affairs? • Do attractive professors get better teaching evaluations? • Does the higher price of cigarettes deter smoking? • What determines housing prices more: lot size or the number of bedrooms? • How do teenagers and older people differ in the way they use social media? • Who is more likely to use online dating services? • Why do some purchase iPhones and others Blackberry devices? • Does the presence of children influence a family’s spending on alcohol? For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon.
  business problems solved by analytics examples: Data Analytics Applications in Latin America and Emerging Economies Eduardo Rodriguez, 2017-07-28 This book focuses on understanding the analytics knowledge management process and its comprehensive application to various socioeconomic sectors. Using cases from Latin America and other emerging economies, it examines analytics knowledge applications where a solution has been achieved. Written for business students and professionals as well as researchers, the book is filled with practical insight into applying concepts and implementing processes and solutions. The eleven case studies presented in the book incorporate the whole analytics process and are useful reference examples for applying the analytics process for SME organizations in both developing and developed economies. The cases also identify multiple tacit factors to deal with during the implementation of analytics knowledge management processes. These factors, which include data cleaning, data gathering, and interpretation of results, are not always easily identified by analytics practitioners. This book promotes the understanding of analytics methods and techniques. It guides readers through numerous techniques and methods available to analytics practitioners by explaining the strengths and weaknesses of these methods and techniques.
  business problems solved by analytics examples: Hands-On Predictive Analytics with Python Alvaro Fuentes, 2018-12-28 Step-by-step guide to build high performing predictive applications Key FeaturesUse the Python data analytics ecosystem to implement end-to-end predictive analytics projectsExplore advanced predictive modeling algorithms with an emphasis on theory with intuitive explanationsLearn to deploy a predictive model's results as an interactive applicationBook Description Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This book provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. The book's step-by-step approach starts by defining the problem and moves on to identifying relevant data. We will also be performing data preparation, exploring and visualizing relationships, building models, tuning, evaluating, and deploying model. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming. What you will learnGet to grips with the main concepts and principles of predictive analyticsLearn about the stages involved in producing complete predictive analytics solutionsUnderstand how to define a problem, propose a solution, and prepare a datasetUse visualizations to explore relationships and gain insights into the datasetLearn to build regression and classification models using scikit-learnUse Keras to build powerful neural network models that produce accurate predictionsLearn to serve a model's predictions as a web applicationWho this book is for This book is for data analysts, data scientists, data engineers, and Python developers who want to learn about predictive modeling and would like to implement predictive analytics solutions using Python's data stack. People from other backgrounds who would like to enter this exciting field will greatly benefit from reading this book. All you need is to be proficient in Python programming and have a basic understanding of statistics and college-level algebra.
  business problems solved by analytics examples: Decision Support, Analytics, and Business Intelligence, Second Edition Daniel J. Power, 2013-01-11 Competition is becoming more intense and decision makers are encountering increasing complexity, rapid change, and higher levels of risk. In many situations, the solution is more and better computerized decision support, especially analytics and business intelligence. Today managers need to learn about and understand computerized decision support. If a business is to succeed, managers must know much more about information technology solutions. This second edition of a powerful introductory book is targeted at busy managers and MBA students who need to grasp the basics of computerized decision support, including the following: What are analytics? What is a decision support system? How can managers identify opportunities to create innovative computerized support? Inside, the author addresses these questions and some 60 more fundamental questions that are key to understanding the rapidly changing realm of computerized decision support. In a short period of time, you’ll “get up to speed” on decision support, analytics, and business intelligence.
  business problems solved by analytics examples: Pricing Strategy Implementation Andreas Hinterhuber, Stephan M. Liozu, 2019-07-31 Pricing can truly transform organizations. The impact of pricing on organizations is a result of two factors: pricing strategy development and the implementation of these strategies. Implementation is arguably the most difficult part in the pricing strategy process where even seasoned practitioners demand guidance. Pricing strategy development requires creativity, analytical rigor, and an ability to master the internal political competition for scarce resources, but it takes place in a well-defined environment. Fast forward to strategy implementation: competitors that stubbornly fail to behave according to assumptions, new entrants, internal resistance, new opportunities, changing customer preferences, leadership changes, regulatory interventions, or market growth rates that change unexpectedly are some of the intervening variables between the pricing strategy originally developed and the strategy actually implemented. This book provides the theories and best practices that enable the effective implementation of pricing strategies. It offers: a best practice overview on how to convert a pricing strategy into superior results insights from current academic research on driving profits via pricing strategy implementation examples on how to deal with digital transformation in the context of pricing tools and insights into how to overcome internal resistance, align the organization, and forge win-win relationships with customers Taking a new approach, Pricing Strategy Implementation is a critical and practical tool for practicing executives and managers, as well as academics and researchers in pricing, marketing strategy, and strategic management.
  business problems solved by analytics examples: Teaching Data Analytics Susan A Vowels, Katherine Leaming Goldberg, 2019-06-17 The need for analytics skills is a source of the burgeoning growth in the number of analytics and decision science programs in higher education developed to feed the need for capable employees in this area. The very size and continuing growth of this need means that there is still space for new program development. Schools wishing to pursue business analytics programs intentionally assess the maturity level of their programs and take steps to close the gap. Teaching Data Analytics: Pedagogy and Program Design is a reference for faculty and administrators seeking direction about adding or enhancing analytics offerings at their institutions. It provides guidance by examining best practices from the perspectives of faculty and practitioners. By emphasizing the connection of data analytics to organizational success, it reviews the position of analytics and decision science programs in higher education, and to review the critical connection between this area of study and career opportunities. The book features: A variety of perspectives ranging from the scholarly theoretical to the practitioner applied An in-depth look into a wide breadth of skills from closely technology-focused to robustly soft human connection skills Resources for existing faculty to acquire and maintain additional analytics-relevant skills that can enrich their current course offerings. Acknowledging the dichotomy between data analytics and data science, this book emphasizes data analytics rather than data science, although the book does touch upon the data science realm. Starting with industry perspectives, the book covers the applied world of data analytics, covering necessary skills and applications, as well as developing compelling visualizations. It then dives into pedagogical and program design approaches in data analytics education and concludes with ideas for program design tactics. This reference is a launching point for discussions about how to connect industry’s need for skilled data analysts to higher education’s need to design a rigorous curriculum that promotes student critical thinking, communication, and ethical skills. It also provides insight into adding new elements to existing data analytics courses and for taking the next step in adding data analytics offerings, whether it be incorporating additional analytics assignments into existing courses, offering one course designed for undergraduates, or an integrated program designed for graduate students.
  business problems solved by analytics examples: Advanced Problem Solving Using Maple William P Fox, William Bauldry, 2020-11-09 Advanced Problem Solving Using MapleTM: Applied Mathematics, Operations Research, Business Analytics, and Decision Analysis applies the mathematical modeling process by formulating, building, solving, analyzing, and criticizing mathematical models. Scenarios are developed within the scope of the problem-solving process. The text focuses on discrete dynamical systems, optimization techniques, single-variable unconstrained optimization and applied problems, and numerical search methods. Additional coverage includes multivariable unconstrained and constrained techniques. Linear algebra techniques to model and solve problems such as the Leontief model, and advanced regression techniques including nonlinear, logistics, and Poisson are covered. Game theory, the Nash equilibrium, and Nash arbitration are also included. Features: The text’s case studies and student projects involve students with real-world problem solving Focuses on numerical solution techniques in dynamical systems, optimization, and numerical analysis The numerical procedures discussed in the text are algorithmic and iterative Maple is utilized throughout the text as a tool for computation and analysis All algorithms are provided with step-by-step formats About the Authors: William P. Fox is an emeritus professor in the Department of Defense Analysis at the Naval Postgraduate School. Currently, he is an adjunct professor, Department of Mathematics, the College of William and Mary. He received his PhD at Clemson University and has many publications and scholarly activities including twenty books and over one hundred and fifty journal articles. William C. Bauldry, Prof. Emeritus and Adjunct Research Prof. of Mathematics at Appalachian State University, received his PhD in Approximation Theory from Ohio State. He has published many papers on pedagogy and technology, often using Maple, and has been the PI of several NSF-funded projects incorporating technology and modeling into math courses. He currently serves as Associate Director of COMAP’s Math Contest in Modeling (MCM).
  business problems solved by analytics examples: Applied Predictive Analytics Dean Abbott, 2014-03-31 Learn the art and science of predictive analytics — techniques that get results Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. Hands-on examples and case studies are included. The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today This guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions Explains methods, principles, and techniques for conducting predictive analytics projects from start to finish Illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios A companion website provides all the data sets used to generate the examples as well as a free trial version of software Applied Predictive Analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.
  business problems solved by analytics examples: Implementing Analytics Nauman Sheikh, 2013-05-06 Implementing Analytics demystifies the concept, technology and application of analytics and breaks its implementation down to repeatable and manageable steps, making it possible for widespread adoption across all functions of an organization. Implementing Analytics simplifies and helps democratize a very specialized discipline to foster business efficiency and innovation without investing in multi-million dollar technology and manpower. A technology agnostic methodology that breaks down complex tasks like model design and tuning and emphasizes business decisions rather than the technology behind analytics. - Simplifies the understanding of analytics from a technical and functional perspective and shows a wide array of problems that can be tackled using existing technology - Provides a detailed step by step approach to identify opportunities, extract requirements, design variables and build and test models. It further explains the business decision strategies to use analytics models and provides an overview for governance and tuning - Helps formalize analytics projects from staffing, technology and implementation perspectives - Emphasizes machine learning and data mining over statistics and shows how the role of a Data Scientist can be broken down and still deliver the value by building a robust development process
  business problems solved by analytics examples: BUSINESS ANALYSIS PRADEEP HARI PENDSE, 2015-07-01 The second edition of this book is a response to the fact that today BAs are expected to not merely help in gathering requirement for software, but solve real-world business problems, act as design thinkers and innovators, architects, drive process, and business transformation, and become ‘trusted advisors’ to managements—while leveraging their core strength in Information Technology. If the earlier edition was the first book on the subject—this edition takes the subject to the next level by preparing a BA to become a design thinker! An architect/design thinker usually views any problem from multiple perspectives. This edition, has therefore, been structured such that most of the chapters represent a distinct view-point about a problem space, business area or a process. Divided into five sections, the book delves onto three important aspects of Business Analysis—Processes, Information and Systems. The external enterprise context, competitiveness and strategy; internal enterprise context; flow perspective; information perspective; decision/business rules perspective; dynamic perspective; innovation and human perspective and technology perspective are some of the key view-points described in the chapters. Each of these perspectives are covered by way of conceptual framework, real-life illustrations and practical tips for a BA. With the help of a comprehensive cases, this edition guides the BA to synthesize these discrete perspectives, and propose meaningful solutions to the organization. In doing this, the book also explains the core artifacts which a BA produces, viz. Requirements Documents, Estimation and Business Cases. The book is designed for the aspiring Business Analysts and IT Managers/CIOs. Besides, the book will be equally beneficial for the students opting for the courses on MIS, Systems Analysis and Design, MBA, MCA and Business Process Analysis.
  business problems solved by analytics examples: Win with Advanced Business Analytics Jean-Paul Isson, Jesse Harriott, 2012-09-25 Plain English guidance for strategic business analytics and big data implementation In today's challenging economy, business analytics and big data have become more and more ubiquitous. While some businesses don't even know where to start, others are struggling to move from beyond basic reporting. In some instances management and executives do not see the value of analytics or have a clear understanding of business analytics vision mandate and benefits. Win with Advanced Analytics focuses on integrating multiple types of intelligence, such as web analytics, customer feedback, competitive intelligence, customer behavior, and industry intelligence into your business practice. Provides the essential concept and framework to implement business analytics Written clearly for a nontechnical audience Filled with case studies across a variety of industries Uniquely focuses on integrating multiple types of big data intelligence into your business Companies now operate on a global scale and are inundated with a large volume of data from multiple locations and sources: B2B data, B2C data, traffic data, transactional data, third party vendor data, macroeconomic data, etc. Packed with case studies from multiple countries across a variety of industries, Win with Advanced Analytics provides a comprehensive framework and applications of how to leverage business analytics/big data to outpace the competition.
BUSINESS | English meaning - Cambridge Dictionary
BUSINESS definition: 1. the activity of buying and selling goods and services: 2. a particular company that buys and….

VENTURE | English meaning - Cambridge Dictionary
VENTURE definition: 1. a new activity, usually in business, that involves risk or uncertainty: 2. to risk going….

ENTERPRISE | English meaning - Cambridge Dictionary
ENTERPRISE definition: 1. an organization, especially a business, or a difficult and important plan, especially one that….

INCUMBENT | English meaning - Cambridge Dictionary
INCUMBENT definition: 1. officially having the named position: 2. to be necessary for someone: 3. the person who has or….

AD HOC | English meaning - Cambridge Dictionary
AD HOC definition: 1. made or happening only for a particular purpose or need, not planned before it happens: 2. made….

LEVERAGE | English meaning - Cambridge Dictionary
LEVERAGE definition: 1. the action or advantage of using a lever: 2. power to influence people and get the results you….

ENTREPRENEUR | English meaning - Cambridge Dictionary
ENTREPRENEUR definition: 1. someone who starts their own business, especially when this involves seeing a new opportunity….

CULTIVATE | English meaning - Cambridge Dictionary
CULTIVATE definition: 1. to prepare land and grow crops on it, or to grow a particular crop: 2. to try to develop and….

EQUITY | English meaning - Cambridge Dictionary
EQUITY definition: 1. the value of a company, divided into many equal parts owned by the shareholders, or one of the….

LIAISE | English meaning - Cambridge Dictionary
LIAISE definition: 1. to speak to people in other organizations, etc. in order to work with them or exchange….

BUSINESS | English meaning - Cambridge Dictionary
BUSINESS definition: 1. the activity of buying and selling goods and services: 2. a particular company that buys and….

VENTURE | English meaning - Cambridge Dictionary
VENTURE definition: 1. a new activity, usually in business, that involves risk or uncertainty: 2. to risk going….

ENTERPRISE | English meaning - Cambridge Dictionary
ENTERPRISE definition: 1. an organization, especially a business, or a difficult and important plan, especially one that….

INCUMBENT | English meaning - Cambridge Dictionary
INCUMBENT definition: 1. officially having the named position: 2. to be necessary for someone: 3. the person who has or….

AD HOC | English meaning - Cambridge Dictionary
AD HOC definition: 1. made or happening only for a particular purpose or need, not planned before it happens: 2. made….

LEVERAGE | English meaning - Cambridge Dictionary
LEVERAGE definition: 1. the action or advantage of using a lever: 2. power to influence people and get the results you….

ENTREPRENEUR | English meaning - Cambridge Dictionary
ENTREPRENEUR definition: 1. someone who starts their own business, especially when this involves seeing a new opportunity….

CULTIVATE | English meaning - Cambridge Dictionary
CULTIVATE definition: 1. to prepare land and grow crops on it, or to grow a particular crop: 2. to try to develop and….

EQUITY | English meaning - Cambridge Dictionary
EQUITY definition: 1. the value of a company, divided into many equal parts owned by the shareholders, or one of the….

LIAISE | English meaning - Cambridge Dictionary
LIAISE definition: 1. to speak to people in other organizations, etc. in order to work with them or exchange….