The practice of business is changing. More and more companies are amassing larger and larger amounts of data, and storing them in bigger and bigger data bases. Consequently, successful applications of data-driven decision making are plentiful and increasing on a daily basis. This book will motivate the need for data and data-driven solutions, using real data from real business scenarios. It will allow managers to better interact with personnel specializing in analytics by exposing managers and decision makers to the key ideas and concepts of data-driven decision making. Business Analytics for Managers conveys ideas and concepts from both statistics and data mining with the goal of extracting knowledge from real business data and actionable insight for managers. Throughout, emphasis placed on conveying data-driven thinking. While the ideas discussed in this book can be implemented using many different software solutions from many different vendors, it also provides a quick-start to one of the most powerful software solutions available. The main goals of this book are as follows: to excite managers and decision makers about the potential that resides in data and the value that data analytics can add to business processes and provide managers with a basic understanding of the main concepts of data analytics and a common language to convey data-driven decision problems so they can better communicate with personnel specializing in data mining or statistics.
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Key Business Analytics will help managers apply tools to turn data into insights that help them better understand their customers, optimize their internal processes and identify cost savings and growth opportunities. It includes analysis techniques within the following categories: Financial analytics – cashflow, profitability, sales forecasts Market analytics – market size, market trends, marketing channels Customer analytics – customer lifetime values, social media, customer needs Employee analytics – capacity, performance, leadership Operational analytics – supply chains, competencies, environmental impact Bare business analytics – sentiments, text, correlations Each tool will follow the bestselling Key format of being 5-6 pages long, broken into short sharp advice on the essentials: What is it? When should I use it? How do I use it? Tips and pitfalls Further reading This essential toolkit also provides an invaluable section on how to gather original data yourself through surveys, interviews, focus groups, etc.
The intensified used of data based on analytical models to control digitalized operational business processes in an intelligent way is a game changer that continuously disrupts more and more markets. This book exemplifies this development and shows the latest tools and advances in this field Business Analytics for Managers offers real-world guidance for organizations looking to leverage their data into a competitive advantage. This new second edition covers the advances that have revolutionized the field since the first edition's release; big data and real-time digitalized decision making have become major components of any analytics strategy, and new technologies are allowing businesses to gain even more insight from the ever-increasing influx of data. New terms, theories, and technologies are explained and discussed in terms of practical benefit, and the emphasis on forward thinking over historical data describes how analytics can drive better business planning. Coverage includes data warehousing, big data, social media, security, cloud technologies, and future trends, with expert insight on the practical aspects of the current state of the field. Analytics helps businesses move forward. Extensive use of statistical and quantitative analysis alongside explanatory and predictive modeling facilitates fact-based decision making, and evolving technologies continue to streamline every step of the process. This book provides an essential update, and describes how today's tools make business analytics more valuable than ever. Learn how Hadoop can upgrade your data processing and storage Discover the many uses for social media data in analysis and communication Get up to speed on the latest in cloud technologies, data security, and more Prepare for emerging technologies and the future of business analytics Most businesses are caught in a massive, non-stop stream of data. It can become one of your most valuable assets, or a never-ending flood of missed opportunity. Technol
- Author : Jonathan P. Pinder
- Publisher : Academic Press
- Release Date : 2016-09-03
- Genre : Business & Economics
- Pages : 448
- ISBN : 9780128104866
Introduction to Business Analytics Using Simulation employs an innovative strategy to teach business analytics. It 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 the uncertainty and variability of business, this comprehensive book provides a better foundation for business analytics than standard introductory business analytics books. Students will gain a better understanding of fundamental statistical concepts that are essential to marketing research, Six-Sigma, financial analysis, and business analytics. Winner of the 2017 Textbook and Academic Authors Association (TAA) Most Promising New Textbook Award 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
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.
A PRACTITIONER S GUIDE TO BUSINESS ANALYTICS Using Data Analysis Tools to Improve Your Organization s Decision Making and Strategy
- Author : Randy Bartlett
- Publisher : McGraw-Hill Education
- Release Date : 2013-02-05
- Genre : Business & Economics
- Pages : 256
- ISBN : 0071807594
The Definitive Guide to Using Analytics for Better Business Decisions "A must-read for anyone who is directly or indirectly leading or managing an analytics function--and anyone who wants to make better decisions based on analytics, not just intuition or an 'overemphasis on industry knowledge, which crowds out good analytics.'" -- Charlotte E. Sibley, President, Sibley Associates, a bioPharma consulting company "Over the long term, those who show the greatest imagination, grow the right skills, build the deepest organizations, and follow rigorous statistical practice will reap the greatest rewards from their analytics efforts. A Practitioner's Guide to Business Analytics lights the way." -- Thomas C. Redman, PhD, the Data Doc, Navesink Consulting Group "Executives beware. This is not your typical management book. This book contains real information from analytical professionals who are outside the executive bubble. . . . Hold on to your seat and be prepared to change the way you think about leaders, leadership qualities, and leadership skills needed for future success in the changing business landscape." -- Thomas J. Scott, Director/Advisor, Marketing Sciences Solutions, TGaS Advisors "Randy Bartlett has written an important and useful book, filling at least some of the large void between books that exhort managers to think more analytically without explaining how, and overly technical books that only quantitative analysts would appreciate. Particular strengths are the recommendations about how to organize to integrate analytical expertise into decision-making and the guidance about how managers can assess whether they are getting good analytical advice." -- Douglas A. Samuelson, D.Sc., President and Chief Scientist, InfoLogix, Inc., Annandale, VA; quantitative analyst, inventor, entrepreneur and executive About the Book: The real tragedy of a company failing while using analytics is the fact that its leaders will have the data to explain the failure, but they won't
Technological advances in the last five years have allowed organizations to use Business Analytics to provide insights, increase understanding and it is hoped, gain the elusive 'competitive edge'. The rapid development of Business Analytics is impacting all enterprise competences profoundly and classical business professions are being redefined by a much deeper interplay between business and information systems. As computing capabilities for analysis has moved outside the IT glass-house and into the sphere of individual workers, they are no longer the exclusive domain of IT professionals but rather accessible to all employees. Complex open-source data analytics packages and client-level visualization tools deployed in desktops and laptops equip virtually any end-user with the instruments to carry out significant analytical tasks. All the while, the drive to improve 'customer experience' has heightened the demand for data involving customers, providers and entire ecosystems. In response to the proliferation of Business Analytics, a new Center and Masters of Science Program was introduced at the National University of Singapore (NUS). The Center collaborates with over 40 different external partner organizations in Asia-Pacific with which all MSBA students undertake individual projects. Business Analytics: Progress on Applications in Asia Pacific provides a useful picture of the maturity of the Business Analytics domain in Asia Pacific.
Collecting, analyzing, and extracting valuable information froma large amount of data requires easily accessible, robust,computational and analytical tools. Data Mining and BusinessAnalytics with R utilizes the open source software R for theanalysis, exploration, and simplification of large high-dimensionaldata sets. As a result, readers are provided with the neededguidance to model and interpret complicated data and become adeptat building powerful models for prediction and classification. Highlighting both underlying concepts and practicalcomputational skills, Data Mining and Business Analytics withR begins with coverage of standard linear regression and theimportance of parsimony in statistical modeling. The book includesimportant topics such as penalty-based variable selection (LASSO);logistic regression; regression and classification trees;clustering; principal components and partial least squares; and theanalysis of text and network data. In addition, the bookpresents: • A thorough discussion and extensive demonstration of thetheory behind the most useful data mining tools • Illustrations of how to use the outlined concepts inreal-world situations • Readily available additional data sets and related Rcode allowing readers to apply their own analyses to the discussedmaterials • Numerous exercises to help readers with computing skillsand deepen their understanding of the material Data Mining and Business Analytics with R is an excellentgraduate-level textbook for courses on data mining and businessanalytics. The book is also a valuable reference for practitionerswho collect and analyze data in the fields of finance, operationsmanagement, marketing, and the information sciences.
Maximize profit and optimize decisions with advanced business analytics Profit-Driven Business Analytics provides actionable guidance on optimizing the use of data to add value and drive better business. Combining theoretical and technical insights into daily operations and long-term strategy, this book acts as a development manual for practitioners seeking to conceive, develop, and manage advanced analytical models. Detailed discussion delves into the wide range of analytical approaches and modeling techniques that can help maximize business payoff, and the author team draws upon their recent research to share deep insight about optimal strategy. Real-life case studies and examples illustrate these techniques at work, and provide clear guidance for implementation in your own organization. From step-by-step instruction on data handling, to analytical fine-tuning, to evaluating results, this guide provides invaluable guidance for practitioners seeking to reap the advantages of true business analytics. Despite widespread discussion surrounding the value of data in decision making, few businesses have adopted advanced analytic techniques in any meaningful way. This book shows you how to delve deeper into the data and discover what it can do for your business. Reinforce basic analytics to maximize profits Adopt the tools and techniques of successful integration Implement more advanced analytics with a value-centric approach Fine-tune analytical information to optimize business decisions Both data stored and streamed has been increasing at an exponential rate, and failing to use it to the fullest advantage equates to leaving money on the table. From bolstering current efforts to implementing a full-scale analytics initiative, the vast majority of businesses will see greater profit by applying advanced methods. Profit-Driven Business Analytics provides a practical guidebook and reference for adopting real business analytics techniques.
Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics. This book will discuss and explore the following through examples and case studies: An introduction to R: data management and R functions The architecture, framework, and life cycle of a business analytics project Descriptive analytics using R: descriptive statistics and data cleaning Data mining: classification, association rules, and clustering Predictive analytics: simple regression, multiple regression, and logistic regression This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book. What You Will Learn • Write R programs to handle data • Build analytical models and draw useful inferences from them • Discover the basic concepts of data mining and machine learning • Carry out predictive modeling • Define a business issue as an analytical problem Who This Book Is For Beginners who want to understand and learn the fundamentals of analytics using R. Students, managers, executives, strategy and planning professionals, software professionals, and BI/DW professionals.
This exciting new textbook offers an accessible, business-focused overview of the key theoretical concepts underpinning modern data analytics. It provides engaging and practical advice on using the key software tools, including SAS Visual Analytics, R and DataRobot, that are used in organisations to help make effective data-driven decisions. Combining theory with hands-on practical examples, this essential text includes cutting edge coverage of new areas of interest including social media analytics, design thinking and the ethical implications of using big data. A wealth of learning features including exercises, cases, online resources and data sets help students to develop analytic problem-solving skills. With its management perspective on analytics and its coverage of a range of popular software tools, this is an ideal essential text for upper-level undergraduate, postgraduate and MBA students. It is also ideal for practitioners wanting to understand the broader organisational context of big data analysis and to engage critically with the tools and techniques of business analytics.
Assuming no prior knowledge or technical skills, Getting Started with Business Analytics: Insightful Decision-Making explores the contents, capabilities, and applications of business analytics. It bridges the worlds of business and statistics and describes business analytics from a non-commercial standpoint. The authors demystify the main concepts and terminologies and give many examples of real-world applications. The first part of the book introduces business data and recent technologies that have promoted fact-based decision-making. The authors look at how business intelligence differs from business analytics. They also discuss the main components of a business analytics application and the various requirements for integrating business with analytics. The second part presents the technologies underlying business analytics: data mining and data analytics. The book helps you understand the key concepts and ideas behind data mining and shows how data mining has expanded into data analytics when considering new types of data such as network and text data. The third part explores business analytics in depth, covering customer, social, and operational analytics. Each chapter in this part incorporates hands-on projects based on publicly available data. Helping you make sound decisions based on hard data, this self-contained guide provides an integrated framework for data mining in business analytics. It takes you on a journey through this data-rich world, showing you how to deploy business analytics solutions in your organization.
The present book provides an enterprise-wide guide for anyone interested in pursuing analytic methods in order to compete effectively. It supplements more general texts on statistics and data mining by providing an introduction from leading practitioners in business analytics and real case studies of firms using advanced analytics to gain a competitive advantage in the marketplace. In the era of “big data” and competing analytics, this book provides practitioners applying business analytics with an overview of the quantitative strategies and techniques used to embed analysis results and advanced algorithms into business processes and create automated insight-driven decisions within the firm. Numerous studies have shown that firms that invest in analytics are more likely to win in the marketplace. Moreover, the Internet of Everything (IoT) for manufacturing and social-local-mobile (SOLOMO) for services have made the use of advanced business analytics even more important for firms. These case studies were all developed by real business analysts, who were assigned the task of solving a business problem using advanced analytics in a way that competitors were not. Readers learn how to develop business algorithms on a practical level, how to embed these within the company and how to take these all the way to implementation and validation.
- Author : Thomas H. Davenport
- Publisher : FT Press
- Release Date : 2012-10-14
- Genre : Business & Economics
- Pages : 1105
- ISBN : 9780133091250
A brand new collection of business analytics insights and actionable techniques… 3 authoritative books, now in a convenient e-format, at a great price! 3 authoritative eBooks deliver comprehensive analytics knowledge and tools for optimizing every critical business decision! Use business analytics to drive maximum value from all your business data! This unique 3 eBook package will help you harness your information, discover hidden patterns, and successfully act on what you learn. In Enterprise Analytics, analytics pioneer Tom Davenport and the world-renowned experts at the International Institute for Analytics (IIA) bring together the latest techniques, best practices, and research on large-scale analytics strategy, technology, implementation, and management. Using real-world examples, they cover everything from building better analytics organizations to gathering data; implementing predictive analytics to linking analysis with organizational performance. You'll find specific insights for optimizing supply chains, online services, marketing, fraud detection, and many other business functions; plus chapter-length case studies from healthcare, retail, and financial services. Next, in the up-to-the-minute Analysis Without Paralysis, Second Edition, Babette E. Bensoussan and Craig S. Fleisher help you succeed with analysis without getting mired in advanced math or arcane theory. They walk you through the entire business analysis process, and guide you through using 12 core tools for making better decisions about strategy and operations -- including three powerful tools covered for the first time in this new Second Edition. Then, in Business and Competitive Analysis, Fleisher and Bensoussan help you apply 24 leading business analysis models to gain deep clarity about your business environment, answer tough questions, and make tough choices. They first walk you through defining problems, avoiding pitfalls, choosing tools, and communicating results. Next, they systematic
AVOID THE MISTAKES THAT OTHERS MAKE – LEARN WHAT LEADS TO BEST PRACTICE AND KICKSTART SUCCESS This groundbreaking resource provides comprehensive coverage across all aspects of business analytics, presenting proven management guidelines to drive sustainable differentiation. Through a rich set of case studies, author Evan Stubbs reviews solutions and examples to over twenty common problems spanning managing analytics assets and information, leveraging technology, nurturing skills, and defining processes. Delivering Business Analytics also outlines the Data Scientist’s Code, fifteen principles that when followed ensure constant movement towards effective practice. Practical advice is offered for addressing various analytics issues; the advantages and disadvantages of each issue’s solution; and how these solutions can optimally create organizational value. With an emphasis on real-world examples and pragmatic advice throughout, Delivering Business Analytics provides a reference guide on: The economic principles behind how business analytics leads to competitive differentiation The elements which define best practice The Data Scientist’s Code, fifteen management principles that when followed help teams move towards best practice Practical solutions and frequent missteps to twenty-four common problems across people and process, systems and assets, and data and decision-making Drawing on the successes and failures of countless organizations, author Evan Stubbs provides a densely packed practical reference on how to increase the odds of success in designing business analytics systems and managing teams of data scientists. Uncover what constitutes best practice in business analytics and start achieving it with Delivering Business Analytics.
Master data analysis, modeling, and spreadsheet use with BUSINESS ANALYTICS: DATA ANALYSIS AND DECISION MAKING, 6E! Popular with students, instructors, and practitioners, this quantitative methods text delivers the tools to succeed with its proven teach-by-example approach, user-friendly writing style, and complete Excel 2016 integration. It is also compatible with Excel 2013, 2010, and 2007. Completely rewritten, Chapter 17, Data Mining, and Chapter 18, Importing Data into Excel, include increased emphasis on the tools commonly included under the Business Analytics umbrella -- including Microsoft Excel’s “Power BI” suite. In addition, up-to-date problem sets and cases provide realistic examples to show the relevance of the material. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
R for Business Analytics looks at some of the most common tasks performed by business analysts and helps the user navigate the wealth of information in R and its 4000 packages. With this information the reader can select the packages that can help process the analytical tasks with minimum effort and maximum usefulness. The use of Graphical User Interfaces (GUI) is emphasized in this book to further cut down and bend the famous learning curve in learning R. This book is aimed to help you kick-start with analytics including chapters on data visualization, code examples on web analytics and social media analytics, clustering, regression models, text mining, data mining models and forecasting. The book tries to expose the reader to a breadth of business analytics topics without burying the user in needless depth. The included references and links allow the reader to pursue business analytics topics. This book is aimed at business analysts with basic programming skills for using R for Business Analytics. Note the scope of the book is neither statistical theory nor graduate level research for statistics, but rather it is for business analytics practitioners. Business analytics (BA) refers to the field of exploration and investigation of data generated by businesses. Business Intelligence (BI) is the seamless dissemination of information through the organization, which primarily involves business metrics both past and current for the use of decision support in businesses. Data Mining (DM) is the process of discovering new patterns from large data using algorithms and statistical methods. To differentiate between the three, BI is mostly current reports, BA is models to predict and strategize and DM matches patterns in big data. The R statistical software is the fastest growing analytics platform in the world, and is established in both academia and corporations for robustness, reliability and accuracy. The book utilizes Albert Einstein’s famous remarks on making things as
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.
Focus on SAP business analytics business gains, key features, and implementation. The book includes example implementations of SAP business analytics, the challenges faced, and the solutions implemented. SAP Business Analytics explains both the strategy and technical implementation for gathering and analyzing all the information pertaining to an organization. Key features of the book are: A 360-degree view of an organization’s data and the methods to gather and analyze that data The strategies that need to be in place to gather relevant data from disparate systems Details about the SAP business analytics suite of products The technical implementations used to gather data from disparate systems such as ERP and CRM Real business cases as examples Analytics is the driving force in today’s business, be it healthcare, marketing, telecommunications, or retail and hence the most vital part of any organization’s strategy. What You'll Learn Gain an understanding of business analytics in general Absorb the technical details of the SAP business analytics suite of products Discover the challenges faced during an enterprise-level analytics project implementation Learn the key points to be kept in mind during the technical implementation of an SAP business analytics project Who This Book Is For Analytics strategists, BI managers, BI architects, business analysts, and BI developers.
Written with the aim of becoming the primary resource for students of business analytics, this book provides a holistic perspective of analytics with theoretical foundations and applications of the theory using examples across several industries.