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.

Gain the competitive edge with the smart use of business analytics In today’s volatile business environment, the strategic use of business analytics is more important than ever. A Practitioners Guide to Business Analytics helps you get the organizational commitment you need to get business analytics up and running in your company. It provides solutions for meeting the strategic challenges of applying analytics, such as: Integrating analytics into decision making, corporate culture, and business strategy Leading and organizing analytics within the corporation Applying statistical qualifications, statistical diagnostics, and statistical review Providing effective building blocks to support analytics—statistical software, data collection, and data management Randy Bartlett, Ph.D., is Chief Statistical Officer of the consulting company Blue Sigma Analytics. He currently works with Infosys, where he has helped build their new Business Analytics practice.

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 have the capabilities in place to filter the data and convert it into actionable business insights. One implication of Big Data is that we need to adapt . . . quickly. A Practitioner''s Guide to Business Analytics integrates powerful strategies for leveraging analytics inside a business with a how-to playbook of tactics to make it happen. The case for competing based on analytics is clear, but until now, there hasn''t been authoritative guidance for inciting a corporate community to evolve into a thriving, analytics-driven environment. This hands-on book gives you the tools, knowledge, and strategies to capture the level of organizational commitment you need to get business analytics up and running in your company. It helps you define what business analytics is, quantify the exponential value it brings to an organization, and show others how to harness its power to gain advantage over competitors. Accomplished business information professional Randy Bartlett brings his comprehensive coverage to life with firsthand accounts of using business analytics at brand-name global companies. Through in-depth examinations of success stories and failures in analytics-based decision making and data analyses, he fully prepares you to: Assess your company''s analytics needs and capabilities, and develop a strategic analytics plan Steward the three pillars of Best Statistical Practice and accurately measure the quality of analytics-based decisions and data analyses Build and organize a specialized Business Analytics Team to lead infrastructural changes Upgrade the foundation that supports business analytics--data collection, data software, and data management Create the essential synergy for success between the Business Analytics Team and IT Effectively integrating analytics into everyday decision making, corporate

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.

The Model Rules of Professional Conduct provides an up-to-date resource for information on legal ethics. Federal, state and local courts in all jurisdictions look to the Rules for guidance in solving lawyer malpractice cases, disciplinary actions, disqualification issues, sanctions questions and much more. In this volume, black-letter Rules of Professional Conduct are followed by numbered Comments that explain each Rule's purpose and provide suggestions for its practical application. The Rules will help you identify proper conduct in a variety of given situations, review those instances where discretionary action is possible, and define the nature of the relationship between you and your clients, colleagues and the courts.

The guide to targeting and leveraging business opportunities using big data & analytics By leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. Analytics in a Big Data World reveals how to tap into the powerful tool of data analytics to create a strategic advantage and identify new business opportunities. Designed to be an accessible resource, this essential book does not include exhaustive coverage of all analytical techniques, instead focusing on analytics techniques that really provide added value in business environments. The book draws on author Bart Baesens' expertise on the topics of big data, analytics and its applications in e.g. credit risk, marketing, and fraud to provide a clear roadmap for organizations that want to use data analytics to their advantage, but need a good starting point. Baesens has conducted extensive research on big data, analytics, customer relationship management, web analytics, fraud detection, and credit risk management, and uses this experience to bring clarity to a complex topic. Includes numerous case studies on risk management, fraud detection, customer relationship management, and web analytics Offers the results of research and the author's personal experience in banking, retail, and government Contains an overview of the visionary ideas and current developments on the strategic use of analytics for business Covers the topic of data analytics in easy-to-understand terms without an undo emphasis on mathematics and the minutiae of statistical analysis For organizations looking to enhance their capabilities via data analytics, this resource is the go-to reference for leveraging data to enhance business capabilities.

Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. This second edition has gone through a major revamp and introduces several significant changes and new topics based on the recent trends in NLP. You’ll see how to use the latest state-of-the-art frameworks in NLP, coupled with machine learning and deep learning models for supervised sentiment analysis powered by Python to solve actual case studies. Start by reviewing Python for NLP fundamentals on strings and text data and move on to engineering representation methods for text data, including both traditional statistical models and newer deep learning-based embedding models. Improved techniques and new methods around parsing and processing text are discussed as well. Text summarization and topic models have been overhauled so the book showcases how to build, tune, and interpret topic models in the context of an interest dataset on NIPS conference papers. Additionally, the book covers text similarity techniques with a real-world example of movie recommenders, along with sentiment analysis using supervised and unsupervised techniques. There is also a chapter dedicated to semantic analysis where you’ll see how to build your own named entity recognition (NER) system from scratch. While the overall structure of the book remains the same, the entire code base, modules, and chapters has been updated to the latest Python 3.x release. What You'll Learn • Understand NLP and text syntax, semantics and structure• Discover text cleaning and feature engineering• Review text classification and text clustering • Assess text summarization and topic models• Study deep learning for NLP Who This Book Is For IT professionals, data analysts, developers, linguistic experts, data scientists and engineers and basically anyone with a keen interest in linguistics, analytics and generating insights from textual data.