Instead of approaching new product development from the inside out, companies have to begin by looking at the process from the outside in, beginning with the customer experience. It's a new way of thinking-and working-that can transform companies struggling to adapt to today's environment into innovative, agile, and commercially successful organizations.Companies must develop a new set of organizational competencies: qualitative customer research to better understand customer behaviors and motivations; an open design process to reframe possibilities and translate new ideas into great customer experiences; and agile technological implementation to quickly prototype ideas, getting them from the whiteboard out into the world where people can respond to them.In Subject to Change: Creating Great Products and Services for an Uncertain World, Adaptive Path, a leading experience strategy and design company, demonstrates how successful businesses can-and should-use customer experiences to inform and shape the product development process, from start to finish.

    H. Evergreen, Effective Data Visualization shows readers how to create Excel charts and graphs that best communicate data findings. This comprehensive how-to guide functions as a set of blueprints--supported by research and the author's extensive experience with clients in industries all over the world--for conveying data in an impactful way. Delivered in Evergreen's humorous and approachable style, the book covers the spectrum of graph types available beyond the default options, how to determine which one most appropriately fits specific data stories, and easy steps for making the chosen graph in Excel.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    Here is a complete library of dimensional modeling techniques-- the most comprehensive collection ever written. Greatly expanded to cover both basic and advanced techniques for optimizing data warehouse design, this second edition to Ralph Kimball's classic guide is more than sixty percent updated.The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including:* Retail sales and e-commerce* Inventory management* Procurement* Order management* Customer relationship management (CRM)* Human resources management* Accounting* Financial services* Telecommunications and utilities* Education* Transportation* Health care and insuranceBy the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts.This book is also available as part of the Kimball's Data Warehouse Toolkit Classics Box Set (ISBN: 9780470479575) with the following 3 books:The Data Warehouse Toolkit, 2nd Edition (9780471200246)The Data Warehouse Lifecycle Toolkit, 2nd Edition (9780470149775)The Data Warehouse ETL Toolkit (9780764567575)

    It's also the evolution of the HCD Toolkit, a fantastic resource that has been purchased or downloaded 141,000 times.

    With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you.Use graphics to describe data with one, two, or dozens of variablesDevelop conceptual models using back-of-the-envelope calculations, as well asscaling and probability argumentsMine data with computationally intensive methods such as simulation and clusteringMake your conclusions understandable through reports, dashboards, and other metrics programsUnderstand financial calculations, including the time-value of moneyUse dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situationsBecome familiar with different open source programming environments for data analysisFinally, a concise reference for understanding how to conquer piles of data.--Austin King, Senior Web Developer, MozillaAn indispensable text for aspiring data scientists.--Michael E. Driscoll, CEO/Founder, Dataspora

    They yearn to come up with a game—changing innovation like Apple's iPod, or create an entirely new category like Facebook. Many make genuine efforts to be innovative—they spend on R&D, bring in creative designers, hire innovation consultants. But they get disappointing results.Why? In The Design of Business, Roger Martin offers a compelling and provocative answer: we rely far too exclusively on analytical thinking, which merely refines current knowledge, producing small improvements to the status quo.To innovate and win, companies need design thinking. This form of thinking is rooted in how knowledge advances from one stage to another—from mystery (something we can't explain) to heuristic (a rule of thumb that guides us toward solution) to algorithm (a predictable formula for producing an answer) to code (when the formula becomes so predictable it can be fully automated). As knowledge advances across the stages, productivity grows and costs drop-creating massive value for companies.Martin shows how leading companies such as Procter & Gamble, Cirque du Soleil, RIM, and others use design thinking to push knowledge through the stages in ways that produce breakthrough innovations and competitive advantage.Filled with deep insights and fresh perspectives, The Design of Business reveals the true foundation of successful, profitable innovation.