The New Edition of a Business Classic This landmark work, the first to introduce business leaders to analytics, reveals how analytics are rewriting the rules of competition. Updated with fresh content, Competing on Analytics provides the road map for becoming an analytical competitor, showing readers how to create new strategies for their organizations based on sophisticated analytics. Introducing a five-stage model of analytical competition, Davenport and Harris describe the typical behaviors, capabilities, and challenges of each stage. They explain how to assess your company’s capabilities and guide it toward the highest level of competition. With equal emphasis on two key resources, human and technological, this book reveals how even the most highly analytical companies can up their game. With an emphasis on predictive, prescriptive, and autonomous analytics for marketing, supply chain, finance, M&A, operations, R&D, and HR, the book contains numerous new examples from different industries and business functions, such as Disney’s vacation experience, Google’s HR, UPS’s logistics, the Chicago Cubs’ training methods, and Firewire Surfboards’ customization. Additional new topics and research include: Data scientists and what they do Big data and the changes it has wrought Hadoop and other open-source software for managing and analyzing data Data products—new products and services based on data and analytics Machine learning and other AI technologies The Internet of Things and its implications New computing architectures, including cloud computing Embedding analytics within operational systems Visual analytics The business classic that turned a generation of leaders into analytical competitors, Competing on Analytics is the definitive guide for transforming your company’s fortunes in the age of analytics and big data.

Dive into the world of Informatica PowerCenter 10.x, where enterprise data warehousing meets cutting-edge data management solutions. This comprehensive guide walks you through mastering ETL processes and optimizing performance, helping you become proficient in this powerful data integration tool. With step-by-step instructions, you'll build your knowledge from installation to advanced techniques. What this Book will help me do Understand how to install and configure Informatica PowerCenter 10.x for enterprise-level data integration projects, ensuring readiness to start transforming data effectively. Gain hands-on experience with PowerCenter's various developer tools, including Workflow Manager, Workflow Monitor, Designer, and Repository Manager, mastering their practical utilities. Learn and apply essential data warehousing concepts, such as Slowly Changing Dimensions (SCDs) and Incremental Aggregations, to create robust data-handling workflows. Leverage advanced PowerCenter features like pushdown optimization and partitioning to optimize performance for large-scale data processing jobs. Become proficient in migrating sources, targets, and workflows between environments, enabling seamless integration of data management solutions across enterprise systems. Author(s) Rahul Malewar, a seasoned expert in ETL and data integration, brings his extensive experience with Informatica PowerCenter to the table. With years spent working alongside global enterprises to streamline their data operations, Rahul's insights transfer into a hands-on teaching style that simplifies even the most advanced concepts. Apt at bridging technical depth with accessible explanations, he has dedicated his career to empowering learners to unlock the full potential of their data warehousing tools. Who is it for? Perfect for developers and data professionals aiming to elevate their enterprise data management skills, this book is ideally suited for those new to or experienced with Informatica. Whether you're striving to become proficient in PowerCenter or seeking to implement advanced ETL concepts in your projects, this guide will equip you with the expertise to succeed. A foundational understanding of programming and data warehousing concepts is recommended for best results.

Solve business problems involving time-to-event and resulting probabilities by following the modeling tutorials in Business Survival Analysis Using SAS®: An Introduction to Lifetime Probabilities, the first book to be published in the field of business survival analysis! Survival analysis is a challenge. Books applying to health sciences exist, but nothing about survival applications for business has been available until now. Written for analysts, forecasters, econometricians, and modelers who work in marketing or credit risk and have little SAS modeling experience, Business Survival Analysis Using SAS® builds on a foundation of SAS code that works in any survival model and features numerous annotated graphs, coefficients, and statistics linked to real business situations and data sets. This guide also helps recent graduates who know the statistics but do not necessarily know how to apply them get up and running in their jobs. By example, it teaches the techniques while avoiding advanced theoretical underpinnings so that busy professionals can rapidly deliver a survival model to meet common business needs.

From first principles, this book teaches survival analysis by highlighting its relevance to business cases. A pragmatic introduction to survival analysis models, it leads you through business examples that contextualize and motivate the statistical methods and SAS coding. Specifically, it illustrates how to build a time-to-next-purchase survival model in SAS® Enterprise Miner, and it relates each step to the underlying statistics and to Base SAS® and SAS/STAT® software. Following the many examples—from data preparation to validation to scoring new customers—you will learn to develop and apply survival analysis techniques to scenarios faced by companies in the financial services, insurance, telecommunication, and marketing industries, including the following scenarios:

Time-to-next-purchase for marketing

Employer turnover for human resources

Small business portfolio macroeconometric stress tests for banks

International Financial Reporting Standard (IFRS 9) lifetime probability of default for banks and building societies

"Churn," or attrition, models for the telecommunications and insurance industries

Oracle has evolved from a simple relational database into one of the most complex e-commerce platforms ever devised. This book presents a compendium of articles from Oracle Internals, Auerbach Publications' newsletter for Oracle database administrators and other Oracle professionals.

A step-by-step guide to predictive modeling!

Kattamuri Sarma's Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Third Edition, will show you how to develop and test predictive models quickly using SAS Enterprise Miner. Using realistic data, the book explains complex methods in a simple and practical way to readers from different backgrounds and industries. Incorporating the latest version of Enterprise Miner, this third edition also expands the section on time series.

Written for business analysts, data scientists, statisticians, students, predictive modelers, and data miners, this comprehensive text provides examples that will strengthen your understanding of the essential concepts and methods of predictive modeling. Topics covered include logistic regression, regression, decision trees, neural networks, variable clustering, observation clustering, data imputation, binning, data exploration, variable selection, variable transformation, and much more, including analysis of textual data.

Develop predictive models quickly, learn how to test numerous models and compare the results, gain an in-depth understanding of predictive models and multivariate methods, and discover how to do in-depth analysis. Do it all with Predictive Modeling with SAS Enterprise Miner!

Analysis of Clinical Trials Using SAS®: A Practical Guide, Second Edition bridges the gap between modern statistical methodology and real-world clinical trial applications. Tutorial material and step-by-step instructions illustrated with examples from actual trials serve to define relevant statistical approaches, describe their clinical trial applications, and implement the approaches rapidly and efficiently using the power of SAS. Topics reflect the International Conference on Harmonization (ICH) guidelines for the pharmaceutical industry and address important statistical problems encountered in clinical trials. Commonly used methods are covered, including dose-escalation and dose-finding methods that are applied in Phase I and Phase II clinical trials, as well as important trial designs and analysis strategies that are employed in Phase II and Phase III clinical trials, such as multiplicity adjustment, data monitoring, and methods for handling incomplete data. This book also features recommendations from clinical trial experts and a discussion of relevant regulatory guidelines.

This new edition includes more examples and case studies, new approaches for addressing statistical problems, and the following new technological updates:

SAS procedures used in group sequential trials (PROC SEQDESIGN and PROC SEQTEST)

SAS procedures used in repeated measures analysis (PROC GLIMMIX and PROC GEE)

macros for implementing a broad range of randomization-based methods in clinical trials, performing complex multiplicity adjustments, and investigating the design and analysis of early phase trials (Phase I dose-escalation trials and Phase II dose-finding trials)

Clinical statisticians, research scientists, and graduate students in biostatistics will greatly benefit from the decades of clinical research experience and the ready-to-use SAS macros compiled in this book.

"QlikView for Developers" is a comprehensive guide to mastering QlikView, a powerful business intelligence tool. This book takes you on a journey from understanding the basics to building scalable and maintainable QlikView applications. Designed to provide practical methods, real-world scenarios, and valuable tips, it is ideal for anyone wanting to learn and effectively use QlikView for BI solutions. What this Book will help me do Understand the key features and architecture of QlikView and what has changed in QlikView 12. Learn to transform, model, and organize data in QlikView to effectively support business processes. Master best practices for creating interactive dashboards using charts, tables, and visualization objects. Discover techniques to optimize data architecture for scalable deployments and ensure data consistency. Implement advanced scripting and calculation methods, such as Set Analysis, to handle complex analytical requirements. Author(s) Miguel Angel Garcia and Barry Harmsen bring years of professional expertise in business intelligence and QlikView application development. Both authors have contributed significantly to the BI community and have extensive experience teaching and consulting on QlikView solutions. Their goal with this book is to provide a resource that is both informative and practical for QlikView developers. Who is it for? This book is intended for developers and analysts looking to harness the capabilities of QlikView for business intelligence purposes. It is suitable for beginners with minimal experience in QlikView, as well as for experienced practitioners wanting to deepen their knowledge and skills. The book provides a balanced approach that caters to various skill levels, ensuring accessible and actionable content for all readers.

Organizations are rapidly consuming more data than ever before, and to drive their competitive advantage, they’re demanding interactive visualizations and interactive analyses of that data be embedded in their applications and business processes. This will enable them to make faster and more effective decisions based on data, not guesses. This practical book examines the considerations that software developers, product managers, and vendors need to take into account when making visualization and analytics a seamlessly integrated part of the applications they deliver, as well as the impact of migrating their applications to modern data platforms. Authors Federico Castanedo (Vodafone Group) and Andy Oram (O’Reilly Media) explore the basic requirements for embedding domain expertise with fast, powerful, and interactive visual analytics that will delight and inform customers more than spreadsheets and custom-generated charts. Particular focus is placed on the characteristics of effective visual analytics for big and fast data. Learn the impact of trends driving embedded analytics Review examples of big data applications and their analytics requirements in retail, direct service, cybersecurity, the Internet of Things, and logistics Explore requirements for embedding visual analytics in modern data environments, including collection, storage, retrieval, data models, speed, microservices, parallelism, and interactivity Take a deep dive into the characteristics of effective visual analytics and criteria for evaluating modern embedded analytics tools Use a self-assessment rating chart to determine the value of your organization’s BI in the modern data setting

Access and clean up data easily using JMP®! Data acquisition and preparation commonly consume approximately 75% of the effort and time of total data analysis. JMP provides many visual, intuitive, and even innovative data-preparation capabilities that enable you to make the most of your organization's data. Preparing Data for Analysis with JMP® is organized within a framework of statistical investigations and model-building and illustrates the new data-handling features in JMP, such as the Query Builder. Useful to students and programmers with little or no JMP experience, or those looking to learn the new data-management features and techniques, it uses a practical approach to getting started with plenty of examples. Using step-by-step demonstrations and screenshots, this book walks you through the most commonly used data-management techniques that also include lots of tips on how to avoid common problems. With this book, you will learn how to: Manage database operations using the JMP Query Builder Get data into JMP from other formats, such as Excel, csv, SAS, HTML, JSON, and the web Identify and avoid problems with the help of JMP’s visual and automated data-exploration tools Consolidate data from multiple sources with Query Builder for tables Deal with common issues and repairs that include the following tasks: reshaping tables (stack/unstack) managing missing data with techniques such as imputation and Principal Components Analysis cleaning and correcting dirty data computing new variables transforming variables for modelling reconciling time and date Subset and filter your data Save data tables for exchange with other platforms

When it comes to business intelligence and analytical capabilities, SAS Visual Analytics is the premier solution for data discovery, visualization, and reporting. An Introduction to SAS Visual Analytics will show you how to make sense of your complex data with the goal of leading you to smarter, data-driven decisions without having to write a single line of code – unless you want to! You will be able to use SAS Visual Analytics to access, prepare, and present your data to anyone anywhere in the world. SAS Visual Analytics automatically highlights key relationships, outliers, clusters, trends and more. These abilities will guide you to critical insights that inspire action from your data. With this book, you will become proficient using SAS Visual Analytics to present data and results in customizable, robust visualizations, as well as guided analyses through auto-charting. With interactive dashboards, charts, and reports, you will create visualizations which convey clear and actionable insights for any size and type of data. This book largely focuses on the version of SAS Visual Analytics on SAS 9.4, although it is available on both 9.4 and SAS Viya platforms. Each version is considered the latest release, with subsequent releases planned to continue on each platform; hence, the Viya version works similarly to the 9.4 version and will look familiar. This book covers new features of each and important differences between the two. With this book, you will learn how to: Build your first report using the SAS Visual Analytics Designer Prepare a dashboard and determine the best layout Effectively use geo-spatial objects to add location analytics to reports Understand and use the elements of data visualizations Prepare and load your data with the SAS Visual Analytics Data Builder Analyze data with a variety of options, including forecasting, word clouds, heat maps, correlation matrix, and more Understand administration activities to keep SAS Visual Analytics humming along Optimize your environment for considerations such as scalability, availability, and efficiency between components of your SAS software deployment and data providers

JMP 13 Consumer Research focuses on analyses that help users observe and predict subject's behavior, particularly those in the market research field. The Uplift platform predicts consumer behavior based on shifts in marketing efforts. Learn how to tabulate and summarize categorical responses with the Categorical platform. Factor Analysis rotates principal components to help identify which directions have the most variation among the variables. The book also covers Item Analysis, a method for identifying latent traits that might affect an individual's choices. And read about the Choice platform, which market researchers use to estimate probability in consumer spending.

The JMP 13 Design of Experiments Guide covers classic DOE designs (for example, full factorial, response surface, and mixture designs). Read about more flexible custom designs, which you generate to fit your particular experimental situation. And discover JMP’s definitive screening designs, an efficient way to identify important factor interactions using fewer runs than required by traditional designs. The book also provides guidance on determining an appropriate sample size for your study.

JMP 13 Fitting Linear Models focuses on the Fit Model platform and many of its personalities. Linear and logistic regression, analysis of variance and covariance, and stepwise procedures are covered. Also included are multivariate analysis of variance, mixed models, generalized models, and models based on penalized regression techniques.

JMP 13 Multivariate Methods describes techniques for analyzing several variables simultaneously. The book covers descriptive measures, such as correlations. It also describes methods that give insight into the structure of the multivariate data, such as clustering, latent class analysis, principal components, discriminant analysis, and partial least squares.

JMP 13 Predictive and Specialized Modeling provides details about modeling techniques such as partitioning, neural networks, nonlinear regression, and time series analysis. Topics include the Gaussian platform, which is useful in analyzing computer simulation experiments. The book also covers the Response Screening platform, which is useful in testing the effect of a predictor when you have many responses.

JMP 13 Profilers covers the family of interactive profiling tools, which enable you to view cross-sections of any response surface. The book also includes details about plotting points and surfaces in a three-dimensional graph.

This book provides hands-on tutorials with just the right amount of conceptual and motivational material to illustrate how to use the intuitive interface for data analysis in JMP. Each chapter features concept-specific tutorials,

examples, brief reviews of concepts, step-by-step illustrations, and exercises.

Updated for JMP 13, JMP Start Statistics, Sixth Edition includes many new features, including:

The redesigned Formula Editor.

New and improved ways to create formulas in JMP directly from the data table or dialogs.

Interface updates, including improved menu layout.

Updates and enhancements in many analysis platforms.

New ways to get data into JMP and to save and share JMP results.

Many new features that make it easier to use JMP.

Describes the basics of using the SQL procedure and provides comprehensive reference information. The usage information includes retrieving data from single and multiple tables; selecting specific data from tables; subsetting, ordering, and summarizing data; updating tables; combining tables to create new tables and useful reports; performing queries on database management system (DBMS) tables; using PROC SQL with the SAS macro facility; and debugging and optimizing PROC SQL code. The reference information includes statements, dictionary components, and system options.

Provides conceptual information about the SAS programming language, as well as step-by-step examples that illustrate the concepts.

You just got the results from your study, and need to get some quick graphical views of your data before you begin the analysis. Do you need a crash course in the SG procedures (also known as ODS Graphics procedures) just to get a simple histogram? What should you do? The ODS Graphics Designer is the answer. With this application, you can use the interactive drag-and-drop feature to create many graphs, including histograms, box plots, scatter plot matrices, classification panels, and more. You can render your graph in batch with new data and output the results to any open ODS destination, or view the generated Graph Template Language (GTL) code as a leg-up to GTL programming. You can do all this with ease!

SAS(R) ODS Graphics Designer by Example: A Visual Guide to Creating Graphs Interactively describes in detail the features of the ODS Graphics Designer. The designer application lets you, the analyst, create graphs interactively so that you can focus on the analysis, and not on learning graph syntax. This book will take you step-by-step through the features of the designer, providing you with examples of graphs that are commonly used for the analysis of data in the health care, life sciences, and finance industries. The examples in this book will help you create just the right graph with ease!

Gives you a quick, convenient overview of new functionality, enhanced features, and new products that you might use in SAS 9.4.

For decades researchers and programmers have used SAS to analyze, summarize, and report clinical trial data. Now Chris Holland and Jack Shostak have updated their popular Implementing CDISC Using SAS, the first comprehensive book on applying clinical research data and metadata to the Clinical Data Interchange Standards Consortium (CDISC) standards.

Implementing CDISC Using SAS: An End-to-End Guide, Second Edition, is an all-inclusive guide on how to implement and analyze the Study Data Tabulation Model (SDTM) and the Analysis Data Model (ADaM) data and prepare clinical trial data for regulatory submission. Updated to reflect the 2017 FDA mandate for adherence to CDISC standards, this new edition covers creating and using metadata, developing conversion specifications, implementing and validating SDTM and ADaM data, determining solutions for legacy data conversions, and preparing data for regulatory submission. The book covers products such as Base SAS, SAS Clinical Data Integration, and the SAS Clinical Standards Toolkit, as well as JMP Clinical. Topics included in this new edition include an implementation of the Define-XML 2.0 standard, new SDTM domains, validation with Pinnacle 21 software, event narratives in JMP Clinical, and of course new versions of SAS and JMP software.

Any manager or user of clinical trial data in this day and age is likely to benefit from knowing how to either put data into a CDISC standard or analyzing and finding data once it is in a CDISC format. If you are one such person--a data manager, clinical and/or statistical programmer, biostatistician, or even a clinician--then this book is for you.

Learn how to solve basic statistical problems with Ron Cody's easy-to-follow style using the point-and-click SAS Studio tasks. Aimed specifically at the health sciences, Biostatistics by Example Using SAS Studio, provides an introduction to SAS Studio tasks. The book includes many biological and health-related problem sets and is fully compatible with SAS University Edition. After reading this book you will be able to understand temporary and permanent SAS data sets, and you will learn how to create them from various data sources. You will also be able to use SAS Studio statistics tasks to generate descriptive statistics for continuous and categorical data. The inferential statistics portion of the book covers the following topics: paired and unpaired t tests one-way analysis of variance N-way ANOVA correlation simple and multiple regression logistic regression categorical data analysis power and sample size calculations Besides describing each of these statistical tests, the book also discusses the assumptions that need to be met before running and interpreting these tests. For two-sample tests and N-way tests, nonparametric tests are also described. This book leads you step-by-step through each of the statistical tests with numerous screen shots, and you will see how to read and interpret all of the output generated by these tests. Experience with some basic statistical tests used to analyze medical data or classroom experience in biostatistics or statistics is required. Although the examples are related to the medical and biology fields, researchers in other fields such as psychology or education will find this book helpful. No programming experience is required. Loading data files into SAS University Edition? Click here for more information.

Use aggregate data to answer high-level business questions!

Data miners, data scientists, analytic managers, and analysts who work in all industries will find the insights in Randy Collica's Strategic Analytics and SAS: Using Aggregate Data to Drive Organizational Initiatives invaluable in their work. This book shows you how to use your existing data at aggregate levels to answer high-level business questions. Written in a detailed, step-by-step format, the multi-industry use cases begin with a high-level question that a C-level executive might ask. Collica then progresses through the steps to perform the analysis, including many tables and screenshots to guide you along the way. He then ends each use case with the solution to the high-level question. Topics covered include logistic analysis, models developed from surveys, survival analysis, confidence intervals, text mining and analysis, visual analytics, hypothesis tests, and size and magnitude of analytic effects. Connect the dots between detailed data on your customers and the high-level business goals of your organization with Strategic Analytics and SAS!

User experience monitoring is essential for enhancing the usability and performance of your mobile native app. How are your customers using your product? Which features do they prefer? How can you spot trouble before it adversely affects your product? This O’Reilly report provides an overview of several metrics you can apply, based on different use-cases. Author Wolfgang Beer explains the typical instrumentation and publishing process of mobile apps, and takes you through different instrumentation approaches. With screenshots from popular tools such as Google Analytics, Ruxit, Fabric, and Flurry Analytics, this report helps you choose the metrics that will help you improve your product’s performance. Monitor performance to understand your app’s stability and usability Measure app user engagement by identifying active and new users, and determining median session length Determine your app’s current retention and churn rates Gather business intelligence by defining users according to personas and lifetime value Oversee the service and infrastructure dependencies of your app in real time Visually track user behavior with heat maps and navigational paths Add automated or manual instrumentation before you publish your app