How to lead the change Analytics are driving big changes, not only in what marketing departments do but in how they are organized, staffed, led, and run. Leaders are grappling with issues that range from building an analytically driven marketing organization and determining the kinds of structure and talent that are needed to leading interactions with IT, finance, and sales and creating a unified view of the customer. The Analytical Marketer provides critical insight into the changing marketing organization—digital, agile, and analytical—and the tools for reinventing it. Written by the head of global marketing for SAS, The Analytical Marketer is based on the author’s firsthand experience of transforming a marketing organization from “art” to “art and science.” Challenged and inspired by their company’s own analytics products, the SAS marketing team was forced to rethink itself in order to take advantage of the new capabilities that those tools offer the modern marketer. Key marketers and managers at SAS tell their stories alongside the author’s candid lessons learned as she led the marketing organization’s transformation. With additional examples from other leading companies, this book is a practical guide and set of best practices for creating a new marketing culture that thrives on and adds value through data and analytics.

Discovering JMP 13 provides a basic introduction to using JMP. For new users of JMP, this is a great place to start. The book also includes details about importing your data into JMP, analyzing the data, and sharing the results.

JMP 13 Basic Analysis covers the initial types of analyses that you often perform in JMP, such as univariate, bivariate, and oneway analyses. Creating tables of summary statistics with the Tabulate platform is included along with approximating sampling distributions using bootstrapping. Find information about how to clean up your data before performing analyses. Read about performing powerful parametric and nonparametric simulation capability using Simulation.

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.

Start with JMP 13 Essential Graphing to find the ideal graph for your data. The book begins with Graph Builder, a quick way to create graphs in a drag-and-drop window. Line charts, ellipses, box plots, and maps are just a few of the graphs available in Graph Builder. Find information about creating other types of plots: bubble plots, scatterplots, parallel plots, and more.

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.

The JMP 13 JSL Syntax Reference focuses on functions and their arguments, and messages that you send to objects and display boxes. Notes and examples are included.

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.

JMP 13 Quality and Process Methods describes tools for evaluating and improving processes. The book begins by discussing creating control charts, which let you visualize process measurements over time, quantify common cause variation, and identify special cause variation. Details about estimating your process capability based on measurement systems analysis studies are included. Lastly, the book discusses Pareto plots and cause-and-effect diagrams to identify root causes of variability.

JMP 13 Reliability and Survival Methods provides details about evaluating and improving reliability in a product or system and analyzing survival data for people and products. The book explains how to fit the best distribution to your time-to-event data or analyze destruction data. A few other topics include analyzing competing causes of failure, modeling reliability as improvements are made over time, and analyzing recurring events.

JMP 13 Scripting Guide provides details for taking advantage of the powerful JMP Scripting Language (JSL). Learn how to write and debug scripts, manipulate data tables, construct display boxes, create JMP applications, and more.

Using JMP 13 shows you how to perform common tasks such as importing data, setting column properties, exporting analyses as graphics or HTML, and modifying JMP preferences. Details about connecting to SAS and working in the Formula Editor are also provided.

Design quality SAS software and evaluate SAS software quality SAS Data Analytic Development is the developer’s compendium for writing better-performing software and the manager’s guide to building comprehensive software performance requirements. The text introduces and parallels the International Organization for Standardization (ISO) software product quality model, demonstrating 15 performance requirements that represent dimensions of software quality, including: reliability, recoverability, robustness, execution efficiency (i.e., speed), efficiency, scalability, portability, security, automation, maintainability, modularity, readability, testability, stability, and reusability. The text is intended to be read cover-to-cover or used as a reference tool to instruct, inspire, deliver, and evaluate software quality. A common fault in many software development environments is a focus on functional requirements—the what and how—to the detriment of performance requirements, which specify instead how well software should function (assessed through software execution) or how easily software should be maintained (assessed through code inspection). Without the definition and communication of performance requirements, developers risk either building software that lacks intended quality or wasting time delivering software that exceeds performance objectives—thus, either underperforming or gold-plating, both of which are undesirable. Managers, customers, and other decision makers should also understand the dimensions of software quality both to define performance requirements at project outset as well as to evaluate whether those objectives were met at software completion. As data analytic software, SAS transforms data into information and ultimately knowledge and data-driven decisions. Not surprisingly, data quality is a central focus and theme of SAS literature; however, code quality is far less commonly described and too often references only the speed or efficiency with which software should execute, omitting other critical dimensions of software quality. SAS® software project definitions and technical requirements often fall victim to this paradox, in which rigorous quality requirements exist for data and data products yet not for the software that undergirds them. By demonstrating the cost and benefits of software quality inclusion and the risk of software quality exclusion, stakeholders learn to value, prioritize, implement, and evaluate dimensions of software quality within risk management and project management frameworks of the software development life cycle (SDLC). Thus, SAS Data Analytic Development recalibrates business value, placing code quality on par with data quality, and performance requirements on par with functional requirements.

Learn all you need to know about seven key innovations disrupting business analytics today. These innovations—the open source business model, cloud analytics, the Hadoop ecosystem, Spark and in-memory analytics, streaming analytics, Deep Learning, and self-service analytics—are radically changing how businesses use data for competitive advantage. Taken together, they are disrupting the business analytics value chain, creating new opportunities. Enterprises who seize the opportunity will thrive and prosper, while others struggle and decline: disrupt or be disrupted. Disruptive Business Analytics provides strategies to profit from disruption. It shows you how to organize for insight, build and provision an open source stack, how to practice lean data warehousing, and how to assimilate disruptive innovations into an organization. Through a short history of business analytics and a detailed survey of products and services, analytics authority Thomas W. Dinsmore provides a practical explanation of the most compelling innovations available today. What You'll Learn Discover how the open source business model works and how to make it work for you See how cloud computing completely changes the economics of analytics Harness the power of Hadoop and its ecosystem Find out why Apache Spark is everywhere Discover the potential of streaming and real-time analytics Learn what Deep Learning can do and why it matters See how self-service analytics can change the way organizations do business Who This Book Is For Corporate actors at all levels of responsibility for analytics: analysts, CIOs, CTOs, strategic decision makers, managers, systems architects, technical marketers, product developers, IT personnel, and consultants.

For SAS programmers or analysts who need to generalize their programs or improve programming efficiency, Art Carpenter thoroughly updates his highly successful second edition of Carpenter's Complete Guide to the SAS Macro Language with an extensive collection of new macro language techniques and examples. Addressing the composition and operation of the SAS macro facility and the SAS macro language, this third edition offers nearly 400 ready-to-use macros, macro functions, and macro tools that enable you to convert SAS code to macros, define macro variables, and more! Users with a basic understanding of Base SAS who are new to the SAS macro language will find more detail, utilities, and references to additional learning opportunities; advanced macro language programmers who need help with data-driven macros and dynamic application development will find greatly expanded treatment of these topics. This revised and enlarged edition includes the following topics: New and expanded introduction to the macro language Functions, automatic macro variables, and macro statements new to the macro language Expanded macro language tools that interface with the operating system Expanded data-driven methodologies used to build dynamic applications Expanded discussion of list processing, with four alternative approaches presented Additional file and data management examples Expanded discussion of CALL EXECUTE and DOSUBL New discussion of using the macro language on remote servers Expanded discussion and examples of macro quoting Far beyond a reference manual issued from an “ivory tower,” this book is pragmatic and example-driven: Yes, you will find syntax examples; yes, the code is explained. But the focus of this book is on actual code used to solve real-world business problems. In fact, an entire appendix is dedicated to listing the nearly 70 classes of problems that are solved by programs covered in this edition. Discussion of the examples elucidates the pros and cons of the particular solution and often suggests alternative approaches. Therefore, this book provides you both a compendium of reusable and adaptable code, and opportunities for deepening your understanding and growing as a SAS programmer.

Data Analysis Plans: A Blueprint for Success Using SAS gets you started on building an effective data analysis plan with a solid foundation for planning and managing your analytics projects. Data analysis plans are critical to the success of analytics projects and can improve the workflow of your project when implemented effectively. This book provides step-by-step instructions on writing, implementing, and updating your data analysis plan. It emphasizes the concept of an analysis plan as a working document that you update throughout the life of a project.

This book will help you manage the following tasks:

control client expectations

limit and refine the scope of the analysis

enable clear communication and understanding among team members

organize and develop your final report

SAS users of any level of experience will benefit from this book, but beginners will find it extremely useful as they build foundational knowledge for performing data analysis and hypotheses testing. Subject areas include medical research, public health research, social studies, educational testing and evaluation, and environmental studies.

Filled with helpful examples and real-life projects of SAS users, A Recipe for Success Using SAS University Edition is an easy guide on how to start applying the analytical power of SAS to real-world scenarios. This book shows you: how to start using analytics how to use SAS to accomplish a project goal how to effectively apply SAS to your community or school how users like you implemented SAS to solve their analytical problems A beginner’s guide on how to create and complete your first analytics project using SAS University Edition, this book is broken down into easy-to-read chapters that also include quick takeaway tips. It introduces you to the vocabulary and structure of the SAS language, shows you how to plan and execute a successful project, introduces you to basic statistics, and it walks you through case studies to inspire and motivate you to complete your own projects. Following a recipe for success using this book, harness the power of SAS to plan and complete your first analytics project!

To satisfy end users who want easily accessible answers, many software vendors are looking to add analytics and reporting capabilities to their applications. Embedding analytics into applications can lead to wider adoption and product use, improved user experience, and differentiated products, but embedding analytics can also come with challenges and complexities. In this report, author Courtney Webster reviews several approaches and methods for embedding analytics capabilities into your applications. Should you implement a separate reporting portal, an in-application reporting tab, or go all in with a fully embedded in-page analytics solution? And do you build your own or buy a solution out of the box? To help you choose the right embedded analytics tool, Webster examines seven challenges—from customization, usability, and capabilities to scalability, performance, and data structure support—and presents best practice solutions for each.

One area where data analytics can have profound effect is your company’s procurement process. Some organizations spend more than two thirds of their revenue buying goods and services, making procurement—out of all business activities—a key element in achieving cost reduction. This report examines how your company can significantly improve procurement analytics to solve business questions quickly and effectively. Author Federico Castanedo, Chief Data Scientist at WiseAthena.com, explains how a probabilistic, bottom-up approach can significantly increase the quality, speed, and scalability of your data preparation operations—whether you’re integrating datasets or cleaning and classifying them. You’ll learn how new solutions leverage automation and machine learning, including the Tamr platform, and help you take advantage of several data-driven actions for procurement—including compliance, price arbitrage, and spend recovery.

Learning Pentaho CTools is a comprehensive guide to building sophisticated and custom analytics dashboards using the powerful capabilities of Pentaho CTools. This book walks you through the process of creating interactive dashboards, integrating data sources, and applying data visualization best practices. You'll quickly gain the expertise needed to create impactful dashboards with ease. What this Book will help me do Master installing and configuring CTools for Pentaho to jumpstart dashboard development. Harness diverse data sources and deliver data in formats like CSV, JSON, and XML for customized analytics. Design and implement dynamic, visually stunning dashboards using Community Dashboard Framework (CDF). Deploy and integrate plugins, leverage widgets, and manage dashboards effectively with version control. Enhance interactivity by customizing dashboard components, charts, and filters to suit unique requirements. Author(s) None Gaspar, an expert in Pentaho and its tools, has been a Senior Consultant at Pentaho, where he gained in-depth experience crafting analytics solutions. He brings to this book his teaching passion and field expertise, combining theoretical insights with practical applications. His approachable style ensures readers can follow technical concepts effectively. Who is it for? This book is ideal for developers who are looking to enhance their understanding of Pentaho's CTools portfolio to build advanced dashboards. A working knowledge of JavaScript and CSS will enable readers to get the most out of this guide. Whether you aim to extend your analytics capabilities or learn the tools from scratch, this book bridges the gap between learning and application.

This book provides a scientific modeling approach for conducting metrics-based quantitative risk assessments of cybersecurity vulnerabilities and threats. This book provides a scientific modeling approach for conducting metrics-based quantitative risk assessments of cybersecurity threats. The author builds from a common understanding based on previous class-tested works to introduce the reader to the current and newly innovative approaches to address the maliciously-by-human-created (rather than by-chance-occurring) vulnerability and threat, and related cost-effective management to mitigate such risk. This book is purely statistical data-oriented (not deterministic) and employs computationally intensive techniques, such as Monte Carlo and Discrete Event Simulation. The enriched JAVA ready-to-go applications and solutions to exercises provided by the author at the book’s specifically preserved website will enable readers to utilize the course related problems. • Enables the reader to use the book's website's applications to implement and see results, and use them making ‘budgetary’ sense • Utilizes a data analytical approach and provides clear entry points for readers of varying skill sets and backgrounds • Developed out of necessity from real in-class experience while teaching advanced undergraduate and graduate courses by the author Cyber-Risk Informatics is a resource for undergraduate students, graduate students, and practitioners in the field of Risk Assessment and Management regarding Security and Reliability Modeling. Mehmet Sahinoglu, a Professor (1990) Emeritus (2000), is the founder of the Informatics Institute (2009) and its SACS-accredited (2010) and NSA-certified (2013) flagship Cybersystems and Information Security (CSIS) graduate program (the first such full degree in-class program in Southeastern USA) at AUM, Auburn University’s metropolitan campus in Montgomery, Alabama. He is a fellow member of the SDPS Society, a senior member of the IEEE, and an elected member of ISI. Sahinoglu is the recipient of Microsoft's Trustworthy Computing Curriculum (TCC) award and the author of Trustworthy Computing (Wiley, 2007).

Enhance your SAS® data wrangling skills with high precision and parallel data manipulation using the new DS2 programming language.

This book addresses the new DS2 programming language from SAS, which combines the precise procedural power and control of the Base SAS DATA step language with the simplicity and flexibility of SQL. DS2 provides simple, safe syntax for performing complex data transformations in parallel and enables manipulation of native database data types at full precision. It also introduces PROC FEDSQL, a modernized SQL language that blends perfectly with DS2. You will learn to harness the power of parallel processing to speed up CPU-intensive computing processes in Base SAS and how to achieve even more speed by processing DS2 programs on massively parallel database systems. Techniques for leveraging Internet APIs to acquire data, avoiding large data movements when working with data from disparate sources, and leveraging DS2’s new data types for full-precision numeric calculations are presented, with examples of why these techniques are essential for the modern data wrangler.

While working through the code samples provided with this book, you will build a library of custom, reusable, and easily shareable DS2 program modules, execute parallelized DATA step programs to speed up a CPU-intensive process, and conduct advanced data transformations using hash objects and matrix math operations.