Books

The Power of People
Nigel Guenole, Jonathan Ferrar, and Sheri Feinzig. 5/26/2017. The Power of People. 1st ed., Pp. 250. Pearson FT Press. Publisher's VersionAbstract

Workforce analytics is the discovery, interpretation, and communication of meaningful patterns in workforce-related data to inform decision making and improve performance.

The human race’s quest for information is never-ending. Businesses and organizations are no exceptions. Business leaders continually seek out knowledge about their organizations to gain insights from all the data that exist. This is so they can make evidence-based decisions to improve their organization’s performance and gain competitive advantage in the marketplace.

The discipline called “analytics” exists to meet this need. Analytics concerning human resources, people and the workforce is known as workforce analytics, and that is the focus of the book, The Power of People. This book sets out how to establish, operate and lead workforce analytics to better serve organizational ambitions. 

Storytelling with Data
Cole Nussbaumer Knaflic. 11/2/2015. Storytelling with Data. 1st ed., Pp. 288. Wiley. Publisher's VersionAbstract

Don't simply show your data—tell a story with it! 

storytelling with data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation.

Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story.

Specifically, you'll learn how to:

  • Understand the importance of context

  • Determine the appropriate type of graph

  • Recognize and eliminate the clutter

  • Direct your audience's attention

  • Think like a designer when visualizing data

  • Leverage the power of storytelling to help your message resonate with your audience

Together, the lessons in this book will help you turn your data into high-impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—storytelling with data will give you the skills and power to tell it!

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The Model Thinker: What You Need to Know to Make Data Work for You
Scott E. Page. 11/27/2018. The Model Thinker: What You Need to Know to Make Data Work for You, Pp. 448. Publisher's VersionAbstract
The Many-Model Thinker 

This is a book about models. It describes dozens of models in straightforward language and explains how to apply them. Models are formal structures represented in mathematics and diagrams that help us to understand the world. Mastery of models improves your ability to reason, explain, design, communicate, act, predict, and explore.

This book promotes a many-model thinking approach: the application of ensembles of models to make sense of complex phenomena. The core idea is that many-model thinking produces wisdom through a diverse ensemble of logical frames. The various models accentuate different causal forces. Their insights and implications overlap and interweave. By engaging many models as frames, we develop nuanced, deep understandings. The book includes formal arguments to make the case for multiple models along with myriad real-world examples.

The book has a pragmatic focus. Many-model thinking has tremendous practical value. Practice it, and you will better understand complex phenomena. You will reason better. You exhibit fewer gaps in your reasoning and make more robust decisions in your career, community activities, and personal life. You may even become wise.

Twenty-five years ago, a book of models would have been intended for professors and graduate students studying business, policy, and the social sciences along with financial analysts, actuaries, and members of the intelligence community. These were the people who applied models and, not coincidentally, they were also the people most engaged with large data sets. Today, a book of models has a much larger audience: the vast universe of knowledge workers, who, owing to the rise of big data, now find working with models a part of their daily lives.

Organizing and interpreting data with models has become a core competency for business strategists, urban planners, economists, medical professionals, engineers, actuaries, and environmental scientists among others. Anyone who analyzes data, formulates business strategies, allocates resources, designs products and protocols, or makes hiring decisions encounters models. It follows that mastering the material in this book—particularly the models covering innovation, forecasting, data binning, learning, and market entry timing—will be of practical value to many.

Thinking with models will do more than improve your performance at work. It will make you a better citizen and a more thoughtful contributor to civic life. It will make you more adept at evaluating economic and political events. You will be able to identify flaws in your logic and in that of others. You will learn to identify when you are allowing ideology to supplant reason and have richer, more layered insights into the implications of policy initiatives, whether they be proposed greenbelts or mandatory drug tests.

These benefits will accrue from an engagement with a variety of models—not hundreds, but a few dozen. The models in this book offer a good starting collection. They come from multiple disciplines and include the Prisoners’ Dilemma, the Race to the Bottom, and the SIR model of disease transmission. All of these models share a common form: they assume a set of entities—often people or organizations—and describe how they interact.

The models we cover fall into three classes: simplifications of the world, mathematical analogies, and exploratory, artificial constructs. In whatever form, a model must be tractable. It must be simple enough that within it we can apply logic. For example, we cover a model of communicable diseases that consists of infected, susceptible, and recovered people that assumes a rate of contagion. Using the model we can derive a contagion threshold, a tipping point, above which the disease spreads. We can also determine the proportion of people we must vaccinate to stop the disease from spreading.

As powerful as single models can be, a collection of models accomplishes even more. With many models, we avoid the narrowness inherent in each individual model. A many-models approach illuminates each component model’s blind spots. Policy choices made based on single models may ignore important features of the world such as income disparity, identity diversity, and interdependencies with other systems.1 With many models, we build logical understandings of multiple processes. We see how causal processes overlap and interact. We create the possibility of making sense of the complexity that characterizes our economic, political, and social worlds. And, we do so without abandoning rigor—model thinking ensures logical coherence. That logic can be then be grounded in evidence by taking models to data to test, refine, and improve them. In sum, when our thinking is informed by diverse logically consistent, empirically validated frames, we are more likely to make wise choices.

 

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