Over the past year, a number of authors have published books chronicling how data is impacting businesses and individuals today, exploring the future implications of data for society, and providing strategies for optimizing these opportunities. Whether you are still shopping for holiday gifts or just catching up on your reading, the Center for Data Innovation presents its recommendations for the 10 Best Books on Data Innovation of 2014.
MIT Media Lab scientist David Rose calls the connected devices in the Internet of Things “enchanted objects” — technologies that previously have only existed in fairy tales and science fiction. Smart watches, cars, trash cans, and hundreds of other common objects will soon be embedded in a network that Rose argues serves to enhance human relationships and ignite the desires for longevity, creativity, and omniscience. Enchanted Objects: Design, Human Desire, and the Internet of Things serves as a blueprint for a connected future, where the Internet of Things offers both technological efficiency and fulfillment of human desires.
Dataclysm, by OkCupid co-founder Christian Rudder, explores some of the unexpected insights that have come from social media data. Influenced by the site’s popular OkTrends data visualization blog, the book touches on data from OkCupid along with Facebook, Twitter, Google, and other sources. Many of the insights involve relationships and sexual orientation—for which OkCupid has data in spades—including the ages at which men and women think people of the opposite sex look most attractive and how one particular demographic expresses sexual desire much less frequently than others.
Long before the term “data visualization” was coined, people used diagrams to organize and show relationships between information. One popular means of visualization, the tree diagram, has been around since the ancient Mesopotamian era. Manuel Lima’s The Book of Trees: Visualizing Branches of Knowledge traces the history of tree diagrams, from those earliest examples through medieval genealogies and contemporary computer storage visualizations. Lima’s hundreds of examples illustrate the versatility the tree diagram and point to a near-universal human proclivity for representing hierarchies in this way. Lima, a New York-based designer and teacher, is best known for his 2011 work Visual Complexity: Mapping Patterns of Information.
Thomas Davenport’s Big Data at Work: Dispelling the Myths, Uncovering the Opportunities is a guide to big data in the enterprise. Geared toward entrepreneurs and established managers alike, the book details the business implications of large-scale data analysis from management to sales across a broad range of industries. Along the way, Davenport, a Babson College information technology management professor, gives working definitions of key data concepts and attempts to dispel myths about what data can and can not do for organizations. With case studies from companies including UPS, GE, Amazon and Citigroup, Davenport illustrates the opportunities and best practices of large-scale data management and integration into existing systems. The book also includes tips on hiring and training data science talent, as well as an overview of technologies that help companies work with their data.
To get a look at the personal side of data science, author Sebastian Gutierrez has assembled a collection of in-depth interviews with prominent data scientists. With experts from a range of sectors include e-commerce, social networks, media, and non-profits, Gutierrez showcases the diverse set of industries where data scientists are employing their skills. Each interview explores the career paths of data scientists, the tools and techniques they use to solve problems, and the advice they have for those workers just beginning down this journey. Perhaps most interesting, the data scientists interviewed for this book give their take on the future of the profession and how it can be used to help build a better world.
In The Leading Indicators, economist and investor Zachary Karabell offers a history and critique of macroeconomic indicators—statistics like gross domestic product, unemployment, and inflation, that policymakers and financiers use to forecast the economic future. Karabell argues that while these indicators have a profound influence on global politics and finance, they are easily misapplied and often too coarse to aid in highly specific forecasts. They were designed for a bygone era, he contends, before the explosion of data in the 21st century, and data’s widespread availability today may help address some of the major indicators’ problems. Karabell recommends drawing on drawing from big data sources to create “bespoke indicators” for particular business questions, less grand in scope but with more predictive power than traditional indicators.
That open data went from a niche topic among a few computer scientists and activists to a subject of national debate and a presidential executive order in a few short years is due in no small part to the work of open data evangelists like Joel Gurin, a senior advisor at New York University’s GovLab. Open Data Now is at once a primer on what open data is and how it can be used, as well as a call to action for government to embrace open data and make these applications possible. The book contains many examples of present and future applications for open data, which are sure to prove inspiring for entrepreneurs, scientists, and tech companies alike, but the book’s real value will be in convincing policymakers that the demand for government data is massive and not to be ignored.
Although baseball analytics—known as sabermetrics—captured the popular imagination following the publication of the bestseller Moneyball in 2003, that book only presented a case study of a successful analytics deployment. The Sabermetric Revolution: Assessing the Growth of Analytics in Baseball, by former New York Mets sabermetrician Benjamin Baumer and sports economist Andrew Zimbalist, chronicles the history of other such initiatives, offers insight into the future of the field, and provides an accessible primer to some of the math behind sabermetrics. The authors conclude that as the use of sabermetrics expands, teams will have to innovate with new methods and approaches to gain ever more insights from player data.
Uncharted follows the authors, applied mathematicians Erez Aiden and Jean-Baptiste Michel, through their work in the field of “culturomics.” Culturomics, which consists of analyzing historical and social trends through quantitative text analysis, borrows methods from text mining, historical time-series analysis and sociology. Work in the field is typified by Google’s Ngram Viewer, which the duo helped create, and which allows users to compare usage rates of words in various different languages, going back decades or centuries. The book explores a number of questions the pair attempted to answer using culturomics methods, including inquiries about how quickly technology spreads and how grammar changes over time.
After going on numerous unsuccessful dates with men she met online, New York-based media entrepreneur Amy Webb decided to use a data-driven approach to finding a partner. First, she developed a comprehensive list of traits she was looking for in a man, assigning each trait a point value and resolving to go on dates only with men who scored above a certain threshold. Then, she created dating site profiles posing as a man, to study the behaviors of women she would be up against in the online dating community. Finally, she changed her real profile to reflect what she’d learned. Her methods worked, and she eventually met her future husband on one of the sites.