Thomas C. Redman – författare

The essential guide for data scientists and for leaders who must get more from their data science teams

The Economist boldly claims that data are now "the world''s most valuable resource." But, as Kenett and Redman so richly describe, unlocking that value requires far more than technical excellence. The Real Work of Data Science explores understanding the problems, dealing with quality issues, building trust with decision makers, putting data science teams in the right organizational spots, and helping companies become data-driven. This is the work that spells the difference between a good data scientist and a great one, between a team that makes marginal contributions and one that drives the business, between a company that gains some value from its data and one in which data truly is "the most valuable resource."

"These two authors are world-class experts on analytics, data management, and data quality; they''ve forgotten more about these topics than most of us will ever know. Their book is pragmatic, understandable, and focused on what really counts. If you want to do data science in any capacity, you need to read it."—Thomas H. Davenport, Distinguished Professor, Babson College and Fellow, MIT Initiative on the Digital Economy

"I like your book. The chapters address problems that have faced statisticians for generations, updated to reflect today''s issues, such as computational Big Data."—Sir David Cox, Warden of Nuffield College and Professor of Statistics, Oxford University

"Data science is critical for competitiveness, for good government, for correct decisions. But what is data science? Kenett and Redman give, by far, the best introduction to the subject I have seen anywhere. They address the critical questions of formulating the right problem, collecting the right data, doing the right analyses, making the right decisions, and measuring the actual impact of the decisions. This book should become required reading in statistics and computer science departments, business schools, analytics institutes and, most importantly, by all business managers." —A. Blanton Godfrey, Joseph D. Moore Distinguished University Professor, Wilson College of Textiles, North Carolina State University

The essential guide for data scientists and for leaders who must get more from their data science teams

The Economist boldly claims that data are now "the world''s most valuable resource." But, as Kenett and Redman so richly describe, unlocking that value requires far more than technical excellence. The Real Work of Data Science explores understanding the problems, dealing with quality issues, building trust with decision makers, putting data science teams in the right organizational spots, and helping companies become data-driven. This is the work that spells the difference between a good data scientist and a great one, between a team that makes marginal contributions and one that drives the business, between a company that gains some value from its data and one in which data truly is "the most valuable resource."

"These two authors are world-class experts on analytics, data management, and data quality; they''ve forgotten more about these topics than most of us will ever know. Their book is pragmatic, understandable, and focused on what really counts. If you want to do data science in any capacity, you need to read it."—Thomas H. Davenport, Distinguished Professor, Babson College and Fellow, MIT Initiative on the Digital Economy

"I like your book. The chapters address problems that have faced statisticians for generations, updated to reflect today''s issues, such as computational Big Data."—Sir David Cox, Warden of Nuffield College and Professor of Statistics, Oxford University

"Data science is critical for competitiveness, for good government, for correct decisions. But what is data science? Kenett and Redman give, by far, the best introduction to the subject I have seen anywhere. They address the critical questions of formulating the right problem, collecting the right data, doing the right analyses, making the right decisions, and measuring the actual impact of the decisions. This book should become required reading in statistics and computer science departments, business schools, analytics institutes and, most importantly, by all business managers." —A. Blanton Godfrey, Joseph D. Moore Distinguished University Professor, Wilson College of Textiles, North Carolina State University

People and Data is an innovative exploration of the relationship between non-data professionals and data in an organization''s success, and why it is only when they work together that a business can unlock its full potential.This book explains how most companies are yet to take advantage of the value that data offers. Their structures and processes are unfit for data and their biggest mistake is that regular employees are not included in the data-driven effort. People and Data illustrates how to change this. It shows how and why improving data quality should be an organization''s first priority, how to tackle the tough organizational issues, such as departmental silos, that get in the way and how to upskill the whole workforce to get the best out of the organization''s data. It is a practical guide written by a global expert which explains how companies can put their data to work by building it into all aspects of the business including their structure, culture and workforce design. By infusing the whole organization with data in this way employees at any level can use insights from the data to improve business performance. Full of practical tips and advice, People and Data includes a Resource Centre featuring a curriculum for training employees, and eight tools that will help companies to leverage their data to meet their business goals and upskill their employees so that everyone can benefit from the power of data. With important takeaways and real-world examples from organizations including AT&T and Morgan Stanley, this book is essential reading for all those wanting to allow their people and data to reach their full potential but are not sure where to start.