Daniel Vaughan currently leads the data science function for Airbnb in Latin America. Previously he was Chief Data Officer and Head of Data Science at Telefonica Mexico. As CDO, he oversaw the design and monitoring of the company's big data strategy, from technical decisions such as the data lake implementation and optimization, to strategic aspects such as value-based ingestion and governance of all the relevant data sources. As Head of Data Science, he led a team of data scientists and engineers responsible for developing all predictive and prescriptive solutions in Mexico. An important part of this role was to work across the board with business stakeholders, helping them translate business questions into problems that could be solved through AI and data science. Before, he held positions as Senior Data Scientist at Banorte, and Economic Researcher at Banco de Mexico. He is passionate about finding new ways to translate business problems into prescriptive solutions, as well as strategies to speed up the transition to a data-driven culture. He holds a Ph.D. in economics from New York University (NYU) and has taught technical and non-technical courses at NYU (USA and UAE), Universidad de los Andes and Universidad ICESI (Colombia), CIDE and TEC de Monterrey (Mexico). He regularly lectures on the material of this book to MBA students at EGADE Business School in Mexico City.

While several market-leading companies have successfully transformed through data- and AI-driven approaches to business, the vast majority have yet to reap the benefits. How can your business and analytics units gain a competitive advantage by capturing the potential of this predictive revolution? This practical guide presents a battle-tested method to help you translate business decisions into tractable descriptive, predictive, and prescriptive problems.

Author Daniel Vaughan shows practitioners of data science and others interested in using AI not only how to ask the right questions but also how to generate value from data and analytics using modern AI technologies and decision theory principles. You’ll explore several use cases common to many enterprises, complete with examples you can apply when working to solve your own issues.

With this book, you’ll learn how to:

Break business decisions into stages and use predictive or prescriptive methods on each stage

Identify human biases when working with uncertainty

Customize optimal decisions to different customers using predictive and prescriptive methods

Ask business questions with high potential for value creation through AI and data-driven methods

Simplify complexity to tackle difficult business decisions with current predictive and prescriptive technologies