But until now, there has been little practical guidance for organizations to formalize decision making and integrate their decisions with data.
With this book, authors L. Y. Pratt and N. E. Malcolm fill this gap. They present a step-by-step method for integrating technology into decisions that bridge from actions to desired outcomes, with a focus on systems that act in an advisory, human-in-the-loop capacity to decision makers.
This handbook addresses three widespread data-driven decision-making problems:
L. Y. Pratt, PhD, Chief Scientist at Quantellia, has been delivering artificial intelligence and machine learning solutions for her clients for over 30 years. These include the Human Genome Project, the Colorado Bureau of Investigation, the US Department of Energy, SAP, and the Administrative Office of the US Courts. She is a machine learning pioneer, having led the teams that invented inductive transfer and decision intelligence (DI). Pratt received the CAREER award from the National Science Foundation, an innovation award from Microsoft, and the Exemplary Research Award from the Colorado Advanced Software Institute (CASI). Formerly a computer science professor at the Colorado School of Mines, Pratt has appeared multiple times on national television and NPR, has given two TEDx talks, and is a respected AI and DI speaker worldwide. Recognized by the Women Innovators and Inventors Project, Pratt continues to push the boundaries of technology as one of the creators and evangelists for decision intelligence, which is the next phase of artificial intelligence, and which will define how AI is used in the 21st century.
N. E. Malcolm, COO at Quantellia, has over 25 years of experience managing and delivering enterprise software, data science, machine learning, and decision intelligence projects. Malcolm has spent five years on the Quantellia executive team with Dr. Pratt, continuously improving and developing best practices for both decision intelligence and Agile AI methodologies and delivering AI and DI projects including: Data science and data management on several large enterprise projects to improve decision making in the telecommunications industryMachine learning to develop a digital twin of retiring key employees for a small financial companyMachine learning to better understand medical device failuresMachine learning for computer security for a medium-sized computer security company Machine learning for customer retention for a community bankEstablishment of a decision intelligence center of excellence for a G7 national bank A NASA decision intelligence STTRMalcolm holds a BS in mathematics from MIT and an MS in computer science from USC.