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Winning with Data Science

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Winning with Data Science

A Handbook for Business Leaders

Columbia Business School Publishing,

15 mins. de lectura
8 ideas fundamentales
Audio y Texto

¿De qué se trata?

To be successful, today’s business leaders need a fundamental understanding of data science.

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Editorial Rating

8

Qualities

  • Eye Opening
  • Concrete Examples
  • For Beginners

Recommendation

Modern business leaders need to know data science basics. As authors and data scientists Howard Steven Friedman and Akshay Swaminathan explain, a company’s business leaders and its data science team need to collaborate in pursuit of larger business goals. For that to occur, leaders must be able to communicate their needs to their data scientists and understand the options they provide. Friedman and Swaminathan’s practical guide to data science offers a host of concrete examples to help business people and entrepreneurs master the fundamentals and put their new knowledge to work.

Summary

Collaborate with your data scientist team to create an optimal data workflow.

A team leader at a top financial firm, Steve, wanted to improve the company’s Recoveries Department operations — maximizing the amount of money they collected and minimizing costs. Hiring more staff wasn’t possible. So, the company needed a process that would allow them to use data-informed insights to prioritize some accounts over others and streamline employee workload.With these goals in mind, he approached the data science team. After discussing his priorities and the Recoveries Department’s constraints, the data scientists collaborated with Steve to develop a “data workflow” plan for the problem-solving effort.

Collecting data from diverse sources and bringing that data to a single location is typically an automated process called “extract, transform, and load (ETL).” Companies can extract data from existing databases, or they can create relational databases from customer purchase histories, phone calls, and the like. Data transformation involves preparing data for analysis, eliminating inconsistencies and anomalies...

About the Authors

Howard Steven Friedman is a data scientist with experience in both the private and public sectors. He is an adjunct professor at Columbia University. His previous books include Ultimate Price and Measure of a Nation. Akshay Swaminathan is a data scientist who focuses on health systems. He is a Knight-Hennessy scholar at the Stanford University School of Medicine.

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