Keep Your Data Science Efforts from Derailing

Business, Case Studies Great America Ballroom J

Many of the ideas covered in this talk stemmed from discussing professional experiences amongst a group of Washington DC-based Data Scientists (Marck Vaisman, Harlan Harris, and Sean Murphy) in doing data science work within various organizations. Some frustrations from dealing with management that was not data savvy, from working within limited technical environments which inhibited analytical efforts, and lastly from the perceived gap between what data scientists can do and reality.

Drawing from all of these experiences, we decided to take action and help bring clarity to both sides of the table: the organizations wanting the benefits and the practitioners executing the data science projects. Some of the questions we investigated include:

  • what if data scientists could better describe what they do?
  • what if data scientists could better understand what they have to learn to be competitive?
  • what if organizations could better understand not only what projects are possible but also the value that can be created from one data scientist or from a team
  • what are the kinds of pitfalls that derail analytical efforts?
  • what do organizations need to consider before adopting Data Science?

We highlight common pitfalls facing organizations planning or executing data science. We will cover optimal organizational mindsets, the technical considerations and end with showing the diversity in skills within the Data Science practitioner community, as shown by a survey of several hundred Data Scientists from around the world. This talk is based on material from the upcoming Bad Data Handbook as well as from the analysis of the survey results.

The intent of this session is to surface many issues that have arisen within Data Science as a young, burgeoning, and potentially highly profitable field, and we attempt to establish a common framework for better communication. New practitioners in this field and organizations that are beginning to incorporate data science into their processes will benefit from attending this session.

Photo of Marck Vaisman

Marck Vaisman

Booz Allen Hamilton

Marck designs data-driven solutions to help clients make better business decisions, recognize opportunities, experiment, gain insights, and solve difficult problems using large datasets and a combination of tools. His experience pulls from multiple disciplines including Internet, telecommunications, and high tech. Marck is an experienced R programmer and advocate, and a contributing author to The Bad Data Handbook and Analyzing the Analyzers. He teaches graduate level classes at Georgetown and George Washington University. Marck founded Data Community DC, an organization that promotes Data Science and Analytics practitioners in the Washington DC Metro area. He holds a B.S. in Mechanical Engineering from Boston University and an MBA from Vanderbilt University.

Photo of Sean Murphy

Sean Murphy


Sean Patrick Murphy, with degrees in mathematics, electrical engineering, and biomedical engineering and an MBA from Oxford University, has served as a senior scientist at the Johns Hopkins Applied Physics Laboratory for the past ten years. Previously, he served as the Chief Data Scientist at WiserTogether, a series A funded health care analytics firm, and the Director of Research at Manhattan Prep, a boutique graduate educational company. He was also the co-founder and CEO of a big data-focused startup: CloudSpree.


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