LEAN SIX SIGMA WORLD CONFERENCE
Data Science for Six Sigma Professionals
Presenter: James E. Duarte, Principal, LJDUARTE & Associates, LLC, Clermont, FL, USA
Data Science, Disruption, Six Sigma, Analytics
Data science is the process of asking meaningful questions, and getting answers, from data. By that definition Six Sigma professionals are, and always have been, data scientists throughout the DMAIC process. This presentation examines the breadth of skills and requirements for data scientists from the perspective of the Six Sigma professional. As Six Sigma projects have the opportunity to mine data bases and data warehouses, the types of data scientists can be better defined. Management seems to have the impression that “everything data” belongs to IT. This “myth” will be dispelled with a collaborative model that makes much more sense than putting all the responsibility on IT. This paper will describe four types of data scientists. First, is the technology professional who needs to collect, store, cleanse, format and provide access to data for those who need to perform analytics (Data Scientist 1 or DS1). Second is the advanced analytics specialist who can perform machine learning and predictive modeling to enhance more traditional tools like Design of Experiments in support of the Analysis phase of DMAIC (Data Scientist 2 or DS2). Third is the analytics application expert who understands the products and processes. This person works closely with the DS2 and process owners to optimize data and analytics for problem solving and project success to maximize monetary returns and customer satisfaction. This person also may write reports for management consumption on progress and recommendations (Data Scientist 3 or DS3). Fourth works closely with the DS3 to validate reports, ensure acceptability by management and obtain answers to additional management questions either personally or with other data scientists when asked by management for clarification (Data Scientist 4 or DS4). The four types have been described as the strategist, the boundary spanner, the applications ninja, and the communicator or storyteller. Six Sigma professionals need not feel that they be proficient in all forms of data science. Areas are explored for having management understand the differences so that when a request for a “Data Scientist” is presented, then they can ask, “Which type?” Only then will the organization be sure to get the right talents in the right place to support the organization. By understanding these areas, Six Sigma professionals can identify new opportunities for their professional development, including data science skills that naturally leverages their strengths and career capabilities.