Six sigma is more a business philosophy rather than a discipline. Typical job titles include Quality Manager, Project Manager, Business Analyst and many more. Promoted by Motorola and GE a few decades ago, it originated in manufacturing and spread other industries. Examples industries include healthcare, life sciences, financial services, and energy. Six Sigma is used for quality control and to optimize business processes in large corporations.
The Lean Six Sigma Linkedin group has 570,000+ members, twice as large as any other analytic LinkedIn group. The strategy is simple: focus your efforts on the 20% of your time that results in 80% of the value. The idea of Six Sigma is to eliminate causes of variances in business processes and improve quality. Many people consider Six Sigma to be outdated. However, the fundamental concepts are solid and will remain. These are also fundamental concepts for all data scientists. Six sigma is a discipline more suited for quality professionals than for statisticians.
Data science is an interdisciplinary field to extract knowledge or insights from data in various forms. In contrast to Six Sigma, Data science is typically applied to “Big Data”. It employs techniques not only from statistics but also computer science, in particular from the subdomains of machine learning and databases.
Job titles include data scientist, senior data analyst, director of analytics and many more. Data science is applied in many fields, but especially marketing, healthcare, finance, and security.
Projects may include recommendation engines, root cause analysis, automated bidding, forensics, and early detection of terrorist activity or pandemics. Important concepts are automation and algorithms running real-time in production mode, e.g. to detect fraud, predict weather or predict home prices for each home. An example project is the creation of the fast growing data science Twitter profile for computational marketing. It uses big data to generate automated high quality, syndicated content generation.
While Data Scientist use tools and techniques applying them to a broader array application areas (compared to Six Sigma practitioners), both disciplines use a similar approach from a 30,000-foot view: You still have to define the problem you want to address and you still have to define how you will go about addressing it. You still have to think about measuring and relevant variables. The problems associated with small data are still present in big data: Clean up of missing and incorrect data entries, sampling bias, overlooked critical variables, etc…and you will need to address every one of these things and more before you move on to actual data analysis. You still have to think about how you are going to analyze the data and the appropriateness of the methods of analysis. You still have to think about of the results of your analysis and what they will mean regarding the expected improvement or prediction. You still have to think about the kinds of controls you will put in place to guarantee that the improvements and/or accurate predictions.