November 18, 201510 yr Quote We had a recent discussion on this topic. All forum members are invited to continue this discussion here. Shahjahan H I know, Data analysis is all about analysing the big junk of data and presenting it in a meaningful way whereas 6 Sigma is all about continuous process improvement. Any other thoughts between Data Analysis Vs Six Sigma? Vishwadeep Khatri Hi Shahjahan, great question. Six Sigma became popular because of its focus on data driven decision making. There are decisions to be made in each phase of a Six Sigma project and we prefer to make those decisions utilizing the right kind of historical, experimental or simulated data. So, data analysis (before analysis we need to ensure that it is the right data for the right purpose in the right format and being planned for use for a fitting purpose) is the core part of Six Sigma You are likely to analyze some kind of data in each phase of a Six Sigma with right validations and a clear objective in mind. Six Sigma is likely to provide good direction and more sting to data analysis. Shahjahan H Thanks VK, I understand about the 6 Sigma - step by step approach (DMAIC / DMADV). Can we say 6 Sigma is the base for Data Analysis / Data Science? As, both 6 Sigma and Data Science uses Statistics techniques to a large extent. Also, Data Science is the advancement of technology, where the simple Excel and Minitab can not be used extensively for Data Science and it requires tools like R, SAS & Python for extraction of the data and to make more sense of the data? Shahjahan H I am coming to the point where, In software industry, we say "Mainframe technology" is becoming outdated / old one and the companies are moving towards advance technology like SAP/Oracle/Cloud etc and MF is considered as Legacy system and slowly degrading. Similarly, at some point of time, will 6 Sigma be considered like that and more preference will be given to Data Science professionals (due to the advancement in technology) and what will be the future for 6 Sigma BB / GB / MBB professionals? Vishwadeep Khatri Hi Shahjahan, you have brought out a very valid point. Thanks for bringing that up. The way we look at Six Sigma today is very different from how it was started by Motorola in 1987. It has been evolving. An MBB curriculum now a days has included Simulation, Advanced DFSS methods, Creativity and Innovation etc. Current outlook - Even today, for a Lean Six Sigma project requiring Big Data analytics, we ensure that experts in that area are part of Lean Six Sigma team in the right phases of the project. Future Outlook - If there is enough demand about Data Science including Big Data, Master Black Belt curriculum is likely to be revised to include these. We carry out surveys every year to be able to get recommendations in for revisions in MBB curriculum of our own. Shahjahan H Thanks VK for your time for giving the clarification. Soon, we can expect a change in the MBB curriculum and we always look for a betterment of the course and eager to learn the new methods of learning with the technology change. Sushant Kaul Valid point : Lets assume a study ; there must be at least 1000 Indian Million revenue based companies. It's not possible to be without a huge MIS Or ERP system. Tons of data &graphs !!! So if data & graphs are already available then are all 1000 companies making best out of available resources !!!! As already highlighted DMAIC is one of the best approach to deal with data ... Paul Astle Shahjahan, as you know the success of LSS relies on fact based decision making. You will always need to do some sort of analysis on data that you have either obtained from a data repository or collected yourself and verified the integrity of. If you personally don't have the necessary skills to be able to analyse your data set, then you need to ensure that you have someone on your LSS project team who can. As a MBB you are expected to have an advanced level of statistical analysis skills to accept/reject the null hypothesis, ensure correct sample size and test for statistical significance etc. So, as I see it, if your data set is that large or complex that you need the expertise of a Data Scientist and an advanced at rest analytics platform to undertake the analysis, then this is outside of the MBB's remit as it is a specialist/full time job in its own right. The MBB should however be able to recognise when the analysis is beyond them and as I said earlier, draft the appropriate colleagues with the right skill sets into the project team. Beverly DanielsAnother way to look at this is that "big data" and its analysis is a tool in the six sigma tool box. It can help us get to the causal mechanism (but will never totally replace hands on experiments) and it can provide us with a control mechanism. But it won't replace MSAs and it can't help us develop, validate and implement solutions (except in a very limited and special way) Shahjahan H Hi Beverly, do you think 6 Sigma expert is really required for the Data Science / Big Data Analysis? Statistical knowledge + Tool knowledge (R, SAS, etc) should suffice, what you say? Rip Stauffer Statistical tools are just some of the tools in a BB/MBB's toolbox. Half of Six Sigma (and the harder half, for a lot of people) is the qualitative stuff...process knowledge. Statistical theory and data analysis certainly exist in many places outside of any Six Sigma framework. Six Sigma is just one scientific approach to continual process improvement, that happens to have some statistical tools attached. Vishwadeep Khatri Hi Shahjahan, let us go deeper into this. What is the objective of data science in a company. I have seen several data scientists analysing data and providing inferences that no one utilizes. I have also seen data scientists contributing very well when they work with a clear objective within a lean six sigma project team. Shahjahan H This makes sense - Working with a clear objective (understanding and achieving business/customer objective using DMAIC approach), where 6 Sigma professionals are trained with. Thanks Sushant, Paul, Beverly, Rip and VK for making this discussion more meaningful and got a better understanding of this topic. Beverly Daniels Shahjahan: Certainly there are many uses of Big Data beyond solving Problems and reducing variation within the DMAIC or Six Sigma frame work. In fact much of the early work with Big Data was not Problem Solving but forecasting and modeling of consumer behaviors and bio-health markers etc. ‘Black Belts’ are not typically trained – or skilled - for this application of science and statistics. Forecasting and modeling require a completely different set of statistical tools. We don’t need t-tests, confidence intervals and ANOVA tables for Big Data (we don’t need them for Six Sigma either but that is a different discussion). We need things like Principal Components analysis, multi factor Correlation studies, cluster analysis, etc. Beverly Daniels On the other hand there are few people who are skilled in “data science” or “statistical engineering”. There are many statisticians and many scientists trying to do this work, but what we really need here are a combination of science (or subject matter experts) and statisticians who first understand the difference between statistical analysis on Sparse Data vs. Big Data. (For example, Big data will generate a lot of correlations with p values <.05 simply by chance and thus this traditional approach has little relevance for Big Data). We must remember that “statistics without science is gambling and science without statistics is psychics”.
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