Recent development in the application of data-driven in the decision making process is phenomenal.  More and more companies shift their decision making strategies from only applying business concepts taught in the MBA program to data analytics.  Therefore, demand for a professional with data mining & analytics expertise and experience is parallel with what has happened many years ago in the labor market for MBA graduates.   While this market is getting cool and slowdown in recent years, demand for professionals with data analytics is soaring.

Along with this development, we noticed that almost everyone is trying to re-brand its organization.   In the process some people think that data visualization and data analytics have the same meaning, content or refer to the same thing.  The fact is, they are not.  Data visualization is just one part of many parts of what data analytics professionals can do.  One thing for sure is that data analytics implementation required some sort of statistical analyses, either estimation or hypothesis tests or mathematical programming or simulation.  Visualization of data does not always generate ultimate answers to solve business problems.  It needs to be supported by more rigorous studies.  This is particularly true in a situation where visualization leads to inconclusive strategic changes or recommendations.  On the other hand, hypothesis tests and mathematical programming will generate clearer cut results which can be used to support strategic actions so long the basic assumption of randomness, Central Limit Theorem and others are satisfied.  The ideal skill sets which will lead to a successful professional in the data analytics field consist of knowing the industry, concepts taught in the MBA program, know how to write codes to solve business problems, understand different type of data, having the talent to translate business strategies into computer codes, know the concept of data security, know how to manage data (merger, slice), know how to treat the outliers and to deal with missing observations and the ability to build econometrics models.