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Introduction: Deriving meaningful information out of heap of data is the minimal requirement for any establishment today for its survival & sustenance. There are many terminologies and buzz words related to this area that blurs the meaning leaving people confused, such as Bigdata, Data Ware house (DWH), BI analytics, AI (Artificial Intelligence), Data Science, Machine Learning, Advance Analytics, Deep Learning, Cognitive, Predictive modeling etc. to name a few. I have seen institutions would have team using machine learning to build classifiers but they call the same as AI team, Business Analysts would run some diagnostic analysis using Tableau but are called Data Scientists, sometimes we write conventional code or may be use RPA tool such as blue prism in automating certain portion of business process (e.g. take data from one app, paste to a file , format the same before sending as expense report) and we might unintentionally call that as AI eco-system and so on. College graduates, job seekers (fresh or lateral), business executives and technologists must make good effort to understand the concept behind various data management subject area (AI, ML, DS, Deep Learning, Cognitive Computing, Statistics, BI, DWH etc.), associated roles (such as data engineers, business analysts, data scientists, ML engineer, Data Modelers, data administrators etc.) and subsequently plan to learn and apply else Industries might lose revenue, CoE & practices blur and frustration creep in between expectation vs reality. We, however, haven’t gone that far in data maturity area (Exploration, diagnostic, prediction & prescriptive), hence important to clarify and understand various data subject area before its late. This article is an attempt to put clarity around these data subject area involving AI, Data Science and related terms to help graduates, data practitioners, business executives and others to develop career, establish practices, community and competency in Data Science area. For further detail, click here