  Informatics and technological advancements have triggered generation of huge
volume of data with varied complexity in its management and analysis. Big Data
analytics is the practice of revealing hidden aspects of such data and making
inferences from it. Although storage, retrieval and management of Big Data seem
possible through efficient algorithm and system development, concern about
statistical consistency remains to be addressed in view of its specific
characteristics. Since Big Data does not conform to standard analytics, we need
proper modification of the existing statistical theory and tools. Here we
propose, with illustrations, a general statistical framework and an algorithmic
principle for Big Data analytics that ensure statistical accuracy of the
conclusions. The proposed framework has the potential to push forward
advancement of Big Data analytics in the right direction. The
partition-repetition approach proposed here is broad enough to encompass all
practical data analytic problems.
