Big Data – Difference between analytic and implication

Link to my previous page regarding Big Data density model is here: Big Data – What is it technically?

There are numerous Big Data use case now. Regretfully, majority of them a mere marketing stunt. So what is the real big data analytic? And what are big data implication, the other kind of “analytic”?

Based on the data density model I previously proposed, big data analytic is a scientific process producing not obvious relationship between two or more data sets by processing and classifying cross plane relationship. There are several keys here. First, the process has to be scientific, so pure guessing or human observation does not count. Second, the final product can not be obvious. Third, the scientific process is applied on cross-plane relationship that tides at least two individual data sets.

Similarly, big data implication is a process producing relationship through direct observation or simple statistical analytic. Basically, anything not so much a big data analytic process is considered big data implication. As the name suggested, many of such implicated relationships are either obvious for human eye or can be obtained through very simple statistical analysis. Things like website click conversion rate is one of these.

Big data analytic is what we are focusing on in big data discussions. What we are after are things not so obvious. Implication is a good starting point, but definitely not our end game.

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