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Making sense of Hype Cycle for Big Data

A few days back Gartner published Hype Cycle for Big Data,2012  and Hype Cycle for Cloud Computing,2012. We reviewed them and compared it with 2011 report and here are a few key new take-away from the 2012 Gartner reports.

The good news which had been expected by research followers is that Big Data has moved from ‘On the Rise’ to ‘At the Peak’.

However, the report goes on to add that Big Data will probably soon move into the Trough of Disillusionment in 2012 where it will mature in terms of offerings, solutions and technology maturity.  This was stated in its Hype Cycle for Cloud Computing, 2011 report also but seems Gartner is now more upbeat about technology players because it goes on to add “However, big data should spend very little time in the trough”.

The Hype Cycle for Cloud Computing, 2012 report also states that there will be attempts to combine MapReduce with Graph as well as natural-language processing and text analytics. Refer one of our articles on Facebook’s one such attempt among its many successful use cases.
The slight worrisome statement from the report is that “big data assets, such as images, video, sound and even three-dimensional object modeling, will also drive big data into the trough”. We know a lot of research is already happening on this and have in this site tried to cover one such architectural solution for Hadoop usage in video, sound search and modeling.

As in many other technology product life cycles, Gartner predicts that specialized technologies could become mainstream while newer technologies will emerge as the next major big data issue surfaces up.

In the Hype Cycle for Big Data, 2012, Gartner attributes one of the major reasons for increased focus on Big Data to “increased availability of scalable, elastic resources in the cloud have allowed organizations to begin big data projects without investing in infrastructure.”

It further details out 3 categories:
Entries that describe enabling technologies for big data
-          among the others, the one to note is column-store DBMS.
o        Refer our post on one such solution which uses column-store DBMS for higher performance

Entries that describe typical use cases for big data
-          among the other, the one catching attention is telematics

Entries that describe new information types, sources and roles
-          on expected lines, we know Data Scientist should feature here

For complete text, you may refer the 2012 reports at the links given above.  

Going a step further, we have applied Map Reduce on Hype Cycle for Big Data, 2012 report. We took all statements where Sample Vendors were suggested by Gartner in the technologies shown in Hype Cycle image at top. Our algorithm returned a count on how many times a vendor has been suggested in the report. The image below shows a graph generated by the data from MapReduce algorithm to arrive at a vendor mention count. (click on image to view full image).

: View full image on Pinterest


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