The "big data" technology vendor market is ripe for consolidation. The myriad of vendors and technologies is causing market confusion. Matt Turck created a nice visualization entitled "Big Data Landscape v. 3.0" (see picture above) showing the tangled mess. Further, the vague definition of "big data" and how to measure return on investment (ROI) is creating skepticism about the true value of this new data technology.
While many vendors received ample venture capital, it is unknown how many are profitable or have a realistic chance of becoming profitable in the future. Also unknown are sales growth rates and whether customers actually attain or perceive strong ROI. If customers perceive they have wasted millions of dollars on technology with little demonstrable value, most of these vendors and technologies are doomed.
Considering we are in the pre-industrial age of sophisticated data management and analytical technology, it is reasonable to surmise that only a few of the vendors will become profitable on their own and most will either be acquired or die a slow death. Predicting winners at this time is difficult if not impossible considering a high causal density environment with numerous variables.
The data science question is: how can we collect and analyze data to measure vendor technology effectiveness and true ROI for customers?