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?
See: http://bit.ly/1ow9EVF
Comment
First, the majority of the companies on the Matt Turck's diagram should have been marked as "Exited - acquisition or IPO", He only marked a fraction of these.
Second, each small rectangle on the diagram is a separate market. Big Data marketplace is an ecosystem of numerous different product/service markets. Product/service markets can grow, shrink, or disappear, and they can impact each other through their impact on business models and business needs. But they cannot consolidate across vertical boundaries.
Would attribution analytics vendors be to niche
Posted 12 April 2021
© 2021 TechTarget, Inc.
Powered by
Badges | Report an Issue | Privacy Policy | Terms of Service
Most Popular Content on DSC
To not miss this type of content in the future, subscribe to our newsletter.
Other popular resources
Archives: 2008-2014 | 2015-2016 | 2017-2019 | Book 1 | Book 2 | More
Most popular articles
You need to be a member of Data Science Central to add comments!
Join Data Science Central