A point that a lot of nominal Big Data startups miss is that genuinely large-scale data management and analytics are not driven by visualizations at all nor fit in a web-driven SaaS-like environment. The purpose is to answer a complex question from unimaginably large volumes of data, not to draw charts and graphs. It is often too I/O intensive for virtualized clouds and the visualization component is almost superfluous to the purpose. Most of the problems that need to be solved in Big Data are low level, down at the computer science and infrastructure level. Many of the use cases are intrinsically poorly suited for web-based SaaS type offering.

To make matters worse, many high-value Big Data analytical problems are (literally) not meaningfully visualizable except for marketing purposes. It is rather tricky to visualize an analytic product when there are a hundred critical values that need to be rendered in some fashion for every pixel your monitor can display. A lot of high-value analytics have this characteristic but most of the nominal Big Data visualization tools ignore this case even though it is arguably the most important one.

Consequently, while labeling your startup "Big Data" is trendy and fashionable, there are very few genuine Big Data startups. Adding value in this market requires a combination of serious theoretical computer science chops plus very creative interface design. Few startups are actually addressing the needs of this market and are instead assuming the market wants the web app they have the skills to produce.