How To Fit Big Data In One Small Page

In the last two years, as Forbes acknowledged last year, various experts have been trying to map and apply category criteria to the field of Big Data. One of the most prominent of these is Mark Turck from FirstMark who has been publishing an acclaimed chart entitled The Big Data Landscape that presents the most important players in all fields of big data from infrastructure to applications and analytics. BIME Analytics was included in the most recent edition of the landscape among the main companies that develop BI platforms - confirming once again the inclusion in the Gartner Cool Vendors in PaaS report for 2014.

Along with publishing version 3.0 of this landscape, Mark expressed for the first time the idea that the one-page chart is no longer enough to cover both all players and all emerging categories. The interesting element to observe here is the fact, even though the market is generally crowded, different sectors are more crowded than others in part due to the continuous appearance of new startups (such as Social Analytics or Data Visualization) while others are clearer because of a powerful process of crystallization regarding the standards of that respective sector (e.g. BI platforms, Cross Infrastructure / Analytics).

Big Data Landscape

Mark Turck also makes the argument that the whole Big Data market is still an early market where funding from VCs will still be available. The size of the market may very well remain the same in terms of the number of companies in general - even though we are close to passing the one-page mark - but, nonetheless, VCs will still pour money in because it will be the number of innovations that will count in order to define this market.

Thus, certain Big Data pioneers are confirming their initial funding rounds through further innovations as well as contributing to the whole market growth - especially on the side of the cloud computing stack of SaaS, IaaS and PaaS providers. More than that, it is relevant for the debate on the diversification vs coagulation of the market to mention that certain categories are nowadays able to also cover the multiple functionalities provided by other sectors such as social analytics, data visualization, big data search, BI platforms being the most exhaustive example here. Furthermore, even though the landscape also defines applications customized by verticals (health, finance, HR, etc.), these kind of platforms are developing in a manner that put BI at the core of business management.

The landscape also sheds light upon the recent decrease of the Big Data hype - as Mark observes, Big Data is becoming less press worthy while corporations start moving projects from experimentation to full production. But the question for now is not whether Big Data is still a thing or not. Big Data is becoming less press worthy because it’s becoming more and more an internal standard. In this case, losing hype means gaining business relevance.

The rise of  analytics is also put under a magnifying glass because - as the UIs and visualization options are getting more complex  - more startups and more VCs orbit around the field in search for the best audience and best strategy to market. The surprising thing is that it seems the companies in this area are seen as trying to separately approach data scientists and / or business users. This may not be the case of BI platforms that enhance business analytics as these platforms are helping initial users go through a fast learning process while using simple UIs and also providing advanced users with customizing capabilities.

Last but not least, it is worth mentioning that this landscape is also diversifying itself regarding the countries where innovations are happening. As Nicolas Celier, a partner at Alven Capital, recently noticed in a Twitter post, French companies such as BIME, mention, focusmatic, Linkurious, Criteo, enovance or Algolia are covering the whole spectrum of Big Data, from infrastructure to social analytics up to open source. This comes in a period when Big Data does not only need a large area of adoption in order to set itself as the standard but also needs a large are of fresh innovation in order to evolve as a technology and for its potential to be fully harnessed.