Red Hot: The 2021 Machine Learning, AI and Data (MAD) Landscape

One story has been the maturation of the ecosystem, with market leaders reaching large scale and ramping up their ambitions for global market domination, in particular through increasingly broad product offerings. Some of those companies, such as Snowflake, have been thriving in public markets (see our MAD Public Company Index), and a number of others (Databricks, Dataiku, Datarobot, etc.) have raised very large (or in the case of Databricks, gigantic) rounds at multi-billion valuations and are knocking on the IPO door (see our Emerging MAD company Index – both indexes will be updated soon).

But at the other end of the spectrum, this year has also seen the rapid emergence of a whole new generation of data and ML startups. Whether they were founded a few years or a few months ago, many experienced a growth spurt in the last year or so. As we will discuss, part of it is due to a rabid VC funding environment and part of it, more fundamentally, is due to inflection points in the market.

In the last year, there’s been less headline-grabbing discussion of futuristic applications of AI (self-driving vehicle, etc.), and a bit less AI hype as a result. Regardless, data and ML/AI-driven application companies have continued to thrive, particularly those focused on enterprise use cases. Meanwhile, a lot of the action has been happening behind the scenes on the data and ML infrastructure side, with entire new categories (data observability, reverse ETL, metrics stores, etc.) appearing and/or drastically accelerating.