Late News

The 2020 data and AI landscape

When COVID hit the world a few months ago, an extended period of gloom seemed all but inevitable. Yet many companies in the data ecosystem have not just survived but in fact thrived.

Perhaps most emblematic of this is the blockbuster IPO of data warehouse provider Snowflake that took place a couple of weeks ago and catapulted Snowflake to a $69 billion market cap at the time of writing – the biggest software IPO ever (see the S-1 teardown). And Palantir, an often controversial data analytics platform focused on the financial and government sector, became a public company via direct listing, reaching a market cap of $22 billion at the time of writing (see the S-1 teardown).

Meanwhile, other recently IPO’ed data companies are performing very well in public markets. Datadog, for example, went public almost exactly a year ago (an interesting IPO in many ways, see my blog post here). When I hosted CEO Olivier Pomel at my monthly Data Driven NYC event at the end of January 2020, Datadog was worth $12 billion. A mere eight months later, at the time of writing, its market cap is $31 billion.

Many economic factors are at play, but ultimately financial markets are rewarding an increasingly clear reality long in the making: To succeed, every modern company will need to be not just a software company but also a data company. There is, of course, some overlap between software and data, but data technologies have their own requirements, tools, and expertise. And some data technologies involve an altogether different approach and mindset – machine learning, for all the discussion about commoditization, is still a very technical area where success often comes in the form of 90-95% prediction accuracy, rather than 100%. This has deep implications for