Here’s why Databricks had no problem raising $140 million to bring AI to companies

Most of the Artificial Intelligence (AI) implementations you hear about aren’t supported by much substance. Unless we’re talking about how Google serves you ads, Facebook helps you engage on its platforms, Twitter tries to convince you to stay on its site, and so on.

“One percent of companies — Amazon, Facebook, Google, LinkedIn, Microsoft, Twitter, others like these — are getting benefits from AI, the other 99 percent are trying,” says Ali Ghodsi, CEO of San Francisco-based Databricks.

That’s probably a generous estimate because according to job search engine Indeed.com only 0.052 percent of job openings in the United States even contain the term “artificial intelligence.” This isn’t suggesting that AI won’t live up to its hype, it’s just that most enterprises aren’t even sticking their toes into the water yet.

Ghodsi and his team plan to change that. Silicon Valley investors are betting big that they can.

Today Databricks,announced that it has raised $140M in new funding, led by Andreessen Horowitz and with participation from NEA and Battery Ventures. It’s worth noting too that Databricks raised $60M just eight months ago (total funding to date $247M) which is not to suggest that it is burning through cash, but that investors like Databricks’ AI pitch, and want a bigger stake in their play.

Outside of Silicon Valley, New York and Seattle most companies are still struggling to reap rewards from big data, so AI seems elusive to them.

After all, unlike the Googles of this world, the typical enterprise doesn’t have tens of thousands of top-tier software engineers, machine learning experts and vertical domain specialists available at their beck and call, even when there might be important, market-making projects to do. Standing up the infrastructure as well as the collaboration platforms on which analysts and subject matter experts can effectively work together isn’t trivial.

This is where Databricks’ “Unified Analytics Platform” comes in.

It brings data infrastructure and analytics workflows together on one platform that encourages collaboration between expert teams.

How it works:

Consider the case of a hospital that wants to leverage AI to detect tumors in images. In order to train the system, Ghodsi says that it would need medical experts who know about tumors, data scientists who know how to build predictive models and to leverage machine learning software, and data engineers who understand data warehouses and databases and how to deal with the volume, velocity and variability of big data.

Not only that, but you would also need the right technologies in place for data cleaning, ingestion, security, predictions, and monitoring and to stitch them to work together.

Databricks is bundling everything up and offering it to companies as a cloud service platform, and a pretty good one at that. Industry analyst Forrester rated Databricks a “strong performer” in its Forrester Wave report on Insight Platform as a Service. The four year old startup’s competition is Google, IBM, GoodData among others.

Databricks’ Offers the Missing Link

While several AI vendors claim to offer the aforementioned, in one configuration or another, Databricks adds, or will soon add, additional accelerators to the mix; namely specific solutions for a range of industries including Healthcare & Life Sciences, Financial Services, Government, and Media & Entertainment. That’s partly what the funding is for.

Ghodsi says that Databricks will also invest in its Unified Analytics Platform, accelerate its global growth strategy and hire teams dedicated toward helping its customers succeed.

Quick Start Success

All of this needs to be done in short order to be a player in AI, and Databricks needs the capital to compete. Even with this round of investment, most of its competition (Google, IBM, Microsoft, OpenText and others) are better funded.

That being said Databricks has three important differentiators in its pocket: its creators created Spark, so it is safe to say they know it better; Databricks is encouraging customers to bring the business to bring users onto the collaboration platform from the start and to actively help build an AI strategy custom to them; and, perhaps, most importantly, Ghosi can articulate Databricks’ value proposition at the level that each level of stakeholder can understand.

Winning people over is half of the battle when introducing a new technology and Databricks’ formula is solid.