Domino Data Lab Raises $100 Million F Round to Enable the Model-Driven Enterprise

On October 5, Domino Data Lab announced a $100 million F round led by private equity firm Great Hill Partners and joined by existing investors Coatue, Highland Capital, and Sequoia Capital. Domino Data Lab is a company we have covered since the inception of Amalgam Insights in 2017. From the start, it was obvious that Domino Data was designed to support data science teams that sought to manage data science exploration and machine learning outputs with enterprise governance.

This investment is obviously an eye catcher and is in line with other massive rounds that data science and machine learning solutions have been raising, such as DataRobot’s July 2021 G round of $300 million, Dataiku’s August 2021 $400 million round, or Databricks’ gobsmacking August 2021 round of $1.6 billion. In light of these funding rounds, one might be tempted to ask the seemingly absurd question of whether $100 million is enough!

Fortunately, even in these heady economic times, $100 million is still a significant amount of cash to fund growth and the other funding rounds demonstrate that this is a hot market. In addition, Domino Data’s focus on mature data science practices and teams means that the marketing, sales, and product teams can focus on high-value applications for developers and data analysts rather than having to try to be everything for everyone.

In addition, the new lead investor Great Hill Partners is a firm that Amalgam Insights considers “smart money” in that it specializes in investments roughly around this $100 million size with the goal of pushing data-savvy companies beyond the billion dollar valuation. A quick look at Great Hill Partners shows that they have assigned both founder Chris Gaffney and long-time tech executive Derek Schoettle to this investment, both of whom have deep expertise in data and analytics.

With this investment, Amalgam Insights expects that Domino Data will continue to solve a key problem that exists in enterprise machine learning and artificial intelligence: orchestrating and improving models and AI workloads over time. As model creation and hosting have become increasingly simple to initiate, enterprises now face the potential issues of technology debt associated with AI. Effectively, enterprises are replacing “Big Data” issues with “Big Model” issues where the breadth and complexity of models become increasingly difficult to govern and support without oversight and AI strategy. This opportunity cannot be solved through automated model creation or traditional analytic and business intelligence solutions as the combinations of models, workflows, and governance associated with data science require a combination of testing, collaboration, and review that is lacking in standard analytic environments. With mature data science teams now becoming an early majority capability at the enterprise level, Domino Data’s market has now caught up to the product.

Domino Data’s funding announcement also mentioned the launch of a co-selling agreement with NVIDIA. Although this agreement isn’t novel and NVIDIA has a variety of agreements with other software companies, this particular agreement allows NVIDIA and Domino Data to provide both the hardware and software to develop optimized machine learning at scale. Amalgam Insights expects that this agreement will allow enterprises to accelerate their development of machine learning models while providing a management foundation for the ongoing governance and support of data science. Enterprise-grade data science ultimately requires not only the technical capability to deploy a model, but the ability to audit and review models for ongoing improvement or disconnections

From an editorial perspective, it is amazing to see how quickly Domino Data Lab has grown over the past three years. When we first briefed Domino Data in 2017, we frankly stated that the solution was ahead of its time as enterprises typically lacked the formal teamwork and organizational structure to support data science. It wasn’t that businesses shouldn’t have been thinking about data science teams, but rather that IT and analytics teams simply were not keeping up with the state of technology. And in response, Domino Data actually launched a data science framework to define collaborative data science efforts.

Recommendation for Amalgam Insights’ Data and Analytics Community

Funding announcements typically are associated with growth expectations: the bigger the round, the higher the sales and marketing expectations. Domino Data is raising this money now both because it is seen as a market leader in supporting data science and that companies have reached a tipping point in requiring solutions for collaborative and compliant data science management.

Amalgam Insights’ key recommendation based on this funding round as well as recent funding from other vendors is to review current data science capabilities within your organization and ensure that the compliance, governance, and collaborative capabilities are on par with your current analytics, business intelligence, and application development capabilities. The toolkits for collaborative data science have evolved massively over the past couple of years and data science is no longer a task for the “lone-wolf genius” but for an enterprise team expected to provide high-value digital assets. Compare current data science operationalization and management solutions to existing in-house capabilities and conduct a realistic analysis of the time, risk, and total cost of ownership savings associated with each approach. With a mature vendor landscape now in place to help support data science, this is the time for early majority data science adopters to take full advantage of their capabilities over market competitors by creating a mature data science environment and quickly building AI where competitors still depend on manual or static black-box processes.