On June 3rd, Alation announced a $110 million D round led by Riverwood Capital with participation from new investors Sanabil Investments and Snowflake Ventures. Current investors Costanoa Ventures, Dell Technologies Capital, Icon Ventures, Salesforce Ventures, Sapphire Ventures, and Union Grove Partners also contributed to the round. With this round, Alation has raised a total of $217 million and is valued at $1.2 billion.
One of the first things that stands out with this investor list is how Alation serves as an example of “investipartnering” where business partners also become limited equity partners. With Snowflake, Salesforce, and Dell all on the cap table, Alation stands out as being a strategic partner for some of the biggest cloud players on the planet.
Here’s why this investment makes sense in today’s data environment.
One of the biggest challenges for data in 2021 is effectively governing & defining data across a wide variety of sources. Alation has been both a pioneer and now a consistent market-leading data catalog both from a revenue and functionality perspective.
Yet, there is still a massive greenfield opportunity to rationalize taxonomies, naming conventions, integrations, and data-centric decisioning processes within the larger enterprise data ecosystem. These data challenges were already challenging enough for analytics, where businesses have had data warehouse, master data, and data integration tech for decades. But now this data also has to be prepped for machine learning & AI, where these structures are less useful. One of the reasons that a variety of industry estimates state that data scientists spend as much as 80% of their time cleansing data is because data scientists have either eschewed traditional enterprise data structures or are simply unaware of the analytic data ecosystem that has been built in enterprises over the past several decades as they seek to tackle problems.
In an agile, “Post-Big Data” data world, the true hub of data intelligence is either at the catalog or datalake level, depending on how data is used and organized. In today’s data world, the data warehouse is an important piece of core infrastructure for enterprise data, but is not typically agile enough to support the rapid data selection, augmentation, transformation, and analysis associated with both self-service analytics. and machine learning efforts. In this modern data context, Alation is a vital player in advancing the cause of referencing, contextualizing, and linking datasets together rapidly.
And the investment by Snowflake Ventures reflects that Snowflake knows they need more control over less structured data. Snowflake is under pressure to justify its massive valuation as a cloud data leader and now has to meet the growth expectations of being worth well over $50 billion and having had a peak valuation of over $125 billion in its brief tenure as a public company. Alation will be a vital part of Snowflake’s story in providing a more agile environment for the entirety of enterprise data as Snowflake moves closer to a variety of datalake capabilities that allow for more flexible data transfer.
I’ve covered Alation since its Series A in 2015 led by Costanoa Ventures, which has now established itself as a premier early stage investor in data-driven startups, back when I was the Chief Research Officer at Blue Hill Research. Their focus on data navigation at a time when Hadoop was seen as The Big Data Answer ended up being prescient and Alation’s value is now established with over 250 enterprise clients.
But there is a larger opportunity. As Alation has expanded from a data catalog solution to a broader data discovery, context, governance, and collaboration solution and as the challenges of data and metadata management move downmarket, Alation’s capabilities are increasingly aligned with fundamental market needs to categorize and share data effectively.
To become a global solution, Alation needs to get into thousands of organizations, a goal that I think is now realistic with this latest round of funding that both boosts sales in the short term and sets a path to ongoing scalable growth.
Recommendation for the Data Management Community
The key takeaway for the data community is that legacy data management tools typically lack the speed and functionality necessary to identify, classify, and organize new data for new analytics, machine learning, and AI use cases. This includes everything from unstructured documents to relevant binary files to time-series, graph, and geographic data. This problem has driven both the commercial and investor interest in Alation and this problem is moving downmarket as more organizations seek to start building scalable and repeatable machine learning, data science, and analytic application development environments. Organizations that are not actively planning to improve their metadata and data collaboration efforts will find themselves fundamentally hampered in trying to make the leap from BI to AI and in keeping up with the new business world of augmented, automated, process mapped, natural language-based, and iterative feedback-driven transformation. Behind all the buzzwords, companies must first understand their existing data and contextualize their new data.