Data as a Service

A business model where the core product is access to a proprietary dataset — as distinct from SaaS (software tools) or compute services (processing). DaaS companies look, in Auren Hoffman’s framing, “like the ugly child of a SaaS company (like Salesforce) and a compute service (like AWS).”

The key structural dynamics Hoffman identifies (Jun 2019):

• Winner-takes-most. A dominant data company can lower prices to gain market share, invest those resources in improving the data, which improves market share further — a data flywheel that is hard to interrupt once established.
• Veracity is the product. Unlike software features, the entire value of data is truth. Hoffman’s framework: “truth vs. religion” — DaaS is about assembling verifiable facts, not narrative.
• Interoperability multiplies value. “Your data will be much more valuable if you enable it to be joined with other datasets (even if you make no money off the other datasets).” The network effects come from integration, not isolation.
• Tailwinds: powerful analytics tools (Snowflake, Looker, Tableau at the time), more data scientists, declining compute costs.

DaaS is often misunderstood because its success metrics differ from SaaS: revenue expansion mechanics, churn dynamics, and the role of data freshness require different diligence than a pure software business.

Where this appears

• 1- Data Businesses Are Generally… (tweet)