Data as a Service Overview and Landscape
This is the second piece in a series covering Data as a Service (DaaS). The previous piece can be read here
AWS helped normalize the idea of infrastructure as a service. Salesforce did the same for software as a service. What we are witnessing now is another line of business: — data as a service (DaaS).
DaaS is an approach to information architecture that aims to transform raw data into usable insights that are available on demand. As Auren Hoffman said “In the end, great data companies look like the ugly child of a SaaS company (like Salesforce), a compute service (like AWS), and an API service (Twilo)”
In the past decade, technology-enabled companies could manage their internal data easily. That is why we saw the rise of cloud services (GCP, Azure, and AWS), analytical services (Tableau), database companies (MongoDB), information graph providers (Liveramp), and many more. With these foundational services available, the next decade will center around private and secure data collaborations.
In this decade, companies will be able to buy, sell, and use sensitive data easily. We will see companies become data leaders in various industries and effectively leverage their data assets. Tools that facilitate data exchange and collaboration, enrich first-party data, protect sensitive data (enabling its use for AI/ML) will increase in numbers and become increasingly central.
Below is an industry map of companies that are active in the DaaS category as well as are enablers for DaaS. I would love to hear from you if you feel that I’ve missed anything.


I see activity in DaaS continuing to accelerate. The big trend I see right now:
- Use of de-identification technologies to enable use of more sensitive data
- Growth of data exchanges that curate data assets for buying and selling
- Rise of data enrichment companies enabling companies to monetize data
- The emergence of traditional companies in the Data as a Service category
In the coming years
- I see the productization of cryptography techniques enabling secure and private use of data, leading to more sensitive data being available to data scientists, and continuing to accelerate machine intelligence and bringing valuable products and applications to consumers.
- The building of new business models to access, buy, and sell data
I’d love to hear your thoughts and answer any questions — please feel free to comment on them below!
Special thanks to Laith Sarhan, Roberto Cervantes, Yawer Ali, Zain Khan, and Nushaine Ferdinand for their help reviewing and editing the article!