DaaS covers the entire data category landscape, from weather data to web data. There’s a DaaS provider for most of the data categories listed on Datarade Marketplace. In general, data that can be delivered via API can be treated and sold as DaaS. For example, geospatial data providers typically deliver location and mobility data via API. That’s because temporality and recency is important for many use cases requiring geospatial intelligence. If you need to understand live mobility trends, it’s no use buying a historical footfall database (for example as an S3 bucket batch file). Real-time delivery via API is crucial.
For this reason, some data categories don’t really lend themselves to the DaaS model. For such categories, it doesn’t matter if the data is delivered on-demand or in real-time. In many instances, it’s impossible to delivery data at such notice.
For example, census data is subject to the government’s collection and distribution timeframe. And it certainly isn’t delivered in real-time. It could also be that the data product is explicitly built to only offer historical data. Some transaction data providers operate like this: they compile databases of consumer purchase history using panels covering an elapsed time frame.
Essentially, the same rules which apply to Big Data in any format also apply to DaaS. One of these key rules? Data is only as valuable as the use case it’s attached to. A crucial part of data-as-a-service is the provider’s capability to serve your specific use case. Whatever your challenge, the data should provide the insights you need to solve it. In this sense, DaaS is the same as any other service or product: its fundamental purpose is to address the customer’s pain points.
Apropos products, let’s look closely into exactly how DaaS differs to other forms of commercial data & jump our fifth and final chapter: Data-as-a-Service vs Data-as-a-Product.