Arguably, Salesforce.com brought the software-as-a-service (SaaS) concept mainstream. Today, if software isn't available as a service, it's considered old school. But software -- as a service or not -- is just a container. What makes software valuable has always been what it does to data. Now, in the same spirit of service-oriented architectures and SaaS, a new concept is emerging, Data-as-a-Service (DaaS).
DaaS is delivering specific, valuable data on demand. A simple example makes it clearer. Hoover's, a Dunn & Bradstreet company, offer business data. On it's own, it's a hodgepodge of names, titles, addresses and other company information. However, via an integration with Salesforce.com, Hoover's streams data on specific leads directly to sales teams before they contact people to make a sale.
What’s exciting about the DaaS model is that it enables any enterprise with valuable data – not just traditional data providers such as Hoover’s – to create new revenue lines based on data they already own.
DaaS Pricing Models
Based on market research we've conducted, companies are converging around DaaS pricing models based on tiered access to the data. The tiers fall in to two basic buckets:
- Volume-based model
There are two main volume-based pricing approaches: 1) quantity-based pricing and 2) pay per call. (A “call” is a single request/response interaction with the API for data.)
With quantity-based pricing, companies charge based the amount of data a customer or partner needs to access via the API. For instance, a tier might cap at 5,000 API calls per day or 100 API calls per minute. This is the easiest DaaS pricing model to implement, but it does not address overages. Another quantity-based option is the "fire-hose" approach offering unlimited volume with no restriction on number of calls made to the API.
The second option, which is more appropriate for lower-volume use, is pay per call, which involves charging a set amount, such as a few cents, for each call to the API.
- Data type-based model
Another model separates the pricing tiers by data type or attribute. An example is a mapping API that offers the geo-coordinates and zip codes of the neighborhoods in an urban area. Additional attributes could include school or post office locations, which are sold for an additional charge.
Data can be sliced and diced in many ways. The most complex pricing models combine value with volume charges to create finer-grained pricing to better meet both the buyers’ and sellers’ needs.
Here are some examples to help illustrate some DaaS models:
Urban Mapping is a geography data service that allows real-estate companies and others to embed data into their own sites and applications. It offers thousands of geographic data attributes priced and delivered on demand. Examples include population density, crime levels, traffic patterns and nearby public transit stations. In addition to providing its partners with different pricing for different types of data, Urban Mapping also segments tiers based on volume.
Xignite provides financial market data on demand via an API. The company lists banks and investment advisors as clients. It's easy to see how piping stock prices and earnings announcements into financial models is valuable. The company also has a fascinating case study on how McDonalds used its DaaS service to power a website that helped stockholders price their stock after a split between McDonalds and Chipotle. Xignite charges for its data by both volume and by types of content.
These two companies have sophisticated DaaS models that require advanced features in their core API. For example, they include interfaces to enable developers to self-register for API access and access the data they're interested in. The API also tracks and bills for data usage in a granular way.
An example of a less sophisticated DaaS model is Hoover's. It offers a paid API for its business database. But the way it works now, a developer registration form captures the prospect's contact information, and several forms later into the online process, the developer still can’t access the API. Instead, interested developers must wait for a sales person to call back and kick off an old-fashioned sales cycle. It's not a very scalable or efficient process, and it’s a known secret that many developers would rather avoid sales people.
Data-as-a-service represents a new market whose time has come. The technology exists already, and DaaS-based businesses are emerging quickly. Businesses across sectors are beginning to see their data not only as fundamentally valuable, but economically viable to distribute. The scale offered by an API strategy allows businesses to unlock the value of that data for their own revenue growth and their customers’ benefit.