[repost from larsleckie.blogspot.com]
I have been preoccupied lately with a software model that is a cousin of Software as a Service (SaaS) - one that I will call Data as a Service (DaaS). Much like SaaS it is more of an evolution of an existing model than something completely different - it just seems to me that the new generation of data delivered as a hosted service are breaking new ground in terms of access to data and analytics. I think more interesting companies will be built around this model.
Definition - DaaS
A software as a service or web service offering that provides customers with access and analytics around a set of proprietary set of aggregated data. The most interesting of DaaS offerings are ones which collect this data from individuals or companies by providing immediate value back for these efforts, but that the aggregated data is able to be resold to a different set of constituents.
Example - Salary.com
Salary.com collects data by offering individuals the ability to benchmark their compensation levels against others by sharing data into the system. This data is then aggregated and anonomized to be resold to companies (usually HR managers) for hiring and compensation related usage. Salary.com went public in 2006 and has a market cap of 100M.
Example - Dun and Bradstreet
Dunn and Bradstreet (D&B) is a 165 year old business that provides credit and commercial information. I have used their service in the past to research the credit history of customers and partners before extending them credit. D&B has a team of people who call companies to gather the data and then they resell it back to other companies - both on individual companies and for industry segments. D&B also sells this data for several other uses, for example, to marketing professionals to cleanse contact lists.
Why the DaaS model works
- Virtuous cycle on data - as the DaaS company acrues more data, it can provide increasing more useful data back to both the contributors and the purchasers. The data becomes a competitive advantage that an upstart would have trouble replicating. This is especially true if the data needs scale across different geographies.
- Analytics and actionable business drivers - the data is the basic asset, but it is significantly enhanced by wrapping analytics into the offering. Some DaaS companies pull their data from publically available sources but an idividual user would have trouble pulling the data, navigating the data and building analytics to make it useful. A DaaS company can make this investment as they can sell the data many times over.
Unfortunately I think that the acronym is still up for grabs with a few alternative software meanings:
Development as a Service - Salesforce
Datacenter as a Service - Cloud Computing
Database as a Service - LongJump
Department of Aging and Adult Services - US Government
Data as a Service - StrikeIron (this one is closest to my definition, but is actually DSaaS - Data Service as a Service)
The message for me is that all companies, SaaS or otherwise, should look at what data assets they have (or could easily have) and consider a DaaS offering. If it can provide useful insights - it will be well worth the development costs.