L4LOD (Licenses for Linked Open Data) is a lightweight vocabulary for expressing the licensing terms in the Web of Data. The vocabulary is not intended to propose yet another license, but it is intended to provide the basic means to define in a machine-readable format, i.e., RDF, the existing licensing terms. The vocabulary does not provide an exhaustive set of properties for licenses definition. Implementations are free to extend L4LOD to add further elements. ·
The Library of Congress has launched the Bibliographic Framework Transition Initiative to better accommodate future needs of the library community. A major focus of the initiative will be to determine a transition path for the MARC 21 exchange format to more Web based, Linked Data standards. Zepheira and The Library of Congress are working together to develop a Linked Data model, vocabulary and enabling tools / services for supporting this Initiative. ·
January 17, 2010 By Scott Brinker 4 Comments
The 8th linked data business model
In response to my post on linked data business models, Leigh Dodds at Talis wrote a terrific piece with his thoughts on the business of linked data. Leigh presents a number of great ideas that I think really carry the conversation forward.
One of his points is that I overlooked an important model, what he calls the “sponsorship model.” Under this model, a government entity or a non-profit organization has a funded mandate to deliver certain data to the public or their targeted constituency. I’d humbly suggest calling it the subsidized model though, to avoid confusion, because sponsorship is often associated with advertising and branding — very different business models. ·
I’ve organized these by how revenue is generated, from direct money-for-data to indirect branding programs.
Within each of these revenue models, there’s also a secondary dimension of how the data is delivered, whether in raw form for others to leverage in their own applications or embedded into a pre-packaged application provided directly to end-users.
1. Subscription model. Some data will be valuable enough that you can charge people a subscription to access it. This model has been around for a while, but it will gain new life as linked data standards make it easier for people to consume and mash-up data in novel applications.
2. Advertising model. Advertising: the second oldest profession. Data-driven applications will have plenty of opportunity for contextual ads and sponsorships. One interesting twist will be advertisers who pay to include information in raw data feeds, data-layer ads if you will.
3. Authority model. If anyone can publish data on the web, how will you know what data is good? That problem will be an opportunity for third-party “authorities” to validate data — or do official reviews and certifications that are published as data — and charge for participation. Compliance services are related to this.
4. Affiliate model. Affiliate marketing programs generate over $6 billion/year in commissions and are a major source of transactions and leads for merchants such as Amazon.com. Embedding affiliate links in data, so that they are activated when surfaced into end-user applications, are a natural extension of this existing model.
5. Value-Add model. Useful data can be bundled with other services to make the overall solution more valuable. For example, think of the benchmarking data now included with Google Analytics. Access to data can also be offered earlier in the sales funnel, as a lead generation incentive.
6. Traffic model. As with Google Rich Snippets, data can be used to boost the visibility and ranking of sites in major and vertical search engines. This is data-enhanced search engine optimization (SEO++) to increase traffic. Nickname: the “data for nothing and links for free” model (apologies to Mark Knopfler).
7. Branding model. As Josh Jones-Dilworth said, “Data shapes conversations and markets.” Data branding can use data — and the vocabularies that define and structure data — to position and promote a company’s worldview and differentiation strategy.
Of course, there will be hybrid models that combine several of these approaches.
Particularly in the early days, most organizations will benefit from experimenting with linked data for traffic, branding, and a little value add. Their own value will be learning as much as anything. As the data web matures, and they become more experienced, they may embrace more direct revenue models.
But don’t underestimate the importance of data branding. When it comes to establishing industry standard vocabularies and ontologies, there is a definite first-mover advantage.
For the entrepreneurs in this space, however, everything is fair game. ·