Linking Rights to Aggregations of Data (Part 2)

In my background research for today's entry I discovered that the smart people at Talis, especially Ian Davis, have been working the problem I outlined in Linking Rights to Aggregations of Data (Part 1). Specifically, back in July 2009 Ian proposed WAIVER: A vocabulary for waivers of rights. In Ian's words,

(The WAIVER) vocabulary defines properties for use when describing waivers of rights over data and content. A waiver is the voluntary relinquishment or surrender of some known right or privilege. This vocabulary is designed for use with the Open Data Commons Public Domain Dedication and License and with the Creative Commons CC-0 waiver

In his July 2009 post Linked Data and the Public Domain Ian argues for providers to unambiguously declare their datasets public domain and explains how to use the WAIVER vocabulary to do this, in the context of a voID description of a dataset. (See also this email discussion thread involving several of the thought leaders in this area on this issue) Ian provides the following example, which I repeat here to illustrate (a) use of voID to describe a dataset named "myDataset," (b) use of the wv:waiver property to link the dataset to the Open Data Commons PDDL waiver, (c) use of the wv:declaration property to include a human-readable declaration of the waiver, and (d) use of the wv:norms property to link the dataset to the community norms he suggests, ODC Attribution and Share-alike.

<?xml version="1.0"?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
  xmlns:dc="http://purl.org/dc/terms/"
  xmlns:wv="http://vocab.org/waiver/terms/"
  xmlns:void="http://rdfs.org/ns/void#">
  <void:Dataset rdf:about="http://myOrganisation.org/myDataset">
    <dc:title>myDataset</dc:title>
    <wv:waiver rdf:resource="http://www.opendatacommons.org/odc-public-domain-dedication-and-licence/"/>
    <wv:norms rdf:resource="http://www.opendatacommons.org/norms/odc-by-sa/" />
    <wv:declaration>
      To the extent possible under law, myOrganisation
      has waived all copyright and related or neighboring rights to 
      myDataset
    </wv:declaration>
  </void:Dataset>
</rdf:RDF>

WAIVER and OAI-ORE: As I proposed in Part 1, we should be able to combine the voID and OAI-ORE approaches. The only conceptual difference is by OAI-ORE guidelines the RDF file shown above would be treated as the resource map for the aggregation URI (in this example, "http://myOrganisation.org/myDataset") and would have a URI unto itself (perhaps "http://myOrganisation.org/myDataset.rdf").

What about other rights? It is critically important for the reader to understand that Ian's example (repeated above) only shows how to declare a waiver of rights, which by its nature is intended to promote the reuse of data based on open principles. Today, this is mostly what the linked data world has focused on, but as the NYTimes open data experiment is showing us, providers will want to assert rights where they can. In a future post I'll applied what we've learned so far, to consider approaches for declaring dataset rights in legal regimes where this is actually possible.

Long Tails and "Scaling Down" Linked Data Services

Chris Anderson's newest book FREE: The Future of a Radical Price received some attention this summer, but I've actually been meditating on principles he laid out three years ago in his blog post, Scaling up is good. Scaling down is even better. In that post he marveled at Google et.al.'s ability to scale down, to run themselves efficiently enough to serve users who generate no revenue at all. Anderson's principles are guidance on approaches to conducting business such that even if only a tiny percentage of ones visitors "convert" into paying customers, by ensuring this small percentage is of a very large number one can still achieve big-time profitability.

My goal with this post is to consider how these ideas might be applied to the domain of Linked Data, and specifically how they pertain to the provision of unique data that adds real value to the greater "Web of Data."

In his blog Anderson gives us four keys to scaling down: Self-service, "Freemium" services, No-frills products and Crowdsourcing...

1. Self-service: give customers all the tools they need to manage their own accounts. It's cheap, convenient, and they'll thank you for it. Control is power, and the person who wants the work done is the one most motivated in seeing that it's done properly.

"Self-service" applies to linked data services in oh-so-many ways! Self- service in this case is not as much about support (see "Crowdsourcing," below) as it is about eliminating any and all intervention customers might need to customize or specialize how services perform for them. In principle, the goal should be to provide users with a flexible API and let them figure it out, with the support of their peers. Ensure that everything is doable from their side, and step out of the way.

The (negative) corollary is this: if you "baby sit" your customers by providing specialized services that require maintenance, then you own it and must eat the cost. By making the specializations a user-side function, then they own it. But they won't be alone; they'll have the support of their community!

2. "Freemium" services: As VC Fred Wilson puts it, "give your service away for free, possibly ad supported but maybe not, acquire a lot of customers very efficiently through word of mouth, referral networks, organic search marketing, etc, then offer premium priced value added services or an enhanced version of your service to your customer base." Free scales down very nicely indeed.

There are any number of ways providers might apply this concept to the linked data world:

Free Access	Premium Access
Restricted vocabulary of assertions	Full access, all assertions
Limited query rate	Unlimited query rate
Limited query extent	Unlimited query extent
Limited data	Unlimited data size
Read-only	Term upload capability
Narrow reuse rights	Broad reuse rights
Community support	Private/ dedicated support
...	...

3. No-frills products: Some may come for the low cost, others for the simplicity. But increasingly consumers are sophisticated enough to know that they don't need, or want to pay for premium brands and unnecessary features. It's classic market segmentation, with most of the growth coming at the bottom.

In the linked data world, achieving "no frills" would seem easy because by definition it is only about the data! For linked data a "frill" is added data complexity that serves no purpose or detracts from the utility of the service. Avoid any temptation to "add value" on behalf of customers, such as merging your core graph with others in an attempt to "make it easy" for them. Providers should also avoid "pruning" graphs, except in the case of automated filtering in order to differentiate between Freemium and Premium services.

4. Crowdsourcing: From Amazon reviews to eBay listings, letting the customers do the work of building the service is the best way to expand a company far beyond what employees could do on their own.

By now it is not only obvious, but imperative that providers should develop communities around their services. Usually communities are about evangelism, and this is certainly true for linked data provides, but increasingly service provides realize well-groomed communities can radically reduce their service costs.

Linked data providers should commit themselves to a minimum of direct support and invest in fostering an active community around their service. Every provider should have a means for members of their community to support each other. Every provider should leverage this community to demonstrate to potential adopters the richness of the support and the inherent value of their dataset.

Finally: In a thought-provoking post Linked Data and the Enterprise: A Two-way Street Paul Miller reminds the skeptical enterprise community that they, not merely their user community, will ultimately benefit from the widespread use of their data, and when developing their linked data strategy they should consider how they can "enhance" the value of the Web of Data, for paying and non-paying users alike:

...[A] viable business model for the data-curating Enterprise might be to expose timely and accurate enrichments to the Linked Data ecosystem; enrichments that customers might pay a premium to access more quickly or in more convenient forms than are available for free...

I've purposely avoiding considering the legal and social issues associated with publishing certain kinds of enterprise data as linked data (see also this), which I'll address in a future post...