Scott Brinker recently published a great blog post covering 7 business models for Linked Data. The post is well worth a read and reviews the potential for both direct and indirect revenue generation from a range of different business models. I’ve been thinking about these same issues myself recently so I’m pleased to see that others are doing similar analysis. Scott’s conclusion that, currently, Linked Data is more likely to drive indirection revenue is sound, and reflects where we are with the deployment of the technology.
The time is ripe though for organizations to begin exploring direct revenue generation models and it’s there that I wanted to add some thoughts and commentary to Scott’s posting.
The traffic model, with its indirect revenue generation by driving traffic to existing content and services, is well understood. The same model has been used to encourage organizations to open up Web APIs, so its natural to consider this for Linked Data also.
Because it is tried and tested it’s currently one of the strongest arguments for driving adoption of Linked Data, so I’d put this right at the top of the list. The feedback loop that is in place now with search engines makes that traffic generation a reality.
Scott mentions adverts as a possible revenue stream and raises the possibility of “data-layer ads”, by which I understand him to mean advertising included in the Linked Data itself. While I agree that an advertising model is a potential revenue stream, I don’t see that “data-layer ads” are really viable or actually useful in practice.
Adverts incorporated into raw data will be too easily stripped out or ignored by applications; by definition the adverts will be easily identifiable. RSS advertising doesn’t seem to have really taken off (I certainly never see them anyway) and I think this is for similar reasons: if the adverts are easily identifiable, then they can be stripped. And if they’re included in content or data values, then this causes problems for further machine-processing of the data and annoyances for end users.
Of course a business could enforce that users of its Linked Data should display ads through its terms and conditions, e.g. requiring data-layer ads to be displayed in some form to users of an application. In practice this can get problematic, especially if there’s not an obvious way to surface the ads to end users. But I think its also problematic as unlike a Web API where I sign up to gain access, for an arbitrary Linked Data site, there is no prior agreement required. My crawler or browser might fetch data without any knowledge of what those terms and conditions might be.
Adverts embedded into data is are not a useful way to distribute them to end-users. In an environment where adverts are increasingly profiled by a range of geographic, demographic or behavioural factors, incorporating blanket ads into data feeds loses all of that targetting capability. It also potentially loses the feedback, e.g. on click-throughs or impressions, that are useful for gauging the success of a campaign.
In my view advertising as a model to support Linked Data publishing is more likely to echo that used by the Guardian as part of its Open Platform terms and conditions (See Section 8, Advertising and Commercial Usage). The terms require users of the content to display ads from Guardian’s advertising network on its website. This avoids the need to include adverts in the data layer and supports a conventional model for delivering ads, making it play well with current advertising platforms and targeting options.
As Brinker notes, subscription models for data, content and services have been around for some time. The interesting thing is to see how these models have been evolving of late due to pressures in various industries, and how these intersect with the open data movement. For Linked Data to be most useful some of its needs to be free: you need to make at least a bare minimum of data freely available, e.g. to identify objects of interest, to enable annotation and linking, etc. In my opinion a freemium model is the core of any subscription model for Linked Data.
Having previously worked in the academic publishing industry which is very heavily driven by subscription revenues, I’ve noticed a number of models that have come to the fore there, most recently driven by the Open Access movement. I think many of these are transferable to other contexts. So while the particulars will vary in different industries, the means of slicing up data into subscription packages are likely to be repeatable.
All of the following assume that some basic element of the Linked Data is free, but that one is paying for:
- Full Access — Pay for access to detailed, denser data. The value-added data might include richer links to other datasets, more content, etc
- Timely Access — Pay for access to the most recent, or more current version of the data. This leaves the bulk of the data open but delivers a commercial advantage to subscribers. As data gets older, it automatically becomes free
- Archival Access — Putting archives of content, or large archival datasets on-line can be expensive in terms of data conversion, digitization, and service provision. So deep archives of data might only be available to subscribers. Commercial advantage derives from having more data to analyse and explore.
- Block Access — paying for access to a dataset based on time, e.g. “for the next 24 hours”; or based on the number, frequency of accesses; or the number of concurrent accesses.
- Convenient Access — paying for access to the data through a specific mechanism. This might seem at odds with Linked Data, but its reasonable to assume that some organizations might want data feeds or dumps rather than on-line only access. This might come at a premium.
These variants can combined and might also be separated out into personal (non-commercial) and commercial subscription packages.
It’s interesting to see how some of these (Timely Access, Convenient Access) are already in use in projects like Musicbrainz that blend Open Data with commercial models.
One model that Scott Brinker doesn’t mention in his posting is Sponsorship. An organization might be funded to publish Linked Data, e.g. for the public good. The organization itself might be a charity and funded by donations.
It’s arguable that this might be more about cost recovery for service provision rather than a true business model, but I think its worth considering. Some of the open government data publishing efforts and possibly even the Linked Data from the BBC, could be seen as falling into this category.
It’s probably most viable for public sector, cultural heritage and similar organizations.
What needs to happen to explore these different models? Is it just a matter of individual organizations experimenting to see what works and what doesn’t?
I think that is largely the case, and we’ll definitely be seeing that process begin to happen in earnest in 2010; a process that we’ll be supporting and enabling with the Talis Platform.
From a technical perspective I’m interested to see how well protocols like OAuth and FOAF+SSL can be deployed to mediate access to licensed Linked Data.