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.
11 thoughts on “Thoughts on Linked Data Business Models”
I’d like to make two observations:
1. Reuse of data and search engines
There is a danger that Google will penalize those who reproduce data from other sites. Although their algo might be something of a blunt instrument at the moment, everything seems shrouded in mystery about what they deem “copied content” – expect this to become more sophisticated.
e.g. Your site about mytown legally reuses data from dbpedia. Which is the most believable? Which should be ranked highest?
Turning this idea around there will also be a need for data to be corrected and sent back to the data supplier.
I can imagine this working in much the same vein as me selling electricity back to the grid.
Is that another model? Might you be at once a supplier and collector of data, or some kind of middle man?
Thanks for the kind comments on my post and taking the discussion further. These are great ideas.
I completely overlooked “sponsorship” model, but I agree that it’s an important one. In my visualization, I’d picture it at the very top, as the most direct form of revenue: you’ve got the revenue (or mandate) even before the data is delivered to end-users. Government services, regulatory requirements, NGO missions, etc. all fall under that heading — and, at least today, represent a significant portion of the big linked data sets out there.
I think you’re absolutely right that freemium models are particularly powerful for subscription and value-add models. You’ve got a great list of ways in which the premium version of the data can be differentiated, without hindering the wide distribution of the free version. One other variation may be tied in with the authority model: validated/certified components of the data that are added for paying subscribers.
However, I want to better explain the idea of advertising inside data.
What I’m thinking of there is not advertisements the way they exist in the visible web, but sponsored extensions to a data set that would have actual utility to people consuming these feeds. Enough utility that people would be more inclined to use them rather than strip them out.
Here’s a hypothetical example. Let’s say Google starts making linked data available about what it knows of local businesses: location, business hours, categories of products and services, etc. Maybe they include ratings data (as part of an “authority” model). Now, imagine that they have an optional set of “coupon” fields for special offers made by those businesses. But in order to include coupon information in the data set, the local business has to pay a fee.
Such coupon data could likely be valuable to a set of folks consuming that data. The local businesses are then subsidizing the data set by their paid inclusion of certain extra data within it it. (Kind of like a supply-side freemium model: extra premiums for being in the data set.)
Granted, this is hypothetical. But does that seem like it might be more useful?
I don’t think ‘Advertising’ should be thought of in a strict sense, a la Google AdWords, but in a broader sense, a la “product placement”. For instance, Coca-Cola could pay a beverage data provider to include their soft drinks in the dataset. The listings might appear just as any other entry in the data set, and still be entirely legit, accurate and processable. The service provided by the data provider to the advertiser thus would be the mere inclusion of the (reliable) data, as opposed to leaving it out – not exaggerating or skewing it as, say, TV advertising tends to do.
I agree with Anders. Ads don’t have to be identified. They have to be relevant.
Say I’m subscribed to a feed about vacuuming. If a link to “Vacumaster 3000” pops up in the stream, I’m okay with it. In the consumer – app / service provider – data provider triangle no one blames the other. I think it’s very viable and will revolutionize marketing.
Great post, and your thoughts on monetizing are clearly defined and interesting. I’ll be using your definitions in future discussions. It occurs to me that what you need for the actual access control component is some kind of ‘content agnostic’ authorization service… I wonder where you might find one of them? We should talk.
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