"This linked data went to market...wearing lipstick!?!"

This post originally appeared on the Wordpress.com version of this blog

Paraphrasing the nursery rhyme,

This linked data went to market,
This linked data stayed open,
This linked data was mashed-up,
This linked data was left alone.
And this linked data went...
Wee wee wee all the way home!

In his recent post Business models for Linked Data and Web 3.0 Scott Brinker suggests 15 business models that "offer a good representation of the different ways in which organisations can monetise — directly or indirectly — data publishing initiatives." As is our fashion, the #linkeddata thread buzzed with retweets and kudos to Scott for crafting his post, which included a very seductive diagram.

My post today considers whether commercial members of the linked data community have been sufficiently diligent in analysing markets and industries to date, and what to do moving forward to establish a sustainable, linked data-based commercial ecosystem. I use as my frame of reference John W. Mullins' The New Business Road Test: What entrepreneurs and executives should do before writing a business plan. I find Mullins' guidance to be highly consistent with my experience!

So much lipstick...
As I read Scott's post I wondered, aren't we getting ahead of ourselves? Business models are inherently functions of markets --- "micro" and "macro" [1] --- and their corresponding industries, and I believe our linked data world has precious little understanding of the commercial potential of either. Scott's 15 points are certainly tactics that providers, as the representatives of various industries, can and should weigh as they consider how to extract revenue from their markets, but these tactics will be so much lipstick on a pig if applied to linked data-based ecosystems without sufficient analysis of either the markets or the industries themselves.

To be specific, consider one of the "business models" Scott lists...

3. Microtransactions: on-demand payments for individual queries or data sets.

By whom? For what? Provided by whom? Competing against whom? Having at one time presented to investment bankers, I can say that "microtransactions" is no more of a business model for linked data than "Use a cash register!" is one for Home Depot or Sainsbury's! What providers really need to develop is a deeper consideration of the specific needs they will fulfill, the benefits they will provide, and the scale and growth of the customer demand for their services.

Macro-markets: Understanding Scale A macro-market analysis will give the provider a better understanding of how many customers are in its market and what the short- and long-term growth rates are expected to be. While it is useful for any linked data provider, whether commercial or otherwise, to understand the scale of its customer base, it is absolutely essential if the provider intends to take on investors, because they will demand credible, verifiable numbers!

Providers can quantify their macro-markets by identifying trends, including demographic, socio-cultural, economic, technological, regulatory, natural. Judging whether the macro-market is attractive depends upon whether do the trends work in favour of the opportunity.

Micro-markets: Identifying Segments, Offering Benefits Whereas macro-market analysis considers the macro-environment, micro-market analysis focuses on identifying and targeting segments where the provider will deliver specific benefits. To paraphrase John Mullins, successful linked data providers will be those who deliver great value to their specific market segments:

• Linked data providers should be looking for segments where they can provide clear and compelling benefits to the customer; commercial providers should especially look to ease customers' pain in ways for which they will pay.
• Linked data providers must ask whether the benefits their services provide as seen by their customers are sufficiently different from and better than their competitors, e.g. in terms of data quality, query performance, more supportive community, better contract support services, etc.
• Linked data providers should quantify the scale of the segment just as they do the macro-environment: how large is the segment and how fast is it growing?
• Finally, linked data providers should ask whether the segment can be a launching point into other segments.

The danger of falling into the "me-too" trap is particularly glaring with linked data, since a provider's competition may come from open data sources as well as other commercial providers: think Encarta vs. Wikipedia!

Having helped found a start-up in the mid-1990s, I am acutely aware of the difference between perceived and actual need. The formula for long-term success and fulfillment is fairly straightforward: provide a service that people need, and solve problems that people need solved!

Notes:

• In an upcoming post I'll discuss the need for providers to perform a linked data industry analysis as a complement to the market analysis described here...
• On the topic of creating datasets that people need, see How to create datasets that the rest of the world needs on the Infochimps.org blog.
• Finally, on the topic of taking your linked data to market, visit Google Apps Marketplace. Stare. Now go to Infochimps.org's listing of datasets. Stare. Go back...Forward...Back...Forward...Thoughts?

References

1. John W. Mullins, The New Business Road Test (FT Prentice Hall, 2006)

Scale-free Networks and the Value of Linked Data

Kingsley Idehen of OpenLink Software and others on the Business of Linked Data (BOLD) list have been debating a value proposition for linked data via Twitter (search for #linkeddata) and email. The discussion has included useful iterations on various "elevator pitches" and citations of recent successes, especially the application of GoodRelations e-commerce vocabularies at Best Buy. After some deep thought I decided to take the question of value in a different direction and to consider it from the perspective of the science of networks, especially with reference to the works of Albert-László Barabási, director of the Center for Complex Network Research and author of Linked: The New Science of Networks. I'd like to test the idea here that data sharing between organisations based on linked open data principles is the approach most consistent with the core principles of a networked economy. I believe that the linked data model best exploits "networking thinking" and maximizes the organisation's ability to respond to changes in relationships within the "global graph" of business. Using Barabási as a framework, linked data is the approach that most embodies a networked view of the economy from the macro- to the micro-economic level, and therefore best empowers the enterprise to understand and leverage the consequences of interconnectedness.

As has been noted numerous times elsewhere, the so-called Web of Data is perhaps the web in its purest form. Following Tim Berners-Lee principles or "rules" as stated in his Linked Data Design Issues memo from 2006, we have a very elegant framework for people and especially machines to describe the relationship between entities in a network. If we are smart about how we define those links and the entities we create to aggregate those links --- the linked datasets we create --- we can build dynamic, efficiently adaptive networks embodying the two laws that govern real networks: growth and preferential attachment. Barabási illustrates these two laws with an example "algorithm" for scale-free networks in Chapter 7 of Linked. The critical lessons are (a) networks must have a means to grow --- there must not only be links, but the ability to add links, and (b) networks must provide some mechanism for entities to register their preference for other nodes by creating links to the more heavily-linked nodes. Preferential attachment ensures that the converse is also true: entities will "vote with their feet" and register their displeasure with nodes by eliminating links.

In real networks, the rich get richer. In the Web, the value is inherent in the links. Google's PageRank merely reinforced the "physical" reality that the most valuable properties in the Web of Documents are those resources that are most heavily linked-to. Those properties provide added value if they in turn provide useful links to other resources. The properties that are sensitive to demand and can adapt to the preferences of their consumers, especially to aggregate links to more resources that compound their value and distinguish them from other properties, are especially valuable and are considered hubs.

Openness is important. At this point it is tempting to jump to the conclusion that Tim Berners-Lee's four principles are all we need to create a thriving Web of Data, but this would be premature; Sir Tim's rules are necessary but not sufficient conditions. Within any "space" where Webs of Data are to be created, whether global or constrained within an organisation, the network must embody the open world assumption as it pertains to the web: when datasets or other information models are published, their providers must expect them to be reused and extended. In particular this means that entities within the network, whether powered by humans or machines, must be free to arbitrarily link to (make assertions about) other entities within the network. The "friction" of permission in this linking process must approximate zero.

Don't reinvent and don't covet! The extent of graphs that are built within organisations should not stop at their boundaries; as the BBC has shown so beautifully with their use of linked data on the revamped BBC web site, the inherent value of their property was increased radically by not only linking to datasets provided elsewhere, openly on the "global graph," but also by enabling reuse of their properties. The BBC's top-level principles for the revamped site are all about openness and long-term value:

The site has been developed against the principles of linked open data and RESTful architecture where the creation of persistent URLs is a primary objective. The initial sources of data are somewhat limited but this will be extended over time. Here's our mini-manifesto: Persistence...Linked open data...RESTful...One web

The BBC has created a valuable "ecosystem"; their use of other resources, especially MusicBrainz and DBPedia, has not only made the BBC site richer but in turn has increased the value of those properties. And those properties will continue to increase in value; by the principle of preferential attachment, every relationship "into" a dataset by valuable entities such as the BBC in turn increases the likelihood that other relationships will be established.

Links are not enough. It should be obvious that simply exposing datasets and providing value-added links to others isn't enough; as Eric Hellman notes, dataset publishers must see themselves service providers who add value beyond simply exposing data. Some will add value to the global graph by gathering, maintaining, publishing useful datasets and fostering a community of users and developers; others will add value by combining datasets from other services in novel ways, possibly decorated by their own. Eric has argued that the only winners in the linked open data space have indeed been those who have provided such merged datasets as a service.

Provide value-adding services and foster community. I would argue that dataset providers asking how they might realise the full value potential of publishing their datasets on the Web should examine whether, based on the principles I've outlined above, they have done everything they can to make their datasets part of the Web (rather than merely "on" the web) and have truly added value to the global graph. Do they view themselves as a service? Have they made their datasets as useful and easy-to-use as possible? Have they provided the best possible community support, including wikis and other mechanisms? Have they fully documented their vocabularies? Have they clearly defined any claimed rights, and in particular have they considered adopting open data principles?