Monthly Archives: January 2010

There has been a growing movement to make data available on-line in a manner that’s easy to access and query for developers. These data sets range Yelp Reviews and government statistics to beer quality. In particular, there has been a rapid increase in Linked Data (i.e. interconnected data sets structured using web standards). I posted to twitter a back of the envelope calculation that Linked Data tripled in size in 9 months of 2009 to almost 13.1 billion triples. Interestingly, the first message to me after posting was: What is the business case for Linked Data?

It is critical to answer this question in order to maintain the viability of Linked Data over time. Obviously, some data (i.e. from governments) will be made available as a public service. But, especially as this data becomes more popular and thus more expensive to host, there needs to be a way to support these data sets and encourage more and better data sets to come online. Additionally, if there are ways to make money from or around Linked Data, we will see a stronger Linked Data developer and support ecosystem. Such an ecosystem will make Linked Data even more valuable both as a public and private resource.

With that in mind, I’ve thought of the following business models for Linked Data. This is in addition to Scott Brinker’s 7 business models. You can find more discussion here and here.

1. Tools for Analytics and Business Intelligence

I like to say that Linked Data is analytics enabled. It provides for rich machine understandable  data sets with common formats and query languages. When done correctly, it connects out to a wider set of data enriching local data with very little developer effort. I believe there is space to develop advanced business intelligence tools that can leverage the linked nature of these data sets. Furthermore, it should be easy to adapt such tools from domain to domain by making use of common ontologies. Thus, these tools can be easily customized for particular business needs. By the way, the business intelligence software market is $8.8 billion.

2. High Resolution and Realtime Data Sets

Open Linked Data sets can be loss leaders for information providers to sell high quality or up to the minute data sets. This is often done by data providers, the classic example is delayed stock ticker information. But it also common for web services to charge for high numbers of queries and better access. In the realm of Linked Data, the Ordnance Survey is exposing mapping Linked Data for free as a public service while charging for higher resolution datasets. We already see the emergence of data marketplaces for purchasing data sets. I think this trend will continue allowing data providers to provide quality data and charge those who need the very best, now.

3. Exposing Data Sets

If companies see the benefits of exposing their data as Linked Data either to sell it or to use new analytic tools, there will obviously be a role for firms that specialize in this practice. I doubt this will be a high revenue business model. However, given the availability of tools, it might be highly profitable.

4. Aggregation

Like the Web, the Web of Data is messy and large. We’ve seen on the Web how aggregators can create tremendous value by collecting, cleaning and ranking data. Building the infrastructure to do this task is expensive and for most companies it’s better to let someone else do it and pay for access. My favorite company that has this model is spinn3r, which provides a indexed, cleaned, structured view of the blogosphere.

5. Tailored Push or Data Set Recommendation

With an ever increasing amount of data, there is the need to find and curate data tailored to an individual corporation’s needs. I envision a subscription service where specifically designed data sets are obtained either through advanced algorithms searching the Linked Data Cloud or by manually creation. These specialized data sets could be built by observing the queries that were made when using the analytics tools discussed previously. Imagine, if I’m examining data sets about beer imports from Holland to America and the next day an accurate break down by type of beer for the last 3 weeks appeared in my data inbox.

I hope this post has helped contribute to the conversation on Linked Data and business. Now it’s time to implement some of these.

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