This my second post on data marketplaces… unfortunately triggered by the bad news of Talis’s winding Kasabi down. There are a number of good posts discussing this and its meaning to the Semantic Web and Linked Data efforts. I’d like to share my ideas here but focusing on the data markeplace side of the story.
In his blog post, Tim Hodson wrote:
So we were too early. We had a vision for easy data flow into and out of organisations, where everyone can find what they need in the form that they need it through the use of linked data and APIs, and where those data streams could be monetized and data layers could add value to your datasets
The previous quote aptly captures the essential aspects of data marketplaces. In its richest form, a data marketplace enables buying/selling access to quality data provided by different publishers (essential aspects are in bold).
Tim went on to say:
Other organisations besides Talis, sharing similar visions, have all had to change the way they present themselves as they realise that the market is simply not ready for something so new.
So I looked at a number of existing data marketplaces and see how they present themselves. It is hard to identify what exactly is a data marketplace, however I am including these mainly based on Paul Miller’s podcasts:
- AggData.com: sells lists crawled from the Web as downloadable files.
- Datafiniti: sells data crawled from the Web through SQL-like interface.
- Microsoft Azure Data Marketplace: sells data from a number of publishers via API access based on OData.
- Infochimps: sells data from a number of publishers via a mix of downloads and API access.
- datamarket.com: sells only numeric data provided by a number of publishers. It focuses mainly on visualization but also provides API access.
- Factual: collects data (mainly related to locations) and sells API access to the data.
- Kasabi: sells API access to data from different publishers.
Form the list above, datamarket.com, Azure, Infochimps and Kasabi fit the more specific definition of data marketplace i.e. provide API access to data provided by different publishers. These functionalities have their implications:
- Supporting different publishers calls for a managed hosted service (a place for any publisher to put its data).
- API Access calls for cleansing and modeling any included data.
Selling simple access to collected data (e.g. downlodable crawled lists) doesn’t involve any of the two challenges above (or involves a simpler version of them). Providing data hosting services (i.e. database-as-a-service) doesn’t necessarily involve data cleansing and modeling (as these only affect the owner of the data which is mostly its only user). Both domains, collect-and-sell-data and database-as-a-service, seem to be doing fine and enjoying a good market. On the other hand, if we look at data marketplaces, it is clear that they don’t present themselves as pure data marketplaces (not anymore at least):
datamarket.com ==> sells the platform as well, specialises in numbers and focuses on visualization.
Infochimps ==> calls itself “Big Data Platform for the Cloud”
Azure Data Marketplace ==> is still a pure marketplace but as part of the Microsoft Azure Cloud Platform.
All these make me wondering, is data marketplace too big a thing to be tackled now? is the market not ready? technology and tools not ready? are marketplaces not selling themselves well? should we give up the idea of having a marketplace for data?
I am just having hard time trying to understand…
P.S. All the best for the great Kasabi team… I learned a lot from you!
One of the questions I was interested in while listening to the excellent series of podcasts by Paul Miller on data marketplaces was: why would people pay to access data? this can be put differently as: what values do data marketplaces offer?
Here is a compiled list of benefits that data marketplaces promise:
- Discoverability: through a central place where datasets are described and can be found.
- Easy access to the data: via providing API access to the data for example.
- Easy publishing: of-the-shelf infrastructure.
- Commercialisation: easy buying and selling data.
- Better data quality: providing curated and maintained datasets.
- Value-added data: having all the datasets in one place enables users (or the marketplace provider) to draw new insights, remix datasets and derive new ones.
A logically following question is: how can we evaluate the extent to which data marketplaces are fulfilling their promises? With the expanding belief that data should be made available for free, it is important for data marketplaces to make clear the additional value they offer. Ironically maybe, this can prove to be very helpful to the open data movement as quality complaints that usually accompany open data can be addressed by marketplaces with a non-prohibitive cost on the consumer side… I believe that an empirical study of the existing data marketplaces can reveal interesting insights and lessons. I don’t have a clear idea about how to evaluate the impact that data marketplaces have achieved regarding their potential benefits but few sketchy ideas…
- Discoverability: do data markets enhance metadata description of datasets? provide an API to search for datasets? standardise metadata description? etc…
- Easy access to the data: this boils down to evaluating the access method (mostly an API) provided along with the service quality metrics such as availability, performance, etc… An interesting idea I came across in this paper(PDF) is that the prevailing charge-per-transaction model hinders ease of acces as clients might have to cache results. Data Licensing is also related to the ease of access and data marketplaces have the potential of fostering convergence on a small, but sufficient, set of data licenses.
- Easy publishing: evaluating the set of services the data market provides for publishers
- Commercialisation: what percentage of datasets on a marketplace is not free? are there datasets available for sale on a market but not anywhere else (its commercialisation is solely enabled by the existence of the market place)?
- Better data quality: did marketplaces enhance the quality of (open) data available elsewhere?
- Value-added data: can users meaningfully remix existing datasets? is there a market-wide query engine? are there new datasets provided by the data marketplace through drawing insights from or remixing a number of existing datasets?
One of the biggest challenges here is that the term “data marketplace” is sill used in a very loose manner which risks ending up comparing apples with oranges… However, a carefully designed comparison can prove vital in advancing the current state of art. I’d be very glad to hear your ideas and feedback on this.