Creating competitive advantage through data

July 21, 2011, 8:00 PM UTC

Companies must recognize the value of their data, even if the data isn’t “big.”

I’ve been focused on investing in data-centric businesses for almost eight years, during which my view of what generates true competitive advantage through data has changed. Where tools and technologies for data storage and management once weighed heavily on my mind, the applications and business models for erecting barriers around proprietary data assets currently dominate my thoughts. And when I took a look at IA Ventures’ portfolio companies several themes became clear:

  • The power of creating contributory databases, where the value of the Nth contributor leads to a non-linear increase in the value of the data asset due to network effects. Examples in the IA Ventures portfolio include BillGuard and Metamarkets. ThinkNear’s plan is to build and monetize a contributory data asset as well.
  • The value of data aggregation, cleansing, normalization, indexing and streaming (data processing platforms), where massive real-time streams can be pushed to the desktop for customized filtering and analysis, made accessible via API for incorporation into live models and indexed and stored for historical analysis. Current portfolio companies in this sphere include Datasift, NewsCred, PlaceIQ, Recorded Future, SavingStar and Sulia.
  • The leveraging of platforms for creating valuable and differentiated data assets (data creation platforms), either as a part of the core mission or as an outgrowth of building a customer-facing business. BankSimple, Coursekit and Kohort each fit this description.
  • The importance of user experience, user interface and data visualization as tools for maximizing the value of data assets across each of IA Ventures’ key themes. Drew Conway joining the IA Ventures team as scientist-in-residence is evidence of the importance we place on helping our companies extract the most value from their data assets.

Contributory databases. The magic of these businesses is that a customer provides their own data in exchange for receiving a more robust set of aggregated data back that provides insight into the broader marketplace, or provides a vehicle for expressing a view. Give a little, get a lot back in return — a pretty compelling value proposition, and one that frequently results in a payment from the data contributor in exchange for receiving enriched, aggregated data. Once these contributory databases are developed and customers become reliant on their insights, they become extremely valuable and persistent data assets. An example of a contributory database is the credit index business of Markit, where they poll dealers for prices on specific fixed income instruments, synthesize the data into a standardized and normalized index, and enable market participants to build products on top of these now industry-standard indices. This was the catalyst for building a multi-billion dollar company. Me likey. A lot.

Data processing platforms. These businesses create barriers through a combination of complex data architectures, proprietary algorithms and rich analytics to help customers consume data in whatever form they please. Often these businesses have special relationships with key data providers, that when combined with other data and processed as a whole create valuable differentiation and competitive barriers. Bloomberg is an example of a powerful data processing platform. They pull in data from a wide array of sources (including their own home grown data), integrate it into a unified stream, make it consumable via a dashboard or through an API, and offer a robust analytics suite for a staggering number of use cases. Needless to say, their scale and profitability is the envy of the industry.

Data creation platforms. These businesses solve vexing problems for large numbers of users, and by their nature capture a broad swath of data from their customers. As these data sets grow, they become increasingly valuable in enabling companies to better tailor their products and features, and to target customers with highly contextual and relevant offers. Customers don’t sign up to directly benefit from the data asset; the product is so valuable that they simply want the features offered out-of-the-box. As the product gets better over time, it just cements the lock-in of what is already a successful platform. Mint was an example of this kind of business. People saw value in the core product. But the product continued to get better as more customer data was collected and analyzed. There weren’t network effects, per se, but the sheer scale of the data asset that was created was an essential element of improving the product over time.

A core part of our mission is helping portfolio companies define their data strategies and assist them create the differentiated, defensible data assets that will generate value for multiple constituencies. Sexy? No (unless, of course, you think like Mike Driscoll of Metamarkets). Glamorous? Definitely not. Effective? We think so. In today’s world, every business generates potentially valuable data. The question is, are there ways of turning passive data into an active asset to increase the value of the business by making its products better, delivering a better customer experience, or creating a data stream that can be licensed to someone for whom it is most valuable? And the data doesn’t need to be “big” to be valuable, though scale is certainly a helpful dimension when working to create defensible data barriers.

We’re in the early stages of a data-driven revolution, and the models outlined above are simply the current iteration of where we see opportunities for creating significant value for customers and investors alike. As exciting as the opportunity set is today, I can hardly imagine the scale of the opportunities tomorrow will bring.

Roger Ehrenberg is founder of IA Ventures. He blogs at

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