The Value of Data, Part 1: Using Data as a Competitive Advantage

Data is incredibly valuable. It helps create superior products, it forms a barrier to entry, and it can be directly monetized. This post is the first in a 3-part series about building companies that leverage the value of data.

3-Part Series

Part 1: An explanation of why data is becoming increasingly valuable + a catalog of ways in which data creates competitive advantages. [this post]
Part 2: How to accumulate data + pitfalls to be aware of.
Part 3: A catalog of business models where data plays a key role.

In this post, I’m going to talk about how the increasing commoditization of software and hardware makes data more valuable, and how data can be used to create competitive moats.

Hardware and Software Commoditization

During my decade of working as a developer I saw some major changes in the software industry:

LinkedIn in 2003-2005:

Factual in 2009-2012:


On the software side, developers have gone from writing low-level libraries to using pre-built libraries to using sophisticated, feature-rich APIs. On the hardware side, we’ve gone from buying machines and storing them a few miles from the office to deploying everything to the cloud.

The trend is clear: both software and hardware infrastructure are becoming more and more commoditized. So what’s left if you’re trying to build a company with sustainable competitive advantages? Data.

Data Moats

Large, meaningful datasets are incredibly hard to build, which is part of what makes them so valuable. A recent trend is for tech companies to open-source non-core software because it’s good for the community and also good marketing. For example, Netflix has publicly released many of its infrastructure and monitoring tools.

However, while companies will publish some of their code, it’s very unusual for them to release their data. That’s because while it’s possible to replicate software with a strong engineering team, it’s very hard to replicate meaningful datasets without large user bases (or very complex web crawling/scraping pipelines). This is an ideal competitive moat: a company can’t copy your product without the corresponding dataset, but that dataset can only be built via an existing heavily used product – a catch-22!

Of course, the scarcity of good datasets is just part of their value. The remaining value lies in the hard-to-copy applications that are enabled by large amounts of data. Here are some ways to apply data to create more defensible products:

In each of these cases, bigger datasets created bigger competitive advantages. At some point, competitors can no longer catch up to you – even if their products are superior – because they don’t have the data that you have.

This is by no means an exhaustive list of competitive advantages, and if you have suggestions for other ways that data helps make products more defensible, please let me know on Twitter.

Next week, I’ll cover effective ways to accumulate data, as well as what to watch out for as you’re building up your data assets.

Additional resource: Building Competitive Moats With Data by Pete Skomoroch

Thanks to Sean Byrnes, Seth Berman, and Eva Ho for feedback on this post.

Tags: Value of DataBusiness Models
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