The Value of Data, Part 2: Building Valuable Datasets

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 second in a 3-part series about making data a core part of a startup's business plan.

3-Part Series

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

In this post, I'm going to talk about tips and strategies for building valuable datasets.

Attributes of a Valuable Dataset

What should you strive for as you accumulate data? Here are the key attributes of valuable datasets:

  1. An ideal dataset is hard for others to build from scratch.
  2. An ideal dataset consists of clean, accurate, up-to-date data.
  3. An ideal dataset has useful applications. For example, a database of restaurant menus would be quite valuable while a database of shoe sizes would be much less valuable.
  4. An ideal dataset grows in value as more people or machines contribute to it. That is, the data has positive network effects.

Data Sources

Ok, you read the first post in this series and you're convinced that data is great and that you want more of it. But how do you get it? Here are some proven approaches:

Tips for Accumulating Data

Caveats and Pitfalls

While building up a huge dataset can be great, there are things that can reduce the value of your data. Here are some questions to ask yourself:


So far, I've covered ways of accumulating valuable data, and how that data can be used as a competitive advantage. In next week's post, I'm going to discuss specific business models that have data at their core.

Thanks to Sean Byrnes, Chad Byers, Eva Ho, Wen-Wen Lam, and Shivon Zilis for feedback on this post.

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