Like so many of my projects, my latest O’Reilly Radar report was born out of a random conversation. The kernel that grew into Business Models for the Data Economy began as casual chat with my coathor, Ken Gleason. We often examine business models as a kind of gedanken exercise, and that day our attention drifted to data. It struck us as odd: data is seen as the new gold rush, yet it seemed many business models in that arena focus solely on analysis.
Granted, the attention to analysis is well-deserved. Turning raw data into actionable insight can lead to improved business decisions, which can in turn drive cost savings, reduce risk, and expose new avenues for profit. But are there other opportunities to make money in the world of data? Are companies leaving money on table if they only concern themselves with analysis? Should other companies and services exist?
To answer these questions, we pondered the ways in which people could build a business around data. We considered the flow of data, from collection to analysis, to build a framework around key points at which someone could provide added value. We also folded in our own experiences, to draw parallels to other industries, and noted real-world companies that provide those very services. Eight major themes emerged from this exercise:
- Collect/Supply: Gather and sell raw data. Interested parties will pay you so they don’t have to gather the data on their own.
- Store/Host: Provide a way to hold on to someone else’s data for them. This can be especially profitable if you hold the same data for several parties, as you benefit from economies of scale.
- Filter/Refine: Data cleansing is a thankless but necessary task in any analytics exercise. Instead of just selling someone a raw dataset, you can charge a premium for one that omits duplicate records and otherwise dirty data.
- Enhance/Enrich: Add value to a raw dataset by merging it with another, or by precomputing results that people are likely to use.
- Simplify Access: Help people cherry-pick the data they want, in the format they prefer. This can be as simple as converting data to a more convenient format (say, from PDF to CSV), to something as complex as a system that helps people find data and subset it to their liking.
- Analyze: Uncover new information to guide business decisions, and otherwise explore data to answer questions.
- Obscure: Data has become easier to collect over the years, but not everyone wishes to be part of an analytics exercise. Certain businesses make their money by helping people and businesses keep their data from prying eyes.
- Consult/Advise: Data has become a key ingredient in a number of business realms. Specialized consultants can help firms to make the most of their data, from how to analyze it to how to monetize it.
We explore these themes, where they work and best, and important considerations in our paper, Business Models for the Data Economy. We hope it inspires you to develop new business models around your data.
Download Business Models for the Data Economy for free.