Some data on data companies

Data companies represent >400bn of public market capitalization

The following does not represent and is not intended to be investment advice.

The last several years have seen increasing interest and funding for data analytics, big data, and machine learning. A large number of both machine learning and data infrastructure companies (“modern data stack”) have led to large financial outcomes for employees, founders, and investors alike. Most of these companies have adopted a SaaS business model: software delivered on the cloud in exchange for recurring subscription payments. 

An overlooked area (by the same prospective employees, founders, and investors) has been data-as-a-service (DaaS) companies. To industry outsiders, the only connotation DaaS has is with marketing firms and associated privacy scandals.

I am often asked if anyone has built a large company that sells data. The universe of such companies, often having nothing to do with marketing or ads, is quite broad and represents hundreds of billions of market capitalization. A (very) incomplete list of such public companies is below:

• S&P 102bn.

  ¹

• Moody's 45bn.

• Iqvia 34bn.

• Costar 29bn.

• Refinitiv 27bn.

  ²

• Verisk 27bn.

• Experian 27bn.

• Gartner 21bn.

• Equifax 21bn.

• ZoomInfo 17bn.

• Factset 15bn.

• Transunion 11bn.

• FICO 10bn.

• Nielsen 10bn.

• Black Knight Financial 10bn.

• Dunn & Bradstreet 5bn.

Taken together, these companies represent >400 bn of market capitalization. Of course, many of these companies derive revenue from more than just selling data

³

, but are predicated on a proprietary data asset.

This lists excludes other public examples and omits the largest private companies including Bloomberg, NielsenIQ/Gfk, and NPD/IRI for which valuations are not easily available. There is also a universe of earlier stage private companies that may eventually fill these ranks, including Safegraph, PredictHQ, Clearbit, and many others.

1. Numbers rounded to the nearest billion, based on Google Finance, accessed 10/2/2022.

2. Based on publicly reported purchase price by LSEG.

3. Auren Hoffman’s framework of such companies as existing on a plane consisting of axes “truth vs. religion” and “data vs. application” is helpful.