Data economics: Understanding and taming Big Tech

This document is highly work-in-progress, and I’d appreciate to hear from you. Contact me at @KKollnig or, or leave a comment below.

Big Tech amass personal data at an unprecedented scale. This has unsettling consequences for individual privacy.

At the same time, Big Tech have a quasi-monopoly over digital services. They attract the majority of web traffic. This potentially restricts digital innovation.

Data protection law—such as the GDPR in the EU—is often regarded as taming Big Tech. However, such legislation is meant to protect the fundamental freedoms of individuals, not to address monopolism.

Likewise, current competition law is unsuited. The tools of the competition authorities fail to keep up with globally operating tech companies.

In fact, there exist hardly any legislation to govern Big Tech.

The data malpractices will continue, if no action is taken, and societal progress be hampered.

This motivates studying data economics, that is tools to investigate the data economy. This both means

  1. foundational, theoretical concepts (such as data production, data value, data market power, data market concentration, and data mergers), and
  2. the empirical, factual and quantitative characterisation of the data economy.

Surprisingly, there exist little research into data economics.

To begin with, one could combine economic theory, legislation (such as the EU General Data Protection Regulation, the EU Business-to-Platform Regulation, and the EU E-Commerce Directive), and computer science (such as big data, NLP, network analysis, and value-driven engineering).

The toolbox could be evaluated on a subset of the overall data economy, a data ecosystem.

The most lucrative such data ecosystem is mobile advertising. Mobile advertising accounts more than 90% of Facebook’s revenue, and more than 65% of Google’s.

Mobile advertising is also interesting from a data privacy perspective. Mobile devices generate some of the most pervasive and most valuable data.

Differences between advertising platforms, such as web, iOS, and Android, might shed light on the implications of different rules, enforcement, and audience for the data economy.

The objective of such studies would be a regulatory toolbox, to investigate if regulation is needed, and if so, what regulatory steps should be taken.

In the end, this regulatory toolbox could be used to assess some of the currently most heavily debated and promising regulatory tools, including break-up of Big Tech, data trusts, data portability, open data, personal data stores, and open APIs.

It is time to act on Big Tech.

Thanks for your interest. If you enjoyed this read, I’d love to hear from you at @KKollnig or, or leave a comment below.