Data is not the new oil

Data is a, if not the, fundamental building block of Industry 4.0 that we are experiencing, where the boundaries between real, virtual and biological worlds are increasingly getting blurred. Advances and cross-fertilization between genetic engineering, robotics and AI are the engines.

However, the role of data within this new form of economy is not sufficiently understood. Current accounting paradigms are not fit to measure data’s value. It has unique features that make it different from traditional assets, goods and services.  Data is not the new ‘oil’. And it is different from typical labor and capital inputs for economic production. It can be replicated and transacted infinitely with a marginal cost approaching zero. It doesn’t depreciate. It can be used to regenerate new data and can serve multiple purposes simultaneously. 

Many attributes of a person’s life, such as demographic profile, home address, and DNA sequence as well as digital footprints on social apps are all examples where data is needed as an input to the production processes of AI algorithms. Data cannot be treated simply as a common good that should be shared by all openly.

Initially, highly personal data was considered to be a form of data waste material, a mere by-product of the data centers that are hosting the streaming services such as Google Search, YouTube, and other data-intensive services that are using cloud infrastructure. But fairly soon the Big Five tech companies realized that using such so-called “exhaust data” could be turned into a highly profitable business model by using this waste product to provide highly targeted, individualized content, perform predictive analytics, or by simply selling the data to other companies. The realization that the exhaust data was actually valuable led to a radical change in the business model of Google Search from being a mere search engine into a data vacuum cleaner for the targeted ads industry. Rather than using data for value creation, as is the normal business model for data-intensive industries, it provided a new mechanism for value extraction.

Passive or active data curation activities, data verification of the intermediary state of a processed data set and the transformation of data into information need to be redefined as new forms of labor. For example, letting a GPS app track our position is essentially a data generation activity done by us and therefore needs to be treated and remunerated as such.

Current business models around data generating activities are not able to respond to the true nature of the Data Economy, its socioeconomic or sociopolitical implications, or AI as its production engine: 

• Data and data rights are exchanged for "free" in return for "free" services, notably to the BigFive tech companies, i.e. Google,  Facebook, Amazon, Apple, and Microsoft. This results in a concentration of wealth in the hands of a few and an increase in overall wealth inequality.
• Donating data to certain organizations or data pools is not creating the explicit channels of value flow to the curators (for example, Amazon's Mechanical Turk, an online marketplace for work performed by a scalable workforce). Further, decision rights on what to do with the data are either taken away or insufficient.

Yet another result of this closed system in the data economy is that individuals and businesses are increasingly becoming reluctant to share and open their data. Open data sharing has become a core feature of the European Commission's strategy to develop the Digital Single Market, which is intended to support job growth of the European digital economy. The European Digital Single Market would become one of the most valuable trade markets in the world for online businesses. It is estimated that a fully functional Digital Single Market could contribute €415 billion per year to the EU economy (See the European Commission Press release).

However, this vision of a digital single market lacks three essential elements that would boost the data economy to its full potential:

1. Privacy-aware and confidentiality-aware data integration;
2. Fair valuation of the economic value of the data generation and curation activities performed by individuals and organizations;
3. Assurances of data quality, and the validity and relevance of data points.

At Lucidminds AI,we have been building three novel services that will contribute a paradigm shift: 

1. A SimCity-like simulation tool. It will help anyone to explore the relation between privacy, wealth and wellbeing in the age of Industry 4.0. We have implemented the first version of the back-end engine and we are working on connecting it to an interactive Web based user interface.  
2. A decentralized data marketplace design and experimentation tool. It will serve as a validation and experimentation space for other initiatives that want to create fair valuation and pricing of data assets and may encourage creation of data cooperatives that would give bargaining power to the collective of individuals who actively or passively curate data assets.
3. A privacy-aware data science platform. We have been collaborating with a number of public and private institutions to enable analytics on sensitive datasets. Our motto is ‘Share insights, not data’.

These are examples of our overall ambition in connecting theory to practice and sharing how we perceive data, AI and privacy within social, economical and political systems.