Here’s What We Need To Create A Data Trading Economy

Big Data Semantics

The commercial shipping industry only really flourished when precise navigation equipment brought certainty and a degree of automation into an adventurous and craftsmanship-based trading practice, where merchants had no assurance that the goods loaded into a ship would ever make it to the destination port.

Nowadays, navigating the oceans of Big Data around us, some skilled explorers are succeeding in ad-hoc trading, but for the rest of the merchants wanting to ship data to far away markets, Data Trading is a really uncertain endeavour. So, I want to elaborate on what I think is needed to enable a reliable Data Trading economy.

In the future data-driven society, the cornerstone will be personal data, and the debate will be on user rights and transparency:

A New Deal For Data

In his book Social Physics, MIT director Alex ‘Sandy’ Pentland, adviser to Telefónica’s digital services, highlights the need to protect the rights of users to control, distribute or delete personal data. A more transparent society can be built on a more open flow of data.

Big Data Ethics

Other influential voices, like Craig Mundie or Jonathan H. King are advocates of Big Data Ethics to guide the use of personal data, and creating “Intelligent Data Use Trackers” for users to know the risks and rewards – and control the use of their data in each application.

The deep insights shared by these top thought leaders should lead the industry in building better tools. This is in contrast to current “online privacy fears” generated among a majority of people, for which technology is an arcane art, but that do understand and value their personal information and privacy.

On how to tackle the handling of Big Data and trading with it, Colin Coleman, executive at Turner Broadcasting (CNN) and former analyst at NASA, described it vividly at the Structure Data event: ”The Big Data complexity lies in the fact that both, the industry and its business model, are in flux”.

According to Coleman, “Hadumping your data” (for Hadoop, a popular Big Data technology) does not solve the complexity, as the Holy Grail for Advertisers and Media seems to be in the correct attribution of ads to audiences, which is simply not possible now. At the same event, Peter Guerra, consultant for US Defence, said that a “Data-GPS” would be needed in order to make sense of the data traffic, tracking the flow of every piece of data.

Let’s imagine a future Big Data Trading Economy in which the “flux” alluded by Coleman has stabilised, and a Data-GPS has provided data tracking and some basic environmental conditions for it to grow. This way, we can try to see how far we are from that future.

Reliability would be a key aspect for a Data-GPS to provide, to ensure authentication and integrity of data from its origin to its intended recipients so that each side recognizes and trusts the other, as exemplified by the attribution of ads to audiences.

Transactional businesses impacted would be: advertising, mobile payments, content delivery, and others where trust and attribution are required. In fact, if we follow the initial analogy, even commercial shipping could benefit from it although, of course, much more than data is involved in hurling physical goods across the world.

Compliance is another fundamental aspect, in regulated industries like Defence, Banks or Telecoms. In those cases, keeping precise track of all transactions is key, e.g. provide your Data Protection Agency indisputable evidence of compliance with security requirements for access and handling of personal data. To enable compliance, fully auditable, attributed and secure transactions would be needed.

And in fact, the telecoms industry is one in which trust, regulation compliance and attribution would be a natural fit, as natural as the everyday task of moving large volumes of data in real-time under heavily regulated privacy and quality of service conditions. Telecom players would be in an ideal position to leverage their particular know-how in order to to lead the Big Data Trading in the near future.

But, for the moment, the telecoms industry at large is playing catch-up with Internet companies, however, their focus has been on Big Data technology while privacy, trust or attribution are issues yet to be solved.

So, at this point, trusted, trackable and compliant Data Trading could evolve to a “stock market” model of trading, in which data contributors, data brokers and data consumers would be part of an automated value chain:

  • Transparency with ethics and privacy rights for users to control the use of personal data
  • Reliability and trust brought by authentication, attribution and integrity of participants
  • Compliance, enabled by trusted, secure and fully trackable transactions, and
  • Automation, enabled by higher confidence in all of the above.

This is an opportunity that I see as being realised not too far in the future. It may very well be that the different elements of this Data Trading Economy already exist in the present. Perhaps they’re just too scattered for us to see it yet.

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Jose-Luis Agundez

Jose-Luis Agúndez leads the Big Data Innovation program for data monetisation within Telefonica's digital services. Jose-Luis is a computer scientist that enjoyed all roles from C developer to senior architect in complex real-time products, before dedicating the last 12 years to discovering differential technology and ways to take it to market, including product and business development, partnerships and VC. He has 5 granted patents, and has contributed to Standardization (3G) and Licensing (4G patent pool). He is a blogger, speaker and panellist focusing on data privacy and the applications of Big Data to society and people.