data, oil

The current industrial development is dubbed as Industry 4.0 or 4th Industrial Revolution. Starting with steam and water power (First Industrial Revolution), we have moved on to a digital revolution that has significant repercussions for the world order. Refineries that provide oil to the world are now being challenged by data refineries. Oil was the driver of growth and change for the previous century, data is surely for this one. The world created by data is in stark contrast to the one built on the fortunes of oil and gas. The ‘data universe’ is infinitely complex but laden with opportunity. Thus, data economy or datanomics warrants a discussion as to what the general populace need to know, and how is data economics playing out amid conventional development.

What is it?

Very much like how oil based development brought forth new infrastructure, flows of data have done the same. Oil companies are now being matched by the likes of Google/ Alphabet, Facebook, Google, Amazon, Microsoft, and a whole bunch of peer to peer services that are defining economics of the modern age.

The economy of data/ information economy is based on the value that data can generate. We have moved on from basic databases and now are entering in the age of Artificial Intelligence. Revenue is generated through a wide variety of avenues ranging from advertising to real time analysis. One of the biggest benefits of the space race between USSR and US were the subsidiary technologies/ products that were created, such as MRI. In the case of information/ data economy, a similar trend is being identified. Data being generated by users across the globe has spawned off thousands of companies working in areas such as customized advertising to crowd analysis through facial recognition. There is a cascade effect in motion. Data is used to attract more users, and in return these users generate more data, thus improving and diversifying services. Data is generated from activities as simple as typing a phrase in your search bar to liking or commenting on a Facebook post.

So very much like an oil well, enterprises are in search of new pockets of data that they can tap into. And it is a very reciprocal approach. Users themselves are improving the state of services through their feedback. For instance, when you rate a restaurant on Facebook or are tagging a friend in a photo, you are basically training FB’s algorithms. This is a very significant development, but further complicates the economics of data, an issue which will be discussed later.

Figure: Big data landscape (Source: Forbes)

 

Consider Uber, whose net worth is approximately $68 bn, and is built on simple data sharing between services provides (drivers) and consumers (passengers). Tesla is another example of data based innovation. The likes of Elon Musk are bent on improving the status of self-driving cars. Tesla models collect data from users which is relayed back to improve self-driving algorithms. Genomics is one such area, whereby sequencing data is improving the status of health IT service. 23 and Me is one start-up that provides genetic services to consumers, and as more and more people sequence their genomes (or a part of) diagnostic services would be transformed.

Datanomics: The Economy of Data

The economics behind data is a tricky subject with diverse opportunities and caveats. First is the question of value generation, how to determine which data is valuable and which isn’t. Secondly, how is the cost of data production and generation transferred to the consumer. It is important to note that merely flows of data are not a commodity. The third big question is that who owns data? Thus, trading data is shrouded in a certain level of uncertainty. A lot of us give up personal data in return for free services, which begs the question whether it is a fair deal?

Also, data economics seems not to follow the conventional sense of ‘increasing economies of scale’. Each additional unit of data may certainly not be useful, and in some bases profitability in data is contingent on finding a needle in the haystack. Data generation becomes profitable when it yields new services.

economic, data, table
Figure: Data economy framework (Source: IBM, 2016)

 

Consumers easily give up their stake in data when the tick the ‘I agree’ conditionality when downloading apps etc. This stems from lack of public knowledge on data, and there is also an argument for ‘learned helplessness’ which posits that people actually have no choice. This builds on to a bigger framework that firms that trade data, are basically doing it on the basis of free data.

Then there are competition laws and data enterprises. The legal infrastructure in a lot of countries is yet to adapt to the phenomenon of data start-ups. The acquisition of WhatsApp by Facebook provides a decent case study. Countries will need to upgrade their laws to ensure fair trade. There are blatant examples of monopoly in the digital world. Google has a global share on what consumers search, Facebook on what they share, and Amazon on what they buy.

Governments, presently have a limited share in the data economy. Most are dominated by private enterprises. Government participation in data based services is important so that better regulation can take place. Similar participation in digital currency will be a significant move to augment digital economy. Bitcoin is a prime example.

There is the obvious question of security of data as well. There has been a fair share of data leaks which have concerned stakeholders. The Snowden episode was a major happening which raised a lot of questions regarding data security and potential surveillance. However, one important offshoot of data based security is the ‘blockchain technology’ which will have massive integration in the years to come. Blockchain, through its decentralized and distributed ledger, defends against collusion of the network. It consists of two kinds of records, transactions and blocks.

Holistically, data economics is a vital theme for modern development. Countries, especially the developing world needs better representation in this sector. The modalities of the data universe are quite unique and the challenges are unprecedented. Unlike the nature’s bounty conferred by oil, data is ubiquitous and can be harnessed by any nation/ enterprise. It is imperative to take initiatives and proactive steps, because data would be of prime value and be source of influence in the years to come. There is a need to upgrade laws and regulations so that the terms of trade behind data economics can be made fair. Presently, lack of consumer knowledge and prevalent monology of a few digital giants is hindering progress.

Muhammad Adeel

Presently a PhD Scholar from WA State Agriculture Biotechnology Center Perth, Australia. He is also a Career Diplomat (44th Common) at Ministry of Foreign Affairs, Islamabad and he has also served as a lecturer at FCCU and manager of PABIC, Pakistan Biotechnology Center. A Passionate debator by heart, he has an experience of debates of more than five years. He has also been honored by the roll off honor for Debates, Essay writing completion and a summa cum laude award from FCCU.

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