Is big data driving policy?

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As the world gets more deeply and digitally interconnected, there is immense scope for harnessing personalised data to optimise modalities in various domains.

Big data delves into the rabbit hole of the sociological pyramid and provides useful perspective. | Flickr

Everything we do leaves a digital footprint. Big data has emerged as a buzzword in recent years. Broadly, it means a large amount of information that is generated as trails or by-products of online and offline activities — what we purchase using credit cards, where we travel via GPS, what we ‘like’ on Facebook or retweet on Twitter, and so on. Today, the Data as a Service (DaaS) movement is gaining momentum, spurring one of the fastest growing industries in the world. A somewhat nebulous term, DaaS refers to the myriad functions that technology serves. Clearly, big data holds vast potential to favourably impact the global socio-economic environment. But is it being used as a signalling device for effective policy changes?

Technology as a Catalyst to Growth

In the last decade, technology has spawned a new wave of economic development by creating new avenues for employment, amplifying economies of scale and reducing costs of production. Consequently, several platforms have emerged to counter growth challenges. For instance, Brazil and Dubai routinely face heavy road traffic. To solve this problem, Uber designed UberCopters and UberChoppers as an alternative to roads. Networks of aerial routes for quotidian air travel are now being rolled out in other countries as well. Agriculture is also being revolutionised by technology.

 

Several European countries utilise Cloud Computing and Telematics to assist farmers at every stage of the value chain, from crop growing (like prescription application that boosts yield) to trade (like digital sale systems eliminating middlemen). This helps farmers to hedge against uncertain variables such as rainfall and soil fertility by smoothing the transaction process. In the realm of financial services, net banking has significantly shrunk costs and time, while also encouraging financial inclusion.

People no longer wait in queues to manage their money; they merely click a button on their smartphones, and the intended action is complete. To make things even easier, money no longer has to be tangible to have value. For example, a large part of the United States runs on credit. In all these cases, a barrier to growth was spotted and resolved by modelling policy based on the impetuses of technology.

From Institutionalisation to Individualisation

The process of using data to augment standards of living involves a shift from the aggregate to the particular, i.e. a more personalised approach. Big data, combined with Behavioural Science, has given rise to a discipline called Psychometrics, which uses people’s digital traces to determine various aspects of their lives. In 2012, Michal Kosinski, one the forerunners of the field, demonstrated that a Facebook user's skin colour, gender, income group, ethnicity, sexual orientation, and religious affiliation could be determined with 85+% accuracy from a dataset of around 70 'likes'. As the number of likes increases, the more nuances they disclose about a person, like the kind of car they drive, the magazines they read, and the chocolate bar they like best.

Ultimately, it is these personality traits that determine behaviour. Hence the application of psychometrics to communications is changing the landscape of consumer retail, business, education, and even politics across the globe. In his Concordia Summit presentation, Alexander Nix, CEO of big data company Cambridge Analytica, stated the absurdity of segmenting audiences based on demographics or geographics. Why should all women or all old people or all rich people receive the same message simply because of their gender or demographics or income status?

Instead, digital trails can pinpoint exactly which messages will appeal to which audiences. He uses the example of television set-top box data; tracking the channels a person watches can be used to streamline advertisements. This would dramatically reduce costs, and multiply returns on investments. Marketers are increasingly selecting the individual versus institutional path to spread their messages.

Where Does India Stand?

India is the second-largest Internet market in the world, with 331 million Internet users. NASSCOM predicts that India’s big data market will be a $16-billion industry by 2025, with a 32% share of the global market and a CAGR of 26%. A simple search — ‘Big Data in India’ — in Google’s news search bar reveals that big data can make Indian cities a better place to live in, increase job opportunities, help track fraud, and influence politics. We have a vast amount of data from all layers of the societal pyramid, but a lot of it is sitting dormant due to ignorance and lack of infrastructure.

To penetrate this problem, JAM — Jan Dhan, Aadhaar, Mobile (Bank Account, Aadhaar Number, Smartphone) — is set to replace Bijlee, Sadak, Paani (Electricity, Transport, Sanitation) and Roti, Kapdaa, Makaan (Food, Clothing, Shelter) as the trinity leading India’s future growth.

With more players entering the market, data prices are recurrently dropping, and smartphones today cost a third of what they did a few years ago. If an individual possesses all three components of JAM, the advantages are multi-fold — they can procure loans via electronic banking, access telemedicine, make online purchases for items which may otherwise be difficult to obtain, etc. Moreover, the data collected from these activities can be translated into policy measures that will improve consumers' lives.

India largely adheres to blanket advertising, but as seen above, other countries are far ahead: Nix employed data analysis to coax the Brexit vote towards 'leave', and was also responsible for propelling Ted Cruz’s campaign, and Donald Trump’s victory to President of the United States. Everything that Trump put out was digitally vetted.

 

It is intriguing to wonder what would happen if big data was used to direct each farmer to the best fertilizer for his land depending on his terrain and choice of crops, or if kirana (small grocery) store–owners and vegetable-sellers could identify which goods sell most so that they can choose their stock more optimally.

For example, data revealed that those who owned an American-made car were more likely to be Trump supporters. Therefore, messages to this group and — more importantly — to other groups were customised appropriately. The Motherboard article that shared these results has sparked a debate on the ethics and dangers of big data.

If just 68 likes can say so much, its unimaginable what 4,000-5,000 data points could do. DaaS has been critical in responding to specific problems like road traffic and financial inclusion. However, on a larger scale, lessons learned from the Trump example strongly indicate that policy needs to function parallel to technology so that the latter doesn't surpass the former.

Policy Needs to Keep Up

Technology and regulation (encompassing the rule of law) have been adversaries for a long time. The key lies in selecting the right technology of regulation, i.e. the design and instrument choice of policy. A flexible balance between the two will not only increase efficiencies, but also foster transparency and inculcate accountability across stakeholders in the economy. In a similar light, laws need to be updated as privacy matters to both online and offline spheres. The Electronic Frontier Foundation is a game-changer in this field: it defends civil liberties in the digital domain through educational guides, activist workshops, and freedom-enhancing tools.

 

India is still cowering under heavy bureaucracy, but steps forward have been made.

Big data was exercised in the 2017 Economic Survey and Annual Budget, shedding new perspectives on the flow of goods, and migration in the country. Yet, a lot more needs to be done. It is intriguing to wonder what would happen if big data was used to direct each farmer to the best fertilizer for his land depending on his terrain and choice of crops, or if kirana (small grocery) store–owners and vegetable-sellers could identify which goods sell most so that they can choose their stock more optimally.

The same matching process can be applied to other industries to mete out more lucrative solutions.

Thus, regulation can either inhibit or stimulate technological change. The relationship between the two is complex, but it is evident that a change in one necessarily needs to be reflected in the other. Only then can big data drive policy, and policy drive big data, to create a virtuous cycle that accelerates socio-economic progress.

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