Improving policymaking with Big Data
Tucked away in the statement put out by NITI Aayog after the third meeting of its governing council on Sunday was an innocuous phrase. The Aayog, the statement said, has partnered with top-ranking institutions “to nurture evidence-based policymaking”. That seems like it should be a given. Surely, all policy is or should be based on solid data. But it became a guiding philosophy of policymaking globally only in the 1990s. It has evolved since then as technological advances have allowed more data to be captured and analysed. To say evidence-based policymaking (EBPM) today is to say policymaking guided by Big Data.
Governance in India, starting with the United Progressive Alliance and continuing into the Narendra Modi administration, has been trending in this direction. The Aadhaar programme, with its hundreds of millions of data points that can be mined for policy formulation and implementation, is a prime example. So is the Centre’s push, kick-started by the currency-swap initiative, to reduce the size of the shadow economy and widen the tax base. The actual amount of black money netted by the initiative might still be a matter of conjecture and debate, but the entire exercise has generated data that can make it tougher for individuals to evade taxes in the future. Geo-tagging of Mahatma Gandhi National Rural Employment Guarantee Scheme assets is another case in point.
This is a natural evolution of EBPM. The spread of the Internet and the rise of social media and the Internet of Things mean that the volume of data we generate is growing exponentially—currently about 2.5 quintillion bytes daily. This is a goldmine for the private sector and governments alike. Online searches can be trawled for data that helps predict disease outbreaks. Cellphone data can help direct relief efforts in the aftermath of a natural disaster. Power-usage data can be analysed to optimize energy grids and plant power generation; discoms in India are already using data from last-mile sensors to implement measures for cutting down aggregated technical and commercial losses.
The uses can range from the national—using healthcare data to revamp the public health system—to the local, where the massive amounts of data generated by cities, from traffic signals to public transport usage, can be used to improve infrastructure and transport systems as Singapore has done. But utilizing Big Data effectively in this fashion will mean keeping a few factors in mind.
First, two of the defining characteristics of Big Data analytics are the volume and veracity of data. The computing concept of garbage in, garbage out holds true here. Infrastructure in India for efficient data collection and management is lacking; this must be strengthened.
