Srikanth
Economic inequality in India is quite blatant and claims the second position as the most unequal country globally, with millionaires controlling 54% of its wealth. Reported by New World Health, India is ranked as the 10th richest country in the world. The report says, an average Indian’s individual wealth is valued at $5, summing at 600 billion where he is comparatively poorer and lives a basic life. Further corroborating the report, data from Credit Suisse estimated that 53% of India’s wealth belongs to the wealthiest 1%. This economic inequality is one of the major impediments to India’s fiscal growth trajectory and responsible for the country’s bleak future growth outlook.
With technology seeping into every aspect of our lives, the country’s economy is no exception. With data science emerging as one of the most prevalent technologies, it is being used in a host of initiatives, with the aim of equal distribution of wealth. Furthermore, big data is set to positively influence the economy through industrial efficiency in every process.
Big Data is set to eventually take the lead in various business aspects such as research, sales, production and business planning, thereby leading to a new industrial revolution. The technology has some substantial opportunities and the capability to uncover profound insight to offer when explored. Given its intelligent capabilities, data is slowly being integrated into public sectors by private organizations and are becoming vital to decision making. With mobile phones and access to internet becoming the norm, the public sector itself spawns a mammoth amount of data in education, employment, agriculture, and manufacturing.
Data science is significantly aiding the government in becoming more efficient, detecting and minimizing fraud, maintaining transparency, boosting the economy and productivity. This is being achieved by public sector organizations collaborating with the private sector to bring in effective services and making it possible to respond swiftly and with more precision to the needs and requirements of the citizens.
Many national and international organizations, in collaboration with the government, are resorting to the application of big data analytics for across various sectors that benefit the society and improve the economy at a grassroots i.e. natural resource management, farming, fishing, and healthcare education. Underscoring the use case of predictive analytics in farming, there are mobile applications for farmers that offer powerful cloud-based predictive analytics, empowering them with information and insights to make well-informed farming decisions.
These apps are pre-loaded with exhaustive historical weather-related data to help farmers plan based on predictions. Smart sensors are also being deployed to measure ground data in real-time to better manage drought or flood conditions, which minimizes crop failures and improves yields. This is a direct example that contributes to better income opportunities for citizens.
Another data science use case was observed in fishery where data analytics was utilized to deliver information of the density of fish and location coordinates to fishermen. Not only does this boost the livelihood of fishermen, but it also improves their efficiency and profits.
Another contributing factor for economic inequality in India is the discrepancy in access to education. Educational deficiencies could undermine a nation’s productive capacity, led by a shortage of skilled workforce. To address the issue, several states in India have resorted to machine-learning based models to improve the educational ecosystem by analyzing and predicting dropout rates. These predictions have helped identify thousands of students who were likely to dropout, enabling the school authorities to take corrective measures and preventive actions towards student retention.
Other significant use cases of data analytics in government include big data analysis to counter issues of black money in India in 2016. Advanced analytics played a vital role in identifying data patterns when the government implemented demonetization. In this case the data supply chain would capture and store data at an unprecedented speed and scale. This could then be supplemented with data that is generated through digital transactions.
In conclusion, global development, in the recent past, lacked the intelligent guidance of data analytics. Poverty and economic inequality demanded intelligent data to tackle these issues, but the absence of proper infrastructure to collect the data related to public policy remained a major impediment. Today, with data science and analytics coupled with an improving data infrastructure, things are progressively changing. These changes only reinforce India’s commitment to attain the Sustainable Development by 2030.
This Article is contributed by By Roop Singh, Executive Director, India with ATCS
