In this digital age, data is currency. Empirical evidence forms the cornerstone to implementing effective and efficient policies and eventually saving and improving lives. What then is the tool that helps us mint, analyse, and invest that currency? Statistics. Given India’s rich contribution to the field of statistics and mathematics, the country is at a clear advantage and it is imperative that we continue to build on this legacy to meet the evolving challenges of our data-rich society.
Recently, questions have been raised around the effectiveness and calibre of India’s data and statistics systems. However, two monumental initiatives, Digital India and the Digital Personal Data Protection (DPDP) law, have ushered India into an era of robust data opportunities. The Digital India programme, launched by Prime Minister Narendra Modi, created a vast data platform with immense potential for statisticians, economists, mathematicians, and data scientists. Secondly, the DPDP law, passed recently in Parliament, promises to safeguard personal data and promote responsible data utilisation.
In today’s data driven environment there is increasing demand for valuable insights, patterns and correlations that can guide research. Across the sectors, three pillars are critical for the robustness of data — reliable statistical design, data analytics and data quality.
Statistical design forms the bedrock for valid, reliable and generalised data results. While India has well-established study designs and sampling methods, periodic adaptations are necessary to align with evolving goals. Some issues to be considered are careful planning, sampling techniques, experimental design and randomisation. Government bodies must continually strengthen study designs and sampling techniques to enhance the validity and generalisability of our findings.
Data analytics has evolved from hypothesis-driven research to evidence-based analysis, and from small-scale computation to big data analytics and AI. This is indispensable for correct inferences in public policy building. Statistical analytical techniques such as regression, principal component analysis, cluster analysis and discriminant function analysis are not only applied in traditional statistical data analysis but also widely used in the field of big data analytics and AI, transforming the way we solve problems and make data driven decisions.
Wealth of data sources
Data quality, akin to refining crude oil into valuable products, is paramount. India’s wealth of data sources, including its robust statistical system, government agencies, NGOs, and the private sector, necessitates a robust data quality guideline system. Initiatives like the Data Quality Guidelines by ICMR-National Institute of Medical Statistics set the right course by emphasising that “good data beats opinions.” Yet, we must continually enhance our data governance ecosystem.
In conjunction with this, Indian is lucky to have statisticians whose prowess is unquestionable, and their role in shaping the nation’s data landscape is unparalleled. To fortify this capability and ensure the highest standards of quality in statistical products, it is imperative to empower the National Statistical Commission (NSC) by infusing it with more statistical domain experts. By augmenting its expertise, we can boost the much-needed quality assurance mechanism that instils confidence in data users across various sectors.
Coupled with this, if we can make diverse scientific project databases exchangeable among top organisations like the ICMR, Department of Science and Technology and Council of Scientific and Industrial Research and also strengthen the role of statisticians in ensuring robust data collection and analysis, we can take a step forward in rejuvenating our statistical ecosystem.
The writer served as the Director of ICMR-National Institute of Medical Statistics