Context

The Ministry of Statistics and Programme Implementation (MoSPI) has proposed to set up a 'National Data Warehouse' with a view to leveraging big data analytical tools to further improve the quality of macro-economic aggregates.

About

More on news:

• Over a period of time, there have been increasing demands on the statistical system for the production of relevant and quality statistics. MoSPI has been accommodating these demands by optimising the available resources and use of technology.
• As in any system, the advent of technology necessitates reforms in statistical processes and products with an aim to synergise the existing resources so that the system remains responsive.
• The recent step for the merger of CSO and NSSO was aimed at leveraging the strengths of the two organisations so that it can meet the increasing demands.
• MoSPI has been criticised in some sections for the quality of macro-economic data.
• As per MoSPI, revision in GDP estimates occur when data coverage from administrative sources improves over time and these improvements get well documented. Consequently, the initial estimates of GDP tend to be conservative.
• To improve GDP estimation it would require concomitant changes in the sectoral data flows and associated regulatory framework in the data source agencies to facilitate the use of more macro modelling techniques.
• MoSPI has also proposed to establish a National Data Warehouse on Official Statistics, where technology will be leveraged for using Big Data Analytical tools for further improving the quality of macro-economic aggregates.
• Efforts are also on to evolve a legislative framework under which the National Statistical Commission (NSC) may function with independence and give holistic guidance for improving the national statistical system.

Way Forward

• The reforms being undertaken in MoSPI will lead to better data sets and better estimates in future, and will be duly deliberated on by the ACNAS during the Base Year revision.
• As all these reforms are an ongoing process, it is important that the readers and users understand and appreciate the limitations of data and the challenges in estimation.