64th ISI World Statistics Congress - Ottawa, Canada

64th ISI World Statistics Congress - Ottawa, Canada

Modernization of Statistical Operations in post COVID Era, opportunities and challenges


64th ISI World Statistics Congress - Ottawa, Canada

Format: IPS Abstract

Session: IPS 443 - Changing the Data Collection Paradigm: Lessons Learned from COVID 19

Wednesday 19 July 2 p.m. - 3:40 p.m. (Canada/Eastern)


Modernization of statistical operations is an evolutionary transition from traditional to modern system. COVID-19 crisis challenged National Statistics Offices on maintaining production of timely and quality key statistical out puts. This magnifies the importance of data revolution to meet the data needs.
Most developing countries used to conduct paper base face to face data collection. The COVID-19 pandemic forced countries to review the statistical operations. Data collection and transfer using electronic devices such as tablets, phone or computers is adopted in post pandemic era. Trainings for field staff and NSS members were also provided virtually. Electronic dissemination using website, CD, and also implementing different open data dissemination platforms is also exercised. This forced NSO to improve the infrastructure for statistical operations.
The transition from paper base to electronic data collection and remote training saves time and cost and improves quality. However, the new system also creates challenges. Some of the challenges are data lose, data storage problem, data transfer challenge, difficulty to easily implement data quality control mechanisms at different level, electric power problem, limited IT infrastructure and budget constraints.
Effective mechanisms should be implemented to sustain the new data collection, transfer and dissemination system. Continuous capacity building and sustainable budget support to maintain the system is required. Better coordination mechanism in the NSS to facilitate data exchange is required. NSO should also integrate other data sources like administrative data and GIS information to produce more disaggregated and timely results. Statistical modeling should also be implemented to make use of different data sources and produce timely, quality disaggregated data.