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National Data Quality Forum (NDQF)

Topic covered:

  1. Statutory, regulatory and various quasi-judicial bodies.

 

National Data Quality Forum (NDQF)

 

What to study?

For prelims: NDQF- features, need and launched by?

For mains: The need for data management, challenges present and measures to address them.

 

Context: Indian Council of Medical Research (ICMR)’s National Institute for Medical Statistics (ICMR-NIMS), in partnership with Population Council, has launched the National Data Quality Forum (NDQF).

 

Aims:

  • The NDQF aims at establishing protocols and good practices when dealing with data collection, storage, use and dissemination that can be applied to health and demographic data, as well as replicated across industries and sectors.
  • The NDQF aims to do brainstorming, piloting and employ advanced modeling techniques in artificial intelligence (AI), machine learning and big data analytics along with using technology-based solutions to improve data quality.

 

Roles and functions:
NDQF will integrate learnings from scientific and evidence-based initiatives and guide actions through periodic workshops and conferences.

It will fetch quality data in upcoming health studies and surveys such as National Family Health Survey (NFHS).

 

Benefits and significance:

Its activities will help establish protocols and good practices of data collection, storage, use and dissemination that can be applied to health and demographic data, as well as replicated across industries and sectors noted a release issued by ICMR.

 

Need:

Data on health and demographics in India is plagued by incomplete information, overestimation, and under- and over-reporting that lead to hindrance in policy planning.

 

Challenges present:

  • lack of comparability and poor usability of national level data sources.
  • discordance between system and survey level estimates.
  • increased questionnaire length and questions on socially restricted conversation topics that translate to poor data quality.
  • age-reporting errors or non-response and intentional skipping of questions.
  • underreporting due to subjective question interpretation and incompleteness.
  • paucity of data to generate reliable estimates on mortality as major barriers to quality data.

 

Sources: the Hindu.