AI and ML

India poor statistical data standards and its implications

Economic development is considered o be a strong pillar of estimating a national overall growth and direction. The grow thing trends and status are used to have an idea about the direction of growth and lay present in the implementations.

India has a presence of certain esteemed organization like NSSO, CSO, MOSPI, Geographical Survey of India. Forest Survey of India, etc. Which are looked at upon for data regarding specific purposes?

To get estimates of economy-related aspects like poverty, unemployment, inflation, etc to arrive at related conclusions and take decisions.

The data standards, however, face conterminal situations due to issues like:

  • Poor standards followed in research analysis.
  • Improper sample size selection.
  • Lack of skilled data – scientists and experts.
  • Lack of skill of advanced techniques (like SPSS, R, DBMS based software etc.)
  • Selection of wrong bases.
  • Involvement of contract-based employees to full fill the ‘ tick mark ’ approach.
  • Conflicts between organizations regarding data( eg NiTi Ayyog vs Cso )
  • Gaps between organizations and their platforms (online).

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Such licensing in the data and statistics organization directly impact the policymaking leading to error decision :

  • Errors may arise in allocating funds.
  • Distribution of funds.
  • Setting targets for budgets and schemes.
  • May lead to baseless conclusions.
  • Hamper organizations as well as Nation’s image.

Way forward :-

To ensure that erroneous data is avoided following measures must be taken :-

  • Set up a national policy on official statistics.
  • Ensure the implementation of defined standards and parameters.
  • Cross-functional training s of employees on the latest technologies to reduce skill gaps among researches.
  • Make use of international alliances for data-based learnings.

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India being a developing country has a lot to improve on its economic

Issues. It is high time that we improve on our data standards and parameters to arrive at optimum results. It is rightly said ‘you cannot improve what you can’t measure ‘, hence the need.

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