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Can data also suffer from bad health issue?

Welcome to the data-driven world! The popularity of data-driven is increasing in such a way that it is bringing the biggest challenge with it: finding reliable data needed for analytics. According to the New York Times article, based on the challenges of data wrangling, it has been noted that “Data scientists spend around 50 to 80 percent of their time in the doing unexciting labor of collecting and preparing unruly digital data before it can be explored for useful nuggets." Companies are struggling a lot with data quality and consistency. Today, in this article, we are discussing three revealing signs, which show data are dangerous and unhealthy in nature. Inconsistency: To work on any project, one needs to find the data in the right place. If any business has inconsistent data then it will results differently depending on the source of data, time of the data-pull, and the source of data-pull. This shows that the intra- data source consistency is as important as inter-data source consistency. Inaccessibility: If anyone will not be able to access the data at the right time, even if it is available, it is of no use. If data cannot be accessible on time it becomes outdated and useless. It is very important to extract the data from the right place and use it at the right time before it becomes outdated. Incompleteness: The problem becomes worse, when a person working on a project, found the right source of data at the right time and access it, but the information required for the project is not available. The company faces this kind of challenge when there are no identified responsible parties. How to deal with unhealthy data If the quality of data will not be improved then companies will not be able to achieve insights. Also, deriving business vision from unhealthy data can be dreadful for any organization. So, it’s crucial to improve the quality of data to protect the company from any data threat. To improve the quality of data and moving forward with trusting business decisions, it is critical to store data at the right place by creating and maintaining a tool through which one can define all data elements and specify validation rules for each data field. Most importantly, it should be clear, transparent and complete. Furthermore, we can implement these four important initiatives to resolve unhealthy data problems: correcting data in the source system, fixing the source system to correct data, accepting bad source data and fixing the related issues during the ETL phase, Application of precision identity.

Welcome to the data-driven world! 

The popularity of data-driven is increasing in such a way that it is bringing the biggest challenge with it: finding reliable data needed for analytics. 

According to the New York Times article, based on the challenges of data wrangling, it has been noted that “Data scientists spend around 50 to 80 percent of their time in the doing unexciting labor of collecting and preparing unruly digital data before it can be explored for useful nuggets." Companies are struggling a lot with data quality and consistency. 

Today, in this article, we are discussing three revealing signs, which show data are dangerous and unhealthy in nature.

  • Inconsistency: To work on any project, one needs to find the data in the right place. If any business has inconsistent data then it will results differently depending on the source of data, time of the data-pull, and the source of data-pull. This shows that the intra- data source consistency is as important as inter-data source consistency.
  • Inaccessibility: If anyone will not be able to access the data at the right time, even if it is available, it is of no use. If data cannot be accessible on time it becomes outdated and useless. It is very important to extract the data from the right place and use it at the right time before it becomes outdated. 
  • Incompleteness: The problem becomes worse, when a person working on a project, found the right source of data at the right time and access it, but the information required for the project is not available. The company faces this kind of challenge when there are no identified responsible parties. 

How to deal with unhealthy data

If the quality of data will not be improved then companies will not be able to achieve insights. Also, deriving business vision from unhealthy data can be dreadful for any organization. So, it’s crucial to improve the quality of data to protect the company from any data threat. 

To improve the quality of data and moving forward with trusting business decisions, it is critical to store data at the right place by creating and maintaining a tool through which one can define all data elements and specify validation rules for each data field. Most importantly, it should be clear, transparent and complete.

Furthermore, we can implement these four important initiatives to resolve unhealthy data problems:

  • correcting data in the source system, 
  • fixing the source system to correct data, 
  • accepting bad source data and fixing the related issues during the ETL phase, 
  • Application of precision identity.

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