Mistakes that most of the analysts do in their analytics

In the field of data science or analytical sciences, any solution can be potential to solve any problem. But, to derive such prospective solutions, the following are the top common mistakes in their practice. 

  1. Unable to apply common sense and subject matter expertise for providing solutions to any given problem using analytical tools and techniques.
  2. The results are tremendously biased towards models such as AI and ML (tools and techniques), but incompetent to realize the significance of the problem. 
  3. The results tend to ignore basic statistical sampling techniques, that will not have enough evidence to have inference.
  4. Analytical solutions are providing inadequate or fragmented solutions; it is due to ignoring root causes such as proper investigation, profound analysis of any designed problem.
  5. Funded projects results are dominant and influenced towards predetermined results and try to convince their investors. These results can mislead them to a great extent in the long run as they did not derive from the data analysis.
  6. The quality of data is being questioned in every analysis, as within the organizational structure have different functionalities will not be willing to share its data with the analytics or data science teams. (as they see the confidentiality is their high priority, but unable to understand data is taking part in the inside employees for the better results). 
  7. If research results are baseless analytical results will always danger. Baseless arguments incur enormous cost and also lead to many adverse effects on the organizations or systems.
  8. Analysis should provide the results based on pieces of evidence from data. In many instances, analysts have the policy to promote acceptable and positives only. The major mistake by many analysts is to translate the negative numbers as acceptable or ignore the negative numbers. 
  9. Analysts should never try to mislead the end-users by promoting predetermined results with anticipation of their personal growth and future aspirations.
  10. As an analytics expert, it’s their responsibility and integrity to provide the best achievable and acceptable results to their clients. Instead, they are anticipating their personal brand and their own image. 
  11. Finally, with the above said mistakes, clients will have improper, undermined and biased results regarding to their business; hence their business decisions are not proper to the markets and also lead to the crisis in their business management.

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Tags: dsc_analytics, dsc_tagged


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