why data science projects gets faild ?

Lack of understanding of business problems

One of the main reasons why AI / machine learning projects fail is because there is a lack of understanding of business problems or opportunities. This can be due to a number of factors, such as the inexperience of the team, unrealistic expectations, or a lack of domain knowledge

Lack of data

Another reason AI / machine learning projects fail is because of a lack of data. This can be due to a number of factors, such as data being too expensive to acquire, data being unavailable, or privacy concerns. In many cases, it is necessary to have a large amount of data in order to train a machine learning model. Without enough data, it is difficult to build a model that generalizes well and performs well on unseen data. Many times, the analytical solutions are built with the data we have. This results in data bias.

In addition to needing a large amount of data, the data must also be of high quality. Poor data quality can lead to inaccurate results and cause machine learning models to perform poorly. Data quality issues can be caused by a number of factors, such as incorrect labels, incorrect values, or missing data.

Lack of resources

AI / machine learning projects can also fail due to a lack of resources. This can be due to a number of factors, such as some of the following:

  • Insufficient staffing: An analytics team requires staff members product managers, data visualization experts, analytics engineers, data scientists. At the minimum, you will need good product managers and great data scientists to ensure success of AI / ML projects.
  • Inadequate infrastructure: Due to the need of training complex models, one needs expensive infrastructure. The organizations which are unable to invest in infrastructure fail to enable their staff members to build great models. This is where cloud based infrastructure comes to rescue.

  • Inadequate funding: Another reason for AI / ML projects failure is inadequate funding. Many times, the AI / ML projects are not given enough budget to be successful.

THESE ARE THE MAJOUR THREE PROBLEMS FOR AI/ML PROJECTS GETS FAILD.

ANOTHER PROBLEMS,

  • Not having the Right Data. I'll start with the most obvious one. 
  • Not having the Right Talent .
  • Solving the Wrong Problem.
  • Not Deploying Value. .
  • Don't Miss Out on the Latest. .
  • Thinking Deployment is the Last Step.
  • Applying the Wrong (or No) Process.
  • Forgetting Ethics.

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