While Data Science is a very lucrative career option, there are also various disadvantages to this field. In order to understand the full picture of Data Science, we must also know the limitations of Data Science. Some of them are as follows:
1. Data Science is Blurry Term
Data Science is a very general term and does not have a definite definition. While it has become a buzzword, it is very hard to write down the exact meaning of a Data Scientist. A Data Scientist’s specific role depends on the field that the company is specializing in.
While some people have described Data Science to be the fourth paradigm of Science, few critics have called it a mere rebranding of Statistics.
2. Mastering Data Science is near to impossible
Being a mixture of many fields, Data Science stems from Statistics, Computer Science and Mathematics. It is far from possible to master each field and be equivalently expert in all of them.
While many online courses have been trying to fill the skill-gap that the data science industry is facing, it is still not possible to be proficient at it considering the immensity of the field.
A person with a background in Statistics may not be able to master Computer Science on short notice in order to become a proficient Data Scientist. Therefore, it is an ever-changing, dynamic field that requires the person to keep learning the various avenues of Data Science.
3. Large Amount of Domain Knowledge Required
Another disadvantage of Data Science is its dependency on Domain Knowledge. A person with a considerable background in Statistics and Computer Science will find it difficult to solve Data Science problem without its background knowledge.
The same holds true for its vice-versa. For example, A health-care industry working on an analysis of genomic sequences will require a suitable employee with some knowledge of genetics and molecular biology.
This allows the Data Scientists to make calculated decisions in order to assist the company. However, it becomes difficult for a Data Scientist from a different background to acquire specific domain knowledge. This also makes it difficult to migrate from one industry to another.
4. Arbitrary Data May Yield Unexpected Results
A Data Scientist analyzes the data and makes careful predictions in order to facilitate the decision-making process. Many times, the data provided is arbitrary and does not yield expected results. This can also fail due to weak management and poor utilization of resources.
5. Problem of Data Privacy
For many industries, data is their fuel. Data Scientists help companies make data-driven decisions. However, the data utilized in the process may breach the privacy of customers.
The personal data of clients are visible to the parent company and may at times cause data leaks due to lapse in security. The ethical issues regarding preservation of data-privacy and its usage have been a concern for many industries.