What are the Challenges faced by Data science Professionals?

Take a look at the top challenges faced by data science professionals with the growing importance of data science in the age of AI

As a growing number of businesses acknowledge the importance of data science, data science professionals seeking to master the domain, face manifold challenges.

Data Security

As enterprises increasingly transition into cloud data management, the risk of cyberattacks has become inevitable. If not harnessed the power of data science correctly, it can harm companies in terms of both financial status and organizational reputation. To cope with this, organizations should utilize advanced AI-powered security platforms and incorporate additional security checks to safeguard their data.

Misconceptions Over the Role

Organizations expect their data scientist to be proficient in all trades, from cleaning to retrieving data and building models to conduct analysis. However, this is very challenging as it requires a collaborative approach, and to function effectively, tasks need to be distributed among individuals about data preparation, model building, data analysis, and more.

Deficiency of Skilled Talent

Finding the right talent or people with germane skills and knowledge to support businesses is very crucial. People with data science skills, who understand business processes and have domain expertise can help guide a company to the future which is a top priority for enterprises today.

Problem Identification

Analyzing a problem to identify it accurately for designing a better solution and defining each aspect of it is a major challenge for data science professionals. Most data scientists attempt a mechanical approach by beginning their work on data and tools without getting a clear understanding of the business requirement from the client.