There are five reasons why analytics talent might not be competitive at the industry level:
1) The conjecture of graduates that data processing works the same as mentioned in their books. Real-world projects can be messy and imperfect. Students like me who want to enter into this field need a realistic and practical approach to data science.
2)The rate of hiring data scientists is expensive. Small industries find difficulty in competing for talent compared to large tech enterprises.
3) Companies are looking to hire an experienced and skillful data scientist, and new graduates lack that. Many companies are looking for impactful hires right away.
4) Difficulty in holding experienced data scientists in a competitive market.
5) The need for new hires to dig deep into statistics, machine learning, and cloud computing to handle big data.
Data science is an emerging field as enterprises need ways to turn their bulk of data into a beneficial asset. Increasing in the demand of data scientists in the industry and lucrative job opportunities in this field, students get captivated by it and ended up taking it as a career. This sign is good, but companies are expecting more than just bookish knowledge from new hires.
To tackle these challenges efficiently, companies can provide training to new hires, develop strategies for hiring, awareness of data science requirements as well as limitations, and finding ways to make experienced ones stay in the enterprise. Applying these alternatives, job seekers who are newcomers in the industry can be placed in an enterprise since most of them are looking for an experienced one.
Besides, establishing ethical culture, values, and incentives, enterprises can make employees stay for a long time. However, before looking for data scientists, the company should know it’s own data and how it will be useful if they hire them. The need for enterprises and job seekers who are new hires can both be satisfied by considering above information.