The Role of Data Scientists: Challenges and Opportunities
In 2012, the role of data scientist was declared the “sexiest job of the 21st century” by Harvard Business Review. Fast forward to today, data scientists have become more mainstream and vital in the age of artificial intelligence and machine learning. However, the job role has evolved, presenting both opportunities and challenges.
According to authors Thomas H. Davenport and DJ Patil, the job has become better institutionalized, with a redefined scope and advancements in technology. Non-technical expertise, such as ethics and change management, has also become more important. Despite these advancements, data scientists still spend a significant amount of time cleaning and wrangling data.
One of the major challenges facing data scientists is the lack of a data-driven culture in many organizations. Despite being hired and paid well, data scientists often struggle to make a difference due to the reluctance of decision-makers to act on their recommendations. This can lead to frustration and high turnover among data scientists.
A survey of analytics professionals conducted by Rexer Analytics found that only 22% of data scientists say their initiatives usually make it to deployment. This lack of deployment is attributed to various factors, including decision-makers’ reluctance to approve changes, lack of proactive planning, and technical hurdles in implementing models.
The interaction between business and data science teams is crucial for the success of data science projects. However, only 34% of data scientists say that project objectives are well-defined before they start. Additionally, less than half of managers and decision-makers are knowledgeable enough to make informed decisions about model deployment.
To address these challenges, data professionals and business leaders need to have greater visibility into how machine learning (ML) projects will improve operations and deliver value. It is essential to plan ML operationalization in detail and measure performance based on business metrics such as ROI.
Despite these challenges, the demand for data science skills continues to grow. Data scientists are hard to find, with 40% of respondents expressing concerns about talent shortages within their organizations. To address this, organizations are increasing internal training and working with universities to promote interest in data science.
Overall, data scientist remains a rewarding and promising career path. The satisfaction levels among corporate data scientists have increased, with more organizations recognizing the value of data science. By addressing the challenges and maximizing the opportunities, data scientists can continue to drive innovation and insights in the ever-evolving world of data.
