Data scientists are considered to be one of the best tech jobs right with the best career opportunities. In recent years, the position has become one of the most coveted across several industries.
Data scientists possess the necessary skills in coding, big data analysis, statistics, machine learning, natural language processing, data manipulation, exploratory data analysis and many more.
With regards to soft skills; effective communication, collaboration as well as a strong educational background are vital for a data scientist’s success. However, like pretty much everything else in the technological community, data science is changing and evolving with technology and making their position even more relevant for businesses today.
As a result, it becomes important to help businesses understand the changing role of data scientists and help them prepare for the future. Here are a few key factors influencing the future of the data science industry.
1. Making data actionable for data science
Inadequate or poorly prepared data is one of the biggest challenges to a data scientist’s success. In order to increase the output of data science projects and decrease failures, CIOs and CDOs need to focus on improving the quality of data as well as providing data that is updated and relevant to current projects and is actionable to data science teams.
2. Shortage of data science talent
Data science is one of the career segments that is experiencing the highest growth in terms of new graduates, the need is far surpassing the available supply. One possible solution is to increase the hiring process while still searching for alternative means for improving the data science process and democratizing access to data science for similarly skilled professionals like BI and analytics. This is an area where introducing automation in data science will likely have the most impact.
3. Accelerating “time to value”
As data science is a largely iterative process. It deals with creating a ‘hypothesis’ and developing tests for it. This approach goes back and forth and involves several experts ranging from data scientists to experts in particular subject matters as well as data analysts. Businesses need to look for ways of accelerating their data science processes to make this approach of ‘try, test and repeat’ more fast and predictable.
4. Transparency for business users
A limitation in the adoption of data science applications is the lack of trust on the part of business users. On the one hand, machine learning models can be revolutionary for many businesses but business users find it difficult to trust processes that they themselves do not completely understand. Data science must be incorporated into ML models to make them easier to understand and explain to business users as well as emphasizing trust and transparency.
5. Improving operationalization
One particular aspect limiting the growth of data science adoption is how difficult it can be to operationalize. Models that give positive results and work well in test scenarios do not work as well in real production environments. Sometimes, after a model has been deployed successfully, continuing changes and growth in production data can negatively impact models over time. This creates the need for an effective way for fine-tuning ML models, even after deployment, which is a crucial part of the data science process.
