The volume of data available to enterprises and the tools used to assess it have increased significantly. And hiring people with data skills has also risen along with the adoption of data-driven tactics. That is, a company can reap more benefits from having a stronger data team, Analytics Insight writes.

Jason Davis is CEO and co-founder of Simon Data, an information platform that empowers businesses everywhere to offer a personalized, data-driven user experience.

He predicts that data scientists will need to specialize in the following three areas: business and market analysts, artificial intelligence (AI) and machine learning technologies, and infrastructure, as work moves away from the generalist approach and technology capacity increases.

Bridging the gap between business and data
According to Davis, business and market analysts will be the ones to bridge the gap between the business and data departments. People in marketing teams will be given the tools to become more analytical in their work as data and marketing tools are more widely used.

Some tasks currently handled by data teams will be transferred to business teams. According to Davis, technology is enabling people who have some degree of technical background to move to a more technical step.

Building a data science infrastructure
The people who build the infrastructure and clean the data will fulfill another function of the data scientists that Davis believes will emerge. That demand is significant and probably won’t go away very soon. Of course, all three categories of data scientists must possess certain capabilities to maximize their effectiveness and success.

According to Davis, good communication, working with business teams, and solving the right problems are necessary for effective data science.

Specialization in AI and machine learning technologies
According to Davis, specialization in artificial intelligence and machine learning is sure to become another career path in data science. He also predicts that programs like ChatGPT will generate a hunger for anyone proficient in building neural networks and doing rigorous AI research and machine learning engineering.

People with years of experience will be a highly sought-after commodity.

Real-world applications of data science
Davis urges data scientists to focus on the real-world applications of data science that would allow them to succeed, not just the theoretical ones. Davis has personally witnessed the industry’s radical transformation, and as a result, he expects that today’s data scientists will be more focused on real-world applications.