Commercial Leaders should not miss out on these trends

In a world where the field of Data Science seems to be growing at the speed of light, commercial leaders should not miss out on these Data Science trends. The article enlists the top 5 Data Science trends for commercial leaders to follow.

Real-time insights

Next-best-action systems and recommendations based on analytics will remain essential. These systems use AI and ML approaches to assess the factors that influence consumer behavior, optimize the conditions of user contact, and create recommendations for a specific client that is nearly real-time.

Natural Language Processing (NLP)

NLP is a rapidly expanding field with a staggering array of practical uses. Essentially, it organizes useful data from unstructured biomedical literature. While several life sciences businesses have already integrated NLP into their core business processes, we anticipate this trend to gain momentum over the next few months since NLP plays a key role in a number of AI use cases that support downstream functions.

Low-code, no-code development tools

Low-code, no-code tools have an impact on other industries, and they can have a similar impact in the life sciences. Applications of data science are always developing, bringing about new efficiencies that will support development and innovation in business operations. By monitoring and responding to these trends, life sciences businesses will be able to get pertinent, business-critical insights quickly, adapt effectively to competition and market changes, create considerable operational efficiencies at scale, and maintain better results.

Convergence

The cornerstones of today's digital world are artificial intelligence (AI), cloud computing, the internet of things (IoT), and ultrafast networks like 5G, and data is the fuel that powers them all. These technologies represent some of the newest developments in data science. These technologies are far more powerful when used together than when used alone. By utilizing artificial intelligence, smart homes, industries, and cities can now be built, allowing IoT devices to function as intelligently as possible without the need for human interaction.

Industrialized Machine Learning (ML)

This is when the importance of ML industrialization becomes clear. In order to advance ML models beyond the pilot stage and enable businesses to scale them successfully and produce insights in time to support business decisions when they are most likely to have an impact, ML industrialization lowers the barriers to generating and operationalizing ML models.