Data science and AI gained significant attention in 2023, with a particular focus on the rise of relational AI. Looking ahead to 2024, the industry is poised to maintain its prominence through various key developments that will impact companies. Here is a breakdown of the anticipated changes and their implications:
1. Focus on Delivering Value with Conceptual AI:
While relational AI has captured widespread interest, the crucial question remains – does it truly deliver tangible value to businesses? Despite the enthusiasm surrounding these technologies, research indicates that the actual financial benefits are yet to be fully realized. Companies are still in the experimental phase, with only a small percentage transitioning to production use. The deployment of relational AI at scale will require substantial investments, organizational adjustments, and reskilling efforts to maximize its potential impact.
2. Evolution of Data Science towards Business Focus:
Data science is transitioning from being perceived as a craft to a more structured and business-oriented discipline. Companies are investing in platforms, processes, and tools like MLOps systems to accelerate model generation and deployment. The emphasis is on increasing productivity, reusing existing data sets, and enhancing the efficiency of data model creation through automation.
3. Rise of Data Products:
The concept of data products, encompassing information, insights, and AI within software offerings, is gaining traction among organizations. Data product management is becoming essential for delivering value to both internal and external stakeholders. Companies are exploring different perspectives on data products, with a focus on integrating analytics and AI capabilities to drive innovation and enhance decision-making processes.
4. Transformation of Data Science Roles:
The traditional role of data scientists is evolving as new specialized positions emerge to handle various aspects of data science projects. The emergence of resident data scientists, machine learning researchers, translators, and data product managers reflects a shift towards a more collaborative and diversified approach to data science. Automation tools like AutoML are streamlining tasks and reshaping the landscape of data science roles.
5. Integration of Data and Technology Leadership:
The roles of data and analytics executives are being integrated into broader technology and digital transformation roles overseen by a centralized “supertech leader.” This shift towards a more unified approach to technology and data governance aims to enhance collaboration, streamline decision-making processes, and drive organizational alignment towards data-driven strategies.
In conclusion, the data science and AI landscape in 2024 will be characterized by a focus on delivering value, accelerating data science processes, embracing data product management, redefining data science roles, and integrating data and technology leadership. These changes are poised to reshape the industry and drive innovation, ultimately influencing how companies leverage data and technology to achieve their business objectives.
