Emerging Data Science Movements in the Imminent Half Decade
In the coming years, the field of data science is poised for significant transformations, driven by technological advancements, regulatory changes, and evolving market needs. Here are some key trends shaping the future of this dynamic industry:
1. **Open-Source Machine Learning Frameworks**: The widespread adoption of open-source machine learning frameworks like TensorFlow and PyTorch will drive platform convergence, encouraging enterprises to adopt vendor-managed platforms and boost productivity.
2. **Regulatory Compliance and Governance**: Stricter model-governance regulations, such as the EU AI Act, will push companies towards managed platforms, with implications for North America and APAC.
3. **Hybrid Data Fabric and Edge-to-Cloud Integration**: The adoption of edge-to-cloud data fabrics will enable hybrid data science platforms, particularly in manufacturing sectors, facilitating more efficient data processing and analysis across different environments.
4. **Expansion of Unstructured Data Analytics**: The explosion of unstructured IoT and video data will necessitate scalable feature stores, a focus area especially pronounced in APAC manufacturing hubs.
5. **Integration with AI and Big Data in Healthcare**: Big data analytics in healthcare will continue to grow, leveraging AI, machine learning, and deep learning for disease prediction, personalized treatment, and clinical decision support.
6. **Advanced AI Technologies**: The adoption of advanced AI technologies, such as generative AI and large language models, is expected to increase significantly across various industries, including the integration of AI in data science platforms to enhance automation and innovation.
These trends will shape the future of data science by enhancing efficiency, improving governance, and expanding the scope of data analytics applications.
In addition to these changes, the freelance route for Data Scientists could become a serious job path, with the potential to command high salaries. The Data Science Creator economy, where Data Scientists create art, write, and consult with their brand name, is growing and could become a realistic career path for many.
Moreover, Machine Learning is expected to be widely adopted in industries outside of technology, such as healthcare, law, manufacturing, agriculture, and more. Data-centric modeling will be a significant trend, with a focus on iterating on data and hyperparameters instead of just the model.
As society starts to record, collect, and store data in new ways, the data skills of tomorrow will become quite specialized and difficult. Understanding motivations, contributing factors, varied personalities, and how to influence those in power will be required of the highest skilled Data Scientists. The function of Data Scientists will need to shift from a focus on mathematical complexity to making actual decisions that drive real-world impact.
[1] Source: Gartner, IDC, McKinsey & Company [2] Source: McKinsey & Company [3] Source: Deloitte, Gartner
Data-and-cloud-computing technologies will play a crucial role in the shift towards managed platforms for machine learning, as enterprise productivity is boosted by the adoption of open-source frameworks like TensorFlow and PyTorch. Artificial-intelligence, particularly advanced AI technologies such as generative AI and large language models, will be increasingly integrated across various industries, including data science platforms, to enhance automation and innovation.
The freelance Data Scientist route is growing, with the potential to command high salaries, as the Data Science Creator economy expands, allowing Data Scientists to create art, write, and consult under their brand names. Moreover, the data skills of tomorrow will require specialized understanding of various factors influencing decision-making beyond mathematical complexity, as society collects and stores data in new ways.
