The pandemic in 2020 led to a heightened awareness among businesses of the significance of AI and Data Science, resulting in a surge in its adoption. Companies invested more in this field and there was a rise in the number of data science jobs available. Although there was a temporary dip in the median salaries of analytics professionals early in the year, the trend has since rebounded and is expected to continue in the coming years. The limitations of data science teams from development to deployment were highlighted during the pandemic, leading to a focus on streamlining and scaling machine learning models through structured frameworks.
The industry recognized the need for a wider range of roles within the field, including niche positions such as data engineers. This shift has led to an evolution in education, with more specialized courses and formal certifications becoming available. Data engineers will play a crucial role in establishing data management systems and improving data access and pipelines. Organizations will be rethinking their data strategies to align with these changes. The use of large language models and advanced algorithms will become widespread, even among smaller companies.
The past year also brought to light the controversies surrounding big tech and the use of biased or unethical algorithms. The accountability of organizations that build AI and ML models has become a pressing issue, and Western countries have taken steps to address this. However, the field of Ethical or Responsible AI is still in its infancy and is expected to grow in 2022 with the hiring of AI ethicists and the integration of third-party auditing into the modeling process. Finally, the importance of localizing AI and ML has been recognized, as training models with local data can result in improved customer engagement and better business outcomes.
