The rise of the data scientist
Machine learning continues to be a core component of financial organizations' business strategy, with expected significant investment in this area. However, majority of firms stated the need to implement a data strategy combined with strong talent were critical to its future success. While ML is currently a horizontal capability, business units will increasingly rely on it to drive competitive advantage and manage risk.
Firms in North America are ahead of other regions like EMEA and APAC in terms of ML adoption, but unforeseen challenges and complications, due to COVID-19, caused major disruptions to models and forecasts. Meanwhile firms in Latin America were able to embrace the disruption, utilizing their agility to transition to digital ML models -- some within two months. Something that would take more established firms years to accomplish.
Poor data quality and data availability continue to be the biggest barriers to successful adoption and deployment of ML. Extracting value and better quality will be most important amongst multiple drivers of ML in the next 12 months.
Data science teams are now the key final decision makers for trial and procurement purchase. However 56% of data scientists work in different areas of their companies supporting business functions rather than in a central team. The growth in numbers of roles is anticipated to increase, but not at the same extent as recent years.