Title

AWS re:Invent 2022 - How AI and ML unlock insights for financial services teams (PRT036)

Summary

Jed Doherty, VP of Platform Strategy at Dataiku, discusses the data journey in financial services before reaching AI and ML.
Financial organizations often start with data preparation and transformation, integrating disparate data sources.
The focus is on empowering analysts, data scientists, and data engineers to work together and understand the data.
The ultimate goal is to move towards predictive analytics, ensuring data accuracy, constant updates, and trustworthiness.
Financial services have a high ratio of employees working with data, necessitating the upskilling of the broader organization.
Dataiku provides a platform that enables data stitching, analytics, and the productionalization of workflows.
The platform helps financial organizations transition from legacy systems to modern data infrastructures like AWS.
Dataiku has successfully streamlined data processes for clients like Standard Charter and Aviva, reducing time and analyst involvement.
The platform also aims to move users from Excel to more scalable, reproducible, and governed data processes.
Dataiku supports a variety of coding languages and integrates with third-party solutions, offering a flexible and evolving toolset.

Financial organizations are often hesitant to adopt AI and ML without first establishing a solid data foundation.
Dataiku positions itself as a bridge between traditional data management and advanced analytics, emphasizing the importance of collaboration and trust.
The platform's ability to reduce the number of analysts needed for data maintenance and to speed up data transformation processes can lead to significant cost savings and efficiency gains.
Upskilling employees in data analytics can improve job satisfaction and retention, as well as increase the overall data literacy of the organization.
Dataiku's approach to integrating with existing systems and preparing for future technologies demonstrates the importance of flexibility in data management tools, especially for large financial institutions with complex legacy systems.
The transition from Excel to more advanced data tools reflects a broader industry trend towards scalable, reproducible, and governed data processes, which are essential for handling the increasing volume and complexity of data in the financial sector.