Successful introduction, implementation, and scaling of automation and generative AI rely heavily on effective data management practices within organizations. Robust data management ensures the availability, quality, integrity, and security of data, providing a solid foundation for automation initiatives. By leveraging well-managed data, organizations can enhance the accuracy, efficiency, and effectiveness of their automation and generative AI programs. Furthermore, it enables them to avoid the “garbage in, garbage out” problems that often get in the way of automation teams having “good and clean data” to work with when developing and rolling out automation initiatives.

In this session, topics of discussion will include, yet will not be limited to:

• Defining data ownership, establishing data quality standards, and implementing security protocols to safeguard sensitive finance operations information and data
• Placing a strong emphasis on data security and compliance by implementing encryption, access controls, and audit trails to protect sensitive data
• Developing comprehensive data management practices that adhere to regulatory requirements and compliance standards, mitigating risks associated with data breaches, privacy violations, and non-compliance

IA Maturity Level: All & Various

Lines of Business Functional Focus: Technology, Operations & Business-Wide