This session will include a moderated Q&A featuring questions from the live audience.
Self-service is an important step in your BI and analytics program, but it is not the endgame. Continuous delivery of data-driven impact requires growing beyond self-service foundations. Advanced stages of BI and analytics maturity operationalize BI and analytics to become part of the fabric of the business, aligned with data monetization efforts.
How much does your analytics program focus on self-service capabilities versus helping increase revenues and/or decrease costs?
In recent years organizations have put a great deal of focus and investment in self-service capabilities to empower analysts to uncover important insights. There has been some success with this approach, but when we step back and consider what we expect data and analytics programs to achieve, is self-service getting us there?
This discussion will offer candid insights from challenging self-service analytics programs—programs where analytics teams try to balance:
- High-quality data assets versus agility for exploration and discovery
- Providing flexibility versus ensuring compliance with evolving regulations
- Assisting AI and analytics teams with productivity challenges from data quality issues
Learn how to develop a balanced analytics program that ensures investments in self-service are reasonable and in alignment with broader data monetization efforts.