As firms scale and tech stacks grow, data flowing from multiple systems into a lake and erroneous entry can create gaps or anomalies that limit the insights that can be drawn from the data. Analytics success often hinges on patching, integrating, and enriching data, but this can often be a time-consuming, manual process.

In this interactive workshop you will have the opportunity to level up your data patching and cleaning.

Collaborate with your peers to gain practical and technical insights into:

• Uncovering why incomplete or inconsistent connections between sources can create significant challenges with data quality, and how to efficiently find and fix the root cause
• Determining how far back you should clean or enrich your firm’s data to balance ROI and effort when deciding what data to trust, clean, or ignore
• Deciding which systems to clean first to create a few clean anchor projects to build best practices that can scale
• Exploring how to leverage automation and AI-driven validation to enhance data integrity and analytics through anomaly detection and mission information repair for stronger analytics outcomes and better data structure
• Establishing the risks of implementing these validation techniques given potential unreliability

CASE STUDY