In part three, we focused on:
• Common data management and normalization challenges
• Common barriers to efficient data governance
• Ensuring data quality and measuring its effectiveness
• Why a full data lineage is critical for transparency
• Fostering accountability and data reliability
• Enabling data collaboration and inclusion
• Collating, cleansing, enriching, and managing enterprise data from virtually any source
• How a well-defined reference architecture sets the stage for efficiency and control