data quality

Data quality is the measure of the accuracy, completeness, and reliability of data, which is critical for effective business operations, decision-making, and the successful implementation of AI systems.

Why it’s in the news: It is in the news now because poor data quality is identified as a primary obstacle to unlocking the value of AI and achieving strategic goals across finance, Africa, and Singapore.

Latest on data quality

• Why data quality dictates security operations success
• AI emerges as decision engine for finance, with governance and data quality key to unlocking value: KPMG
• AI is now finance's decision engine, but data quality is key: Report
• Agentic AI ambitions in Singapore run into legacy systems and data quality gaps
• Expert warns poor data quality may derail Africa’s AI ambitions
• Poor data quality is breaking AI ambitions: Anand Ramamoorthy, Director APAC Data Governance and Quality, Informatica, Salesforce
• Investors now treat ERP maturity as a proxy for AI governance, but nobody is asking what happens when the underlying data is too messy to train on.
• Better data still flows through models that regulators don't fully understand
• Enova is betting that better data—not AI—will decide which lenders survive the next credit cycle
• Data quality and assurance readiness remain critical barriers that organizations must address to extract maximum value from their AI investments.
• most finance teams are still building data infrastructure for reporting, not for accountability
• artificial intelligence models depend entirely on the accuracy of information fed into them

Connections

6 entities linked to data quality across the news graph.