Poor Data Quality Undermines AI Success in Global Organizations

As organizations worldwide accelerate artificial intelligence adoption, many fail to recognize the critical importance of properly curating and preparing their data first, according to Dave Curtis, Chief Technology Officer at RobobAI. Curtis identifies data quality as a primary challenge facing global enterprises, noting that most AI initiatives fail due to unexpected costs associated with collecting and rectifying data issues.

Accurate and complete data forms the foundation of all analytics supporting business decisions, Curtis explains. Downstream applications including AI predictive models require substantial historical information to effectively forecast future outcomes. Many companies consistently report poor data quality resulting from multiple truth sources, lack of automated validation, and manual entry errors, creating significant barriers to data-driven decision making.

Properly implemented automation tools can reduce business user workloads, decrease turnaround times, and enable underlying data to be more readily utilized across various business applications including AI and machine learning use cases. RobobAI observes increasing trial uses of AI not for predictive modeling but for addressing data deficiencies in ways that substantially reduce manual effort requirements.

Curtis emphasizes the importance of maintaining data integrity after correction, noting that many organizations employ entire teams exclusively dedicated to data fixes. Companies are exploring opportunities to demonstrate return on investment by reducing or eliminating this effort through improved data management practices.

RobobAI platforms employ AI techniques including natural language processing and clustering to preprocess data, identify and reduce duplication, and enhance incomplete records with missing attributes from alternative sources. While organizations focus heavily on analytics and AI implementation, Curtis stresses that many neglect examining their foundational data infrastructure first.

Companies must consider complete end-to-end models when building business cases and understanding potential returns, ensuring data quality receives appropriate attention before advanced analytical applications. The relationship between proper data preparation and successful AI implementation remains crucial for organizations seeking to leverage artificial intelligence effectively across their operations.