Trusted Data, Powerful AI: Driving Better AI Outcomes through Data Quality and Governance

Get trusted AI outcomes by ensuring optimal data quality, robust governance, and data observability.

With its capacity to analyze massive datasets and streamline complex processes, Artificial Intelligence (AI) can transform businesses across industries. However, AI’s effectiveness is directly tied to the quality of the data it processes. It’s the ultimate “Garbage in/Garbage out” technology: When AI training models are given quality data, they can make useful decisions. However, if the data is poor quality, if it is inaccurate, outdated, incomplete, inconsistent, irrelevant, biased, or redundant, AI results will be wrong, leading to bad experiences for customers and business partners, delayed action, diminished revenue, higher risk, and higher costs.

Historically, poor data quality has been addressed with a reactive approach; when there’s a problem, someone does a root cause analysis to determine the cause, fixes it, and puts processes or rules in place to stop it from happening again. With AI, the damage is done as soon as the model uses bad data. In other words, putting GenAI on top of poor data will simply give you the wrong answer faster, and the damage will be harder to undo.

When AI models are given quality data, businesses enjoy increased efficiencies, cost savings, improved regulatory compliance, customer engagement and satisfaction, and reduced output bias.

Though AI can significantly improve every aspect of business, only 4% of organizations say their data is AI-ready. Let’s explore the data quality fundamentals you need to verify your data is ready to support your AI initiatives.