GenAI and data quality: Paving the path to AI success

Generative AI (GenAI) is transforming the business landscape by automating tasks, analyzing large volumes of data, and uncovering insights that once may have been difficult to perceive. The excitement surrounding AI’s potential is palpable, with many companies now fully embracing these technologies, and exploring GenAI’s potential to enhance productivity and decision-making across the areas of compliance, third party risk management and sales and marketing.

And as businesses explore the power of GenAI, it’s critical that its outputs are reliable, traceable, and explainable. When done right, GenAI has the potential to significantly increase the value, foundational importance, and critical insight that data offers, to unlock new opportunities, drive greater impact, and help enable alignment between data-driven decisions and business goals.
 

The symbiotic relationship between GenAI and data quality 

With GenAI emerging as a transformative force in artificial intelligence, its success hinges on the quality of the underlying data. High-quality data plays a critical role in enabling GenAI models to produce more accurate, reliable, and actionable outputs; without high-quality, well-structured data, even the most advanced AI models can produce inaccurate outputs, potentially leading to issues such as regulatory non-compliance and reputational damage. 

Effective data governance and real-time management of the data estate feeding GenAI models can help improve validation methods, enabling more reliable AI-supported decision-making. 

As GenAI continues to grow into an integral tool for effective decision-making across risk management and wider business strategies, maintaining high-quality data — namely, accurate and complete data — becomes increasingly critical. 
 

Foundational principles of data quality in AI systems and how GenAI enhances data quality frameworks 

Data quality encompasses the six core dimensions listed below; in GenAI systems, these attributes directly influence a model’s performance.  
 

1. Accuracy is critical as it plays a role in having the data correctly represent the real-world scenarios the model is intended to learn from and predict.
2. Consistency is vital for data congruence across different sources and time frames, allowing models to perform reliably regardless of varied inputs.
3. Timeliness enables data to be current and relevant, helping models produce up-to-date and actionable insights.
4. Validity for GenAI involves adherence to the syntax and format rules, allowing models to interpret and utilize data correctly.
5. Uniqueness addresses the potential for redundancy by striving for each data entry to be distinct, enhancing the efficiency of data processing and analysis.
    

Mastering these dimensions is critical for effective GenAI deployment, facilitating robust, insightful AI applications.

Notably, integrating GenAI into data management systems marks a pivotal shift in how businesses maintain data quality, offering innovative solutions that address long-standing challenges. 

By employing advanced algorithms, GenAI is poised to help with many processes, namely facilitating automated data cleansing processes and identifying duplicate records, missing values, and formatting inconsistencies with remarkable precision.

Moreover, GenAI can enhance real-time data validation, streamline the data ingestion process, and allow for more immediate detection of errors that, in the past, would have taken significantly longer to identify through traditional batch-processing methods. 

These advancements in data management help companies operate more efficiently since potentially fewer resources are required to manually identify and rectify data issues. As GenAI continues to evolve, its role in refining data quality frameworks will only become more pronounced, offering new opportunities for businesses to optimize their data management strategies and drive sustained growth.
 

Building a data-driven culture: transforming organizations from within

Building a data-driven culture is crucial; even the most advanced AI tools are only effective when supported by teams who understand and use data effectively. A strong data culture starts with prioritizing data literacy at all levels, making sure every employee — regardless of their role — is in a position to make informed decisions based on reliable data.

One key strategy in developing a strong data culture is appointing “data champions” — individuals within the organization who promote data awareness and encourage the adoption of data-driven practices. These champions can help weave data into the fabric of everyday decision-making, fostering an environment in which data drives innovation, agility, and smarter business outcomes. 

A data-driven culture thrives on experimentation and collaboration across different groups. This dynamic allows organizations to continuously learn and adapt, refining their strategies based on real-time insights and results.

For example, financial institutions’ risk and compliance functions are going through a revolution as they continue to further collaborate with their data office to refine their strategies with the help of GenAI. Below are three areas to use as examples to illustrate how risk and compliance professionals are transforming their data engagement, as well as monitoring and escalating their data needs with the help of GenAI: 

The above table is intended solely for educational and illustrative purposes and should not be construed as reflecting real-world scenarios or legal advice.

*Traditional governance processes continue to operate in parallel with the enhanced GenAI governance framework, helping with continuity while enabling innovation.

Conclusion 

The interplay between GenAI and data quality represents both a technological breakthrough and an organizational imperative. As GenAI models grow more sophisticated, their dependence on high-quality data intensifies, creating a virtuous cycle where improved data enables better AI, which in turn enhances data management capabilities.

Organizations succeeding in this space appear to share three traits (among others): 

• Rigorous data governance frameworks
• Investments in AI-driven quality tools
• Cross-functional teams bridging data science and domain expertise

To fully realize GenAI’s potential, organizations should consider prioritizing: 

• Implementing real-time data quality monitoring at all pipeline stages 
• Developing ethical AI guidelines addressing bias and privacy 
• Fostering collaboration between IT, legal, and operational teams 
• Adopting adaptive architectures for evolving data ecosystems
   

Recognizing data quality as a strategic asset—not just an IT concern—is essential to ensuring GenAI initiatives deliver transformative value. With robust data foundations, enterprises can harness GenAI to drive innovation, efficiency, and competitive advantage in an increasingly AI-driven world.

To explore how you can ground your large language models (LLMs) with extensive third-party data from Moody’s to decode risk and unlock opportunity, please get in touch.