Scalable Data Quality Through AI-Cloud Synergy

The increasingly complex and dynamic digital environment is creating challenges related to data. The extraction, verification, and management of large volumes of data must not only ensure accuracy, consistency, and reliability, but must also do so at a scale adequate to meet the demands of modern businesses, say experts.

“The pandemic accelerated the push for companies to reassess how they manage their data, leading them to move toward platforms that allow for more efficient decision making,” says Rodrigo Olivares, AI and Analytics Senior Manager, Nissan Motor Corporation America. In this context, an AI-cloud strategy emerges as a potential solution, enabling businesses to automate data validation processes, enhance scalability, and maintain high standards of data quality across diverse and growing data sets.

This combination of AI and cloud infrastructure allows companies to manage much larger data volumes without compromising quality, while also ensuring flexibility and scalability as they grow. However, data governance is an unavoidable step in this process. "Data governance is fundamental to innovation. Companies need to build a strong framework to ensure their AI tools are applied effectively and transparently. Without a solid governance structure, companies risk making decisions based on inaccurate or biased data," says Juan Martín Pampliega, CEO and Co-Founder, MUTT Data.

Making decisions based on faulty data can lead to costly mistakes. Many companies struggle with integrating effective data quality systems, especially when dealing with multiple markets and heterogeneous data sources. However, this does not have to be the case. "Data governance is often seen as a complex challenge, but we view it as an opportunity to improve the decision-making process. While we are still on our journey, building a data model that evolves with the business needs has been invaluable," says Francisco Viana, CDO, Danone.

Cleaning, unifying, and standardizing data have been key steps in Danone’s data unification journey, which began three years ago, says Viana. AI has also played a key role in analyzing content and generating actionable insights, particularly through social media analytics.

Furthermore, AI's capabilities for pattern detection, error correction, and automating verification processes significantly improve data quality. "AI, particularly Generative AI, is a game-changer in how we handle and validate data. By using AI, we can improve accuracy and automate much of the process. However, it is essential to carefully choose the right partners and tools to ensure we extract true value from these technologies," says Juan David Ospina, Chief Analytics Officer, Círculo de Crédito. Structured governance helps companies monetize and extract value from data, he adds, significantly enhancing data quality and providing better market insights.

Despite the promising potential AI offers to data management, companies still face significant obstacles in deploying this technology. "Adoption is often the most challenging part of implementing AI. The integration into existing processes and overcoming internal resistance is key. For us, it is about finding the balance between speed and accuracy as we automate more of our data management," says Carlos Taboada, Director of Data Governance, AT&T Mexico.

One of the most challenging aspects in deploying these models is resistance to change within organizations, as well as a lack of employees with the technical expertise to manage AI and data analytics systems. Many organizations lack a specialized team, delaying the adoption of AI-powered solutions. Furthermore, AI tools need to be closely aligned with business goals and must be adaptable to varying needs across departments, says Taboada. Data quality issues could have hidden costs that often go unnoticed until they escalate, he adds.

Moreover, data integrity issues such as duplication or lack of standardization complicate the integration of AI in cloud systems. To successfully implement AI-powered data quality solutions, companies must not only invest in technology but also in the training of their teams and the development of the necessary infrastructure. Effective data governance is critical to ensure transparency and maintain oversight in automated processes. 

"The future of data quality will rely on our ability to integrate AI and cloud solutions effectively. But we must not forget that governance and transparency are critical to avoid unintended consequences. We need to continue evolving our systems to meet the demands of a rapidly changing landscape," says Olivares.

In the long term, the integration of AI and the cloud is expected to lead to increasingly automated and efficient data management systems. "The key is building a robust data model that can adapt to business needs," says Viana.