What if the true power of AI was hidden in mainframe data?
Artificial intelligence (AI) is rapidly becoming integrated as a pillar of modern business operations. Today, 92% of IT managers are now actively investing in AI to better leverage and analyze their data. The rise of these projects makes data quality more crucial than ever.
To effectively power their AI and advanced analytics projects, organizations need to be able to identify and leverage their most valuable data sets. To achieve this, they often have to draw on their historical transactional systems, designed well before the arrival of these new technologies. As operations evolve, mastering data becomes more complex, posing an additional challenge for businesses engaged in modernization.
Symptoms of this complexity often include weakened data integrity, fragmented governance, and persistent data silos, all of which prevent smooth, meaningful use of data and undermine the real impact of AI and analytics initiatives.
The data accessibility challenge
In many businesses, mainframes house decades of critical and reliable data, tracing the complete history of customer operations and interactions. However, only a minority of IT managers fully exploit this resource in their data development projects. One of the main reasons lies in the integration of this data with modern AI systems. According to an IDC study, 44% of companies surveyed cite compatibility issues or technology gaps when considering migrating or modernizing their mainframe applications.
Once these technical obstacles have been overcome, some questions remain: what is the exact origin of the data? Have they been modified or altered? Are they managed in accordance with good governance practices? The reliability of AI systems depends entirely on the quality of the data that feeds them. IT leaders must therefore be able to answer these questions with certainty and traceability, otherwise they risk compromising the very quality of the decisions produced by their AI tools.
A new era of data governance
With the rapid evolution of regulations, data governance is now going beyond compliance to become a strategic issue. Any company handling sensitive data must deal with the constant risk of cyberattacks and information leaks. This requires a solid security posture and rigorous risk management, based on robust governance practices. Spot checks and one-off audits are no longer enough: new requirements call for continuous visibility into how data flows and transforms within the enterprise.
Effective governance relies on a framework that preserves the integrity of data at every stage of its lifecycle. It must guarantee their accuracy, consistency and reliability, while ensuring complete traceability, rigorous management of metadata and regular verifications. As data is transferred, synchronized or duplicated to the environments where AI applications need it, it must remain accessible, reliable and actionable.
Modern data management solutions now integrate these protections directly into business processes. Automated traceability, built-in access controls, audit logs, and compliance frameworks help businesses meet regulatory requirements as well as increase the trustworthiness of AI-powered data. By making data protection the foundation of its adoption, organizations can evolve their projects while preserving the reliability and security essential to their long-term development.
Bringing a new dimension to analysis with mainframe data
Advanced analytics tools and AI systems rely on the same resource: data. To limit innovation risks and maximize the value of their technology investments, companies must approach the exploitation of their mainframe data with a clear and strategic approach. The potential benefits are numerous: more informed decisions, better visibility into operational performance and real-time understanding of competitive dynamics.
Operationally, this means adopting modern integration tools that can solve governance challenges while increasing visibility within complex IT environments. However, each modernization strategy remains unique. Some managers favor turnkey solutions, others prefer to adapt their existing systems. There is no universal model, and implementation often remains the most delicate phase.
Beyond the choice of tools or approaches, companies still have to deal with very concrete constraints related to data access, security, compliance and scalability, which frequently complicate integration efforts. Added to this is a perception of risk which can increase reluctance, making the process longer and sometimes discouraging. Many companies then choose to postpone their projects, while others move forward on several projects simultaneously.
Overcoming these obstacles requires both technical expertise and a shift in perspective. By approaching integration strategically and adopting tools that can bridge mainframe and cloud environments, businesses can overcome bottlenecks and get the most out of their data. The gains are considerable: more precise AI models, richer analyzes to guide decisions and new perspectives for the benefit of a sustainable competitive advantage.
