Survey finds that while 92% of organizations are pursuing AI projects, the majority are experiencing obstacles in leveraging data stored on the mainframe, compromising model accuracy
Rocket Software, a global technology leader in modernization software, today announced the findings from its survey, Rethinking the Role of Mainframe Data in Enterprise AI and Analytics. Conducted by Foundry Media for Rocket Software, the survey polled over 200 business leaders and decision-makers in data analytics, management, engineering, and architecture across the U.S., U.K., Germany, and France, to understand how organizations are leveraging mainframe data as part of their AI and analytics initiatives. The survey found that only 28% of survey respondents are using mainframe data extensively in data-driven initiatives. Not factoring mainframe data – which includes both real-time and historical information on customer interactions, account data, financial transactions, and inventory – into AI models is a missed opportunity. By integrating this rich data, models become more accurate, insightful, and reflective of the full scope of an organization’s operations, unlocking powerful insights and driving more informed decision-making.
AI and advanced analytics are playing an increasing role in how businesses differentiate themselves, unlocking opportunities for new efficiencies, growth drivers, and customer experiences. The success and usefulness of an AI model lies in the data that it is trained on. In the race to adopt AI, a majority of organizations have failed to fully leverage mainframe data to enhance their models. AI that accurately represents all of a business’s data empowers leaders with greater visibility into operations and provides deeper insights, facilitating informed decision-making in real-time. In fact, 46% of respondents said mainframe data was a potential means for improving data quality, accuracy, and completeness of existing datasets.
Challenges, both real and perceived, have led many to struggle when it comes to integrating mainframe data into their AI and analytics capabilities:
- 76% of leaders said they found accessing mainframe data and contextual metadata to be either very or somewhat challenging
- 64% said they considered integrating mainframe data with cloud data sources to be somewhat to very challenging
- The biggest obstacles to leveraging mainframe data were found to be:
- Complexity of data retrieval and extraction processes (59%)
- Concerns regarding security, compliance, and data privacy (56%)
- Proprietary data formats (41%)
“If organizations fail to incorporate their mainframe data into AI and analytics, they risk developing models that are less intelligent, powerful, or accurate,” said Michael Curry, President, Data Modernization Business Unit, at Rocket Software. “Rocket Software has the technology and expertise to help enterprises easily bridge their mainframe data into their AI and analytics initiatives, automating away the complexity, and reducing the need for specialized skills and knowledge to protect, retrieve, and extract mainframe data.”
Mainframe modernization is a worthwhile pursuit. Forty two percent of respondents said they prefer to adopt a prebuilt solution to integrate their mainframe data with cloud data, and 51% cited building new analytical capabilities or business initiatives that were not previously possible was the most attractive use case for mainframe data. That’s where experienced partners, who offer resources across the modernization continuum, can support businesses by mitigating challenges to unlock data’s full potential. Survey respondents noted scalability for large datasets (82%), interoperability with existing data management tools and platforms (82%), and robust security and encryption (81%) as the top benefits for integrating mainframe and cloud data.
To download the full study, click here. For further insights, register for Rocket Software’s webinar on November 12, here.
Methodology
Foundry surveyed 213 business leaders and decision-makers, including those employed in data analytics, data management, data engineering, or data architecture roles between May 10, 2024, and May 27, 2024, to understand how organizations are leveraging or planning to leverage mainframe data as part of their AI and analytics initiatives to drive strategy, improve operational efficiencies, and enhance competitive advantage.
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