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Transforming Business with AI: The Untapped Potential of Mainframe Data

Rebecca Dilthey
Rocket Software

Being able to access the full potential of artificial intelligence (AI) and advanced analytics has become a critical differentiator for businesses. These technologies allow for more informed decision-making, boost operational efficiency, enhance security, and reveal valuable insights hidden within massive data sets. Yet, for organizations to truly harness AI's capabilities, they must first tap into an often-overlooked asset: their mainframe data. With the market competition growing fiercer, businesses that effectively integrate and leverage this data can position themselves to outpace rivals.

The immense value of mainframe data lies in its deep reservoirs of transactional and customer information, particularly in industries like telecommunications, retail, and finance. Mainframes have served as the backbone of critical sectors for decades, not only storing historical data but also continuously generating vital information every day. This wealth of data has the potential to drive innovation, optimize operations, and enhance decision-making. However, many organizations continue to struggle with integrating this data into their AI and analytics strategies. A recent survey conducted by Rocket Software in partnership with Foundry, involving over 200 global IT leaders across data analytics, data management, and data engineering, sheds light on the complexities and possibilities of unlocking the value of mainframe data.

Why AI and Analytics Matter Now

AI and analytics are no longer optional; they are essential tools for survival. According to the survey, 92% of organizations are actively pursuing AI initiatives, with many running multiple AI projects simultaneously. These projects are aimed at enhancing operational efficiency, improving risk management, and optimizing decision-making processes.

AI's ability to automate routine tasks, enhance security, and provide predictive insights offers a clear competitive advantage. For example, a bank may use AI to detect fraudulent transactions before they happen, or a healthcare provider could leverage AI to identify system vulnerabilities and protect patient data. But none of this is possible without access to high-quality, comprehensive data — data that often resides in mainframe systems.

The survey reveals three primary motivators for organizations integrating AI and analytics: operational efficiency (56%), improved risk management (53%), and better decision-making (51%). With the right AI tools, businesses can identify inefficiencies in their IT systems, streamline operations, and reduce costs. In fact, 85% of respondents cited business optimization as their top goal for AI and analytics initiatives, while 74% pointed to the potential for AI to enhance customer experiences and drive innovation. However, while most businesses recognize the value of AI, integrating mainframe data into their analytics efforts remains a significant challenge.

Understanding the Full Potential of Mainframe Data

Mainframes house a diverse range of data, including customer transactions, operational metrics, financial records, and regulatory compliance information. This extensive data repository is crucial for organizations aiming to leverage AI and analytics effectively. As data is what informs AI models, organizations that successfully tap into this data stand to benefit from improved model quality, enhanced decision-making capabilities, and a more comprehensive view of their operations. The survey found that 46% of respondents viewed improving the quality, accuracy, and completeness of their data sets as one of the most attractive use cases for mainframe data. Additionally, 44% of leaders saw mainframe data as a way to gain a holistic view of their business operations, which can lead to better strategic decisions and improved outcomes.

With the integration of mainframe data with AI and advanced analytics, businesses can create new analytical capabilities that were previously out of reach. For instance, a retail company with decades of customer transaction data stored on its mainframes can use AI to identify patterns, optimize processes, and personalize customer experiences. Similarly, a financial institution could leverage its mainframe data to build predictive models that help mitigate risk and improve fraud detection.

Challenges to Leveraging Mainframe Data

Mainframe data holds immense promise, but extracting, synchronizing, and analyzing it can be a complex process. The survey found that 59% of respondents identified the complexity of data retrieval and extraction as a top obstacle to fully leveraging mainframe data. This complexity often stems from the gap between traditional mainframe systems and modern cloud-based analytics platforms.

Security concerns are also a significant barrier. Mainframes house some of the most sensitive business data, and 56% of survey respondents expressed concerns about the security, compliance, and data privacy issues that arise when integrating mainframe data into cloud environments. With regulations like the Digital Operational Resilience Act (DORA) set to take effect in 2025, businesses need to ensure that their mainframe data is both secure and compliant.

Other obstacles include the proprietary nature of some mainframe data, skills gaps within data teams, and limited scalability to handle large data volumes. Overcoming these challenges requires organizations to adopt robust data management strategies and invest in tools that facilitate seamless integration between mainframe systems and modern analytics platforms.

A Path Forward: Embracing Hybrid Cloud Solutions

To fully harness the potential of mainframe data, businesses must adopt a hybrid cloud strategy that facilitates seamless integration between on-premises mainframes and cloud environments. Hybrid cloud solutions offer the necessary flexibility, scalability, and security to manage increasing data volumes while adhering to regulatory requirements. The survey highlights that 51% of respondents are actively seeking solutions that align with their existing data management capabilities and provide the scalability needed to navigate this growing landscape.

By implementing a hybrid cloud approach, organizations can ensure their mainframe data is not only accessible and secure but primed for real-time analytics. This path forward involves a blend of advanced tools, strong data management strategies, and a steadfast commitment to innovation. With the right framework in place, mainframe data can transform into a vital asset, fueling AI initiatives, enhancing operational efficiency, and positioning businesses for sustained success in a competitive market.

Rebecca Dilthey is a Product Marketing Director at Rocket Software

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Transforming Business with AI: The Untapped Potential of Mainframe Data

Rebecca Dilthey
Rocket Software

Being able to access the full potential of artificial intelligence (AI) and advanced analytics has become a critical differentiator for businesses. These technologies allow for more informed decision-making, boost operational efficiency, enhance security, and reveal valuable insights hidden within massive data sets. Yet, for organizations to truly harness AI's capabilities, they must first tap into an often-overlooked asset: their mainframe data. With the market competition growing fiercer, businesses that effectively integrate and leverage this data can position themselves to outpace rivals.

The immense value of mainframe data lies in its deep reservoirs of transactional and customer information, particularly in industries like telecommunications, retail, and finance. Mainframes have served as the backbone of critical sectors for decades, not only storing historical data but also continuously generating vital information every day. This wealth of data has the potential to drive innovation, optimize operations, and enhance decision-making. However, many organizations continue to struggle with integrating this data into their AI and analytics strategies. A recent survey conducted by Rocket Software in partnership with Foundry, involving over 200 global IT leaders across data analytics, data management, and data engineering, sheds light on the complexities and possibilities of unlocking the value of mainframe data.

Why AI and Analytics Matter Now

AI and analytics are no longer optional; they are essential tools for survival. According to the survey, 92% of organizations are actively pursuing AI initiatives, with many running multiple AI projects simultaneously. These projects are aimed at enhancing operational efficiency, improving risk management, and optimizing decision-making processes.

AI's ability to automate routine tasks, enhance security, and provide predictive insights offers a clear competitive advantage. For example, a bank may use AI to detect fraudulent transactions before they happen, or a healthcare provider could leverage AI to identify system vulnerabilities and protect patient data. But none of this is possible without access to high-quality, comprehensive data — data that often resides in mainframe systems.

The survey reveals three primary motivators for organizations integrating AI and analytics: operational efficiency (56%), improved risk management (53%), and better decision-making (51%). With the right AI tools, businesses can identify inefficiencies in their IT systems, streamline operations, and reduce costs. In fact, 85% of respondents cited business optimization as their top goal for AI and analytics initiatives, while 74% pointed to the potential for AI to enhance customer experiences and drive innovation. However, while most businesses recognize the value of AI, integrating mainframe data into their analytics efforts remains a significant challenge.

Understanding the Full Potential of Mainframe Data

Mainframes house a diverse range of data, including customer transactions, operational metrics, financial records, and regulatory compliance information. This extensive data repository is crucial for organizations aiming to leverage AI and analytics effectively. As data is what informs AI models, organizations that successfully tap into this data stand to benefit from improved model quality, enhanced decision-making capabilities, and a more comprehensive view of their operations. The survey found that 46% of respondents viewed improving the quality, accuracy, and completeness of their data sets as one of the most attractive use cases for mainframe data. Additionally, 44% of leaders saw mainframe data as a way to gain a holistic view of their business operations, which can lead to better strategic decisions and improved outcomes.

With the integration of mainframe data with AI and advanced analytics, businesses can create new analytical capabilities that were previously out of reach. For instance, a retail company with decades of customer transaction data stored on its mainframes can use AI to identify patterns, optimize processes, and personalize customer experiences. Similarly, a financial institution could leverage its mainframe data to build predictive models that help mitigate risk and improve fraud detection.

Challenges to Leveraging Mainframe Data

Mainframe data holds immense promise, but extracting, synchronizing, and analyzing it can be a complex process. The survey found that 59% of respondents identified the complexity of data retrieval and extraction as a top obstacle to fully leveraging mainframe data. This complexity often stems from the gap between traditional mainframe systems and modern cloud-based analytics platforms.

Security concerns are also a significant barrier. Mainframes house some of the most sensitive business data, and 56% of survey respondents expressed concerns about the security, compliance, and data privacy issues that arise when integrating mainframe data into cloud environments. With regulations like the Digital Operational Resilience Act (DORA) set to take effect in 2025, businesses need to ensure that their mainframe data is both secure and compliant.

Other obstacles include the proprietary nature of some mainframe data, skills gaps within data teams, and limited scalability to handle large data volumes. Overcoming these challenges requires organizations to adopt robust data management strategies and invest in tools that facilitate seamless integration between mainframe systems and modern analytics platforms.

A Path Forward: Embracing Hybrid Cloud Solutions

To fully harness the potential of mainframe data, businesses must adopt a hybrid cloud strategy that facilitates seamless integration between on-premises mainframes and cloud environments. Hybrid cloud solutions offer the necessary flexibility, scalability, and security to manage increasing data volumes while adhering to regulatory requirements. The survey highlights that 51% of respondents are actively seeking solutions that align with their existing data management capabilities and provide the scalability needed to navigate this growing landscape.

By implementing a hybrid cloud approach, organizations can ensure their mainframe data is not only accessible and secure but primed for real-time analytics. This path forward involves a blend of advanced tools, strong data management strategies, and a steadfast commitment to innovation. With the right framework in place, mainframe data can transform into a vital asset, fueling AI initiatives, enhancing operational efficiency, and positioning businesses for sustained success in a competitive market.

Rebecca Dilthey is a Product Marketing Director at Rocket Software

Hot Topics

The Latest

OpenTelemetry enjoys a positive perception, with half of respondents considering OpenTelemetry mature enough for implementation today, and another 31% considering it moderately mature and useful, according to a new EMA report, Taking Observability to the Next Level: OpenTelemetry's Emerging Role in IT Performance and Reliability ... and almost everyone surveyed (98.7%) express support for where OpenTelemetry is heading  ...

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If you've been in the tech space for a while, you may be experiencing some deja vu. Though often compared to the adoption and proliferation of the internet, Generative AI (GenAI) is following in the footsteps of cloud computing ...

Lose your data and the best case scenario is, well, you know the word — but at worst, it is game over. And so World Backup Day has traditionally carried a very simple yet powerful message for businesses: Backup. Your. Data ...

Image
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A large majority (79%) believe the current service desk model will be unrecognizable within three years, and nearly as many (77%) say new technologies will render it redundant by 2027, according to The Death (and Rebirth) of the Service Desk, a report from Nexthink ...

Open source dominance continues in observability, according to the Observability Survey from Grafana Labs.  A remarkable 75% of respondents are now using open source licensing for observability, with 70% reporting that their organizations use both Prometheus and OpenTelemetry in some capacity. Half of all organizations increased their investments in both technologies for the second year in a row ...

Significant improvements in operational resilience, more effective use of automation and faster time to market are driving optimism about IT spending in 2025, with a majority of leaders expecting their budgets to increase year-over-year, according to the 2025 State of Digital Operations Report from PagerDuty ...

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Are they simply number crunchers confined to back-office support, or are they the strategic influencers shaping the future of your enterprise? The reality is that data analysts are far more the latter. In fact, 94% of analysts agree their role is pivotal to making high-level business decisions, proving that they are becoming indispensable partners in shaping strategy ...

Today's enterprises exist in rapidly growing, complex IT landscapes that can inadvertently create silos and lead to the accumulation of disparate tools. To successfully manage such growth, these organizations must realize the requisite shift in corporate culture and workflow management needed to build trust in new technologies. This is particularly true in cases where enterprises are turning to automation and autonomic IT to offload the burden from IT professionals. This interplay between technology and culture is crucial in guiding teams using AIOps and observability solutions to proactively manage operations and transition toward a machine-driven IT ecosystem ...

Gartner identified the top data and analytics (D&A) trends for 2025 that are driving the emergence of a wide range of challenges, including organizational and human issues ...

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