Redefining the Role of AI and Mainframe Data in Enterprise Analytics

Whether it’s a retailer hoping to optimize processes and enhance customer experiences, or a healthcare organization looking to improve security and identify potential threats before they emerge, mainframe data is ripe with potential. And in a highly competitive environment, organizations that collect, analyze, and apply data insights at scale have a clear competitive advantage. Data helps identify new revenue and business opportunities that would otherwise remain hidden. It also gives leaders the confidence to innovate and explore ideas. High-quality data illuminates black boxes and mitigates substantial risk for modern enterprises.

Additionally, advances in AI, cloud computing, and data management technologies have made leveraging data much easier. These technologies allow data and analytics leaders to unlock data for highly valuable insights, operational efficiencies, and product and service innovations that enhance customer experiences.

Leaders Remain Optimistic About AI and Advanced Data Analytics

A recent Rocket Software and Foundry survey confirmed that teams are bullish on AI and advanced data analytics. Over 200 leaders and decision-makers in data roles were asked about their AI activity. Ninety-two percent of those respondents reported they were actively using AI to advance data and analytics initiatives within the organization, and on average, those respondents said they had as many as five active or planned AI projects.

What’s driving this enthusiasm for AI? Organizational leaders see the technology as an influential way to transform operations end-to-end within their IT systems and enhance customer experiences as well. Rocket Software’s survey identified a few key motivators including improvements to operational efficiency (56%), bolstering risk management (53%), and elevating decision-making (51%). Of those top motivators, 85% of respondents said they were focused on business optimization, driven by a desire to boost operational efficiency or improve their risk management. And overall, 96% of respondents had one of these three factors in their top three motivations for investing in AI.

Using Mainframe Data to Enable Enterprise Analytics

Leaders need reliable, accurate, and timely data to fully capitalize on these opportunities. One data source that often goes untapped today is the mainframe. Most businesses have long relied on mainframe systems in some form to securely house huge amounts of transactional data. Many of these mainframe systems have been in existence for decades, accumulating a massive trove of information that holds serious potential for businesses that can successfully put it to use. Considering AI and analytics depend on data to feed models and generate insights, mainframe data has become an asset that cannot be ignored. The Rocket Software and Foundry survey found that many leaders agree.

Forty-six percent of respondents said mainframe data provides a way to improve data quality, accuracy, and completeness of existing datasets. When considered within the context of AI initiatives, 42% of respondents stated mainframe data is a viable option for enriching insights.

Among the potential use cases that can come with mainframe data, most respondents (51%) said that the most attractive use case for this data is building out new analytical capabilities and pursuing novel business opportunities. But successfully building those new capabilities and ultimately creating new opportunities requires the right strategy, and importantly, the right technology partner to support that evolution.

Choosing the Right Strategy to Incorporate Mainframe Data

When used effectively within a mainframe modernization strategy, mainframe data can lead to several competitive advantages for businesses, from filling knowledge gaps to building a more holistic view of business operations. The key is knowing how to integrate mainframe data into a broader AI strategy. Companies should be able to retrieve and manage mainframe data efficiently, maintain data integrity, ensure compliance, and protect privacy.

However, that’s easier said than done to most leaders and decision-makers. Rocket Software’s survey found that 56% of respondents identified security, compliance, and data privacy as a top obstacle to leveraging their own mainframe data. As these organizations try to strike a balance between leveraging mainframe data while also not compromising on security and compliance, Rocket Software’s portfolio of security and compliance solutions offers an effective means to accomplish exactly that.

For example, with structured data, Rocket Data Replicate and Sync enables companies to securely access, replicate, and synchronize mainframe data in real time while ensuring compliance with industry regulations like GDPR and HIPAA. By integrating seamlessly with both on-premises and cloud environments, it leverages metadata-driven intelligence from Rocket Data Intelligence to optimize data replication and maintain data consistency. With advanced encryption and role-based access controls, the solution enhances data security while supporting scalability in hybrid cloud architectures.

Similarly, with unstructured content and information, Rocket Mobius provides a comprehensive content management solution focused on data governance, allowing companies to efficiently discover and manage mainframe data. Its new Content Smart Chat AI capabilities facilitate governance-driven content querying, making it easier to capture and analyze data securely. With robust encryption, audit trails, and automated data classification, Mobius emphasizes data integrity and compliance, helping organizations mitigate risks in their data management practices.

Any data pipelines and processes between mainframes and other infrastructure - like a cloud platform - must be scalable. Scalability, however, remains a problem, as Rocket Software’s survey found that nearly a third (31%) of respondents felt this was an issue. That’s where a solution set like Rocket Software’s Hybrid Cloud Data Suite can help. These solutions make it easier for organizations to get the best of mainframe and cloud infrastructure. The result is greater scalability and the ability to create a simplified view of the organization’s data that spans both structured and unstructured and stretches between on-premises infrastructure and the cloud.

When it comes to implementing a solution, 42% of survey respondents said they preferred to adopt a prebuilt solution to integrate their mainframe data with cloud data. And over half (58%) that already had a strategy in place said they were using either a data fabric or data mesh. That desire for a prebuilt solution compares to just 28% who said they would prefer to build a solution in-house. Rocket Software is uniquely suited to meet that desire. With myriad challenges to adoption, from talent shortages to rising costs to simple misperceptions around mainframe data, a trusted partner like Rocket Software can deliver a broad set of solutions and mainframe knowledge that makes the process simple and impactful from the start.

Learn more about how your organization can tap into the power of mainframe data and fuel better AI and analytics.