Where Leaders Struggle to Overcome Mainframe Data Challenges

Mainframe data has the potential to dramatically enhance AI initiatives and advanced data analytics projects. Many larger organizations, especially, store decades of core transactional, inventory, and customer data in legacy mainframes. Being able to retrieve and use this data at scale is a tremendous opportunity for modern enterprises. More and more leaders are starting to realize this for their own AI and analytics pursuits.

According to a recent survey of over 200 business leaders and decision-makers conducted by Rocket Software and Foundry, 46% of business leaders believe that mainframe data can improve data quality, accuracy, and completeness of existing datasets. On top of that, 44% of respondents agree that mainframe data can provide a more holistic view of business operations. This transparency enables teams to make better decisions and uncover opportunities that may previously have gone unnoticed. Going one step further, 42% of survey respondents acknowledged that mainframe data can bolster existing AI initiatives and enrich insights.

Not only does mainframe data provide a more complete picture of what’s happening across the enterprise, but it also unlocks innovation. Enterprise AI and data analytics capabilities are a true differentiator today, and being able to use mainframe data further separates companies from the competition.

All sounds great, right? The reality is not as simple. Twenty-eight percent of respondents said they use mainframe data extensively in data-driven initiatives. And four in ten leaders (42%) are struggling to integrate mainframe data with existing cloud data sources.

So, why are many enterprise leaders only starting to come around to leveraging mainframe data in their AI and analytics initiatives? The answer is a combination of very real challenges and obstacles mixed together with a handful of considerations that may be more perception than reality.

Let’s explore further.

Using Mainframe Data Comes With Unique Challenges

The potential for mainframe data is clear, but the problem is that many companies are unclear on the best path forward when it comes to how to actually use it. The survey respondents revealed several areas where real challenges persist among IT operations. The top barrier preventing teams from using mainframe data today is the complexity of data retrieval and extraction. Fifty-nine percent of respondents cited this as the top obstacle, ranking slightly above concerns around security, privacy, and compliance (56%).

Outside of these main challenges, a significant proportion of those surveyed also listed internal skills gaps (31%) and lack of scalability (31%) as blockers to leveraging mainframe data. As organizations look to not only adopt, but grow, AI use across their organizations, scalable and accessible mainframe modernization solutions will be crucial to achieving the best possible outcomes.

The survey data also found that leaders want mainframe data to be usable by technical and non-technical professionals. Nearly two-thirds of respondents (64%) said their business has had some difficulty making mainframe data accessible across the organization, and 36% said this was a very challenging problem. In other words, companies know there is untapped potential in their mainframe data, but they don’t know how to operationalize it.

Then, there’s the issue of perception. Nearly three-quarters (76%) of surveyed leaders said they found accessing mainframe data and contextual metadata to be either very or somewhat challenging. And almost half (42%) of business leaders considered integrating mainframe data with cloud data sources to be very challenging. With a relatively widespread perception of being difficult to access and integrate, organizations may be hesitant to pursue leveraging their mainframe data within AI and analytics models.

Overcoming Mainframe Data Challenges Requires Tailored Solutions

Overcoming mainframe data challenges requires that organizations implement a holistic solution set. Many of these organizations are already at some point of a journey toward modernization. That journey requires solutions that are flexible, scalable, and ready to meet them at any point of that journey. Solutions like Rocket Software’s Hybrid Cloud Data Suite gives business leaders the tools to make integrating cloud and mainframe data easier, and more accessible. This set of solutions also provides crucial scalability and creates a simplified view of an organization’s data, encompassing both structured and unstructured data.

Organizations also need to maintain data integrity and accuracy without compromising speed. This requires efficient pipelines and model training capabilities that bring mainframe data together with cloud data without adding immense operational complexity. Tools like Rocket® Data Replicate and Sync let IT teams seamlessly and securely replicate and synchronize live data into a cloud environment or anywhere else it may be needed. This can give business leaders the right tools to help break down the complexity that comes with maintaining data integrity and speed, enabling real-time, high-performance data replication and synchronization across diverse platforms, from on-premises mainframes to the cloud.

The Rocket Software and Foundry survey uncovered that 42% of decision-makers would prefer to adopt a pre-built solution to accomplish these objectives. Twenty-eight percent said they would opt to build a custom solution in-house, and 30% are considering both options. The right decision depends on the specific business and use case. But no matter the route an organization chooses, opting to work with a trusted partner like Rocket Software lets business leaders tap into a robust portfolio of solutions and services that can help modernize systems and make mainframe data more accessible for AI and analytics initiatives in the long run.

What’s most important is that any attempt to incorporate mainframe data into enterprise AI and analytics initiatives should be safe and scalable and empower end users all across the business. Injecting mainframe data into advanced data projects represents a fusion of the old and the new, which is why it’s such an intriguing challenge. But those challenges are not insurmountable. And the organizations that embrace the right tools to leverage mainframe data securely, and at scale, will have a major advantage over those who rely only on real-time data.

Learn more about how Rocket Software can help your organization overcome its toughest mainframe data challenges and get the most out of AI initiatives.