Where mainframe data and AI fit into enterprise analytics

The insights that can be derived from mainframe data represent a huge opportunity for businesses. It could be a retail store looking to rework outdated processes and improve the customer experience, or a healthcare network hoping to get a handle on its security posture with enhanced fraud detection.

No matter the intended result, organizations that understand the potential of mainframe data and actively collect, analyze, and apply its insights at scale have a unique advantage. That advantage can instill confidence among decision-makers and leaders, ensuring they are equipped with the best real-time insights when looking to innovate.

For leaders searching for ways to maximize the value of their mainframe data, a number of advances in areas including artificial intelligence (AI), cloud computing, and data management  can help make leveraging data easier. These tools and technologies give data and analytics leaders a powerful means to improve operations, boost efficiencies, and transform experiences.  

The path to advanced analytics runs through mainframe data

The hype behind AI is nothing new, and it has shown plenty of promise in its ability to transform operations end-to-end within IT systems and enhance customer experiences. In a survey conducted by Rocket Software, respondents identified several benefits that motivated them to pursue AI initiatives. These include 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.

But before businesses can reap the benefits of AI investments, they need to ensure they have access to reliable, accurate, and timely data. This is where mainframe data, an often-under-leveraged resource, comes into play. A majority of organizations have relied on mainframe systems in some form or another to house vast amounts of transactional data — many of which have been around for decades. That historical context and huge data set make mainframe data ripe for the picking when it comes to AI and analytics — two things that depend on data to feed models and generate insights. When considered within the context of AI initiatives, 42% of surveyed leaders said they considered mainframe data to be a viable option for enriching insights.

So, what about putting mainframe data into practice? Those leaders identified the ability to build out new analytical capabilities as the top use case for this data. But successfully building those new capabilities and generating new opportunities means having an effective modernization strategy, as well as an experienced technology partner to support that transformation.

Building the right strategy to maximize mainframe data

Rocket Software’s survey found 56% of decision-makers identified security, compliance, and data privacy as a top obstacle to actually utilizing mainframe data. Getting past that hurdle is all about striking the right balance between leveraging data while also ensuring its use is in line with existing policies and guidelines. Achieving this requires a robust set of security and compliance solutions to help bridge the gap and enable consistently secure use of mainframe data in broader AI efforts.

For example, the right data integration solutions, like those in the Rocket® DataEdge suite, provide a broad set of tools to help organizations ensure all their data can be easily accessed, managed, and interpreted while still adhering to crucial regulations like GDPR and HIPAA. Organizations should also include a comprehensive content management solution, like Rocket Mobius, as part of their portfolio to deliver stronger data governance.

Beyond security, an effective strategy also needs to ensure that an organization’s data pipelines and the processes that exist across the mainframe and other infrastructures are easily scalable. Scalability, however, has proven to be a pain point for many leaders. Of those surveyed by Rocket Software, nearly a third (31%) identified scalability as an issue. As organizations look to establish strategies that include mainframe data, they need to incorporate solutions that help tap into the best of both cloud environments and the mainframe, like Rocket Software’s Hybrid Cloud Data Suite. Doing so gives organizations the ability to create a simplified view of data — structured and unstructured — spanning on-premises infrastructure and the cloud.

Mainframe data is full of opportunity for growth, new opportunities, and more impactful AI and analytics. Properly leveraging mainframe data brings forth deeper analytical insights that can transform the way businesses leverage AI. But a number of challenges stand in the way as organizations look to access that data securely and use it at scale. With the right technology solutions and a trusted partner, leaders can bring mainframe data to their modernization strategy, improve operations, and effectively leverage AI and advanced analytics.

Learn more about how your organization can tap into the power of mainframe data.