Bridging the gap between mainframe data and hybrid cloud environments

A high hurdle many enterprises have yet to overcome is accessing mainframe data via the cloud. According to a study from Rocket Software and Foundry, 76% of IT decision-makers say challenges around accessing mainframe data and contextual metadata are a barrier to mainframe data usage, while 64% view integrating mainframe data with cloud data sources as the primary challenge.

Mainframes hold an enormous amount of critical and sensitive business data including transactional information, healthcare records, customer data, and inventory metrics. Much of this data must adhere to regulations for organizations to remain compliant, which is why they are often housed in a secure mainframe.

The mainframe also often holds the most current and complete view of transactions within an organization. Data professionals need to access and work with this information for businesses to run efficiently, and to make strategic forecasting decisions through AI-powered data models. Without integrating mainframe data, it is likely that AI models and analytics initiatives will have blind spots. However, according to the same study, only 28% of businesses are fully tapping into the potential of mainframe data insights despite widespread acknowledgment of the data’s value for AI and analytics.

In order to make the most of critical mainframe data, organizations must build a link between mainframe data and hybrid cloud infrastructure.

Bringing mainframe data to the cloud

Mainframe data has a slew of benefits including analytical advantages, which lead to operational efficiencies and greater productivity. It enhances scalability, flexibility, and cost-effectiveness, while maximizing existing infrastructure investments.

Integrating this data in near real-time can be even more powerful so that applications, analytics, and AI-powered tools have the latest view for businesses to make decisions. Giving the mobile workforce access to this data via the cloud allows them to be productive from anywhere, fosters collaboration, and improves overall strategic decision-making.

Additionally, integrating mainframe data with the cloud enables enterprises to feed information into data lakes and data lake houses, which is ideal for authorized data professionals to easily leverage the best and most modern tools for analytics and forecasting. Connecting mainframe data to the cloud also has financial benefits as it leads to lower mainframe CPU costs by leveraging cloud computing for data transformations.

Despite the benefits of bringing mainframe data to the cloud, many organizations are not taking advantage of this opportunity, as the Foundry survey shows. Four key challenges prevent them from doing so:

1. Accessing data and contextual mainframe metadata from the cloud – One of the most significant hurdles of connecting mainframe data to the cloud is the tools commonly used for cloud data integration, analytics, and management often lack the ability to access or understand mainframe data. These tools don’t have the necessary connectors, metadata relationships, or lineage mapping that spans both mainframe and cloud environments. As a result, cloud data teams can struggle to determine what mainframe data is available and which data to use. This presents a lack of visibility in the metadata lineage spanning across mainframe and cloud data.

2. Ensuring security and compliance during data transit – Mainframes are some of the most secure environments in IT, housing highly sensitive transactional data. However, transferring this data to the cloud introduces new security concerns. Protecting data in transit and understanding which sensitive information should be redacted is critical to maintaining compliance. Differences in security models, access controls, and tracking the origin of data across platforms further complicate this process.

3. Integrating mainframe data with cloud data sources – Data teams working with cloud infrastructure often lack visibility into what data lives in the mainframe and how it can be used effectively. The absence of contextual metadata, variations in data formats and structures, and the different skill sets required to handle both cloud and mainframe data further hinder integration efforts. Without these insights, leveraging mainframe data in cloud initiatives remains a challenge.

4. Simplifying data integration for business or non-technical users – For mainframe data integration to become more widespread, it must be easier to use. Current ETL tools often require specialized skills, and many workflows have evolved into legacy code that’s difficult to maintain. Bridging the gap will require making mainframe data as accessible to business analysts and data teams as any cloud-based data source, removing the complexity that currently limits broader adoption.

Accessing data from the edge

Bridging the gap between mainframe data and hybrid cloud infrastructure can solve the challenges of leveraging modern applications with critical business data at scale, and give data professionals a complete, real-time view of critical business information.

For example, Rocket® DataEdge simplifies mainframe-to-cloud integration with easy-to-use, bi-directional connectors that enable seamless data movement between any mainframe source and cloud destination. Automated metadata scanning and linking provide visibility across data tiers, while unified governance features ensure sensitive data is filtered, redacted, and protected in accordance with mainframe security models.

DataEdge also supports batch replication, real-time change data capture (CDC), and virtualized data access, allowing full bi-directional integration with open data formats to streamline hybrid environments. Additionally, it empowers data analysts and engineers to quickly discover, understand, and select relevant mainframe data, making it easier to generate actionable insights across the enterprise.  

It’s incredibly important for enterprises today to leverage hybrid infrastructure for a variety of reasons, including scalability and adaptability, but it’s equally important to leverage this infrastructure with critical mainframe data.

Learn more about how Rocket® DataEdge can help organizations bridge the gap between mainframe data and hybrid cloud infrastructure.