Datenintegration
The missing link in your analytics: Real-time mainframe data
Despite investing heavily in analytics platforms like Power BI, many organizations still find themselves struggling to make timely and effective decisions. Often, the culprit is a lack of access to real-time mainframe data. Learn how to bridge this gap, empower analytics teams, and set the stage for future innovation.
Most enterprises today have invested heavily in analytics platforms like Power BI, Tableau, Snowflake, and others. Yet despite these investments, many organizations still find themselves with an incomplete picture of their business in these tools for one simple reason: a lack of access to real-time Großrechner data.
The most authoritative operational data in the enterprise commonly resides in mainframe systems. But historically, the process of connecting mainframe data to modern analytics platforms has been complex and labor-intensive. As a result, analytics platforms like Power BI lack Echtzeitzugriff to a critical piece of business context, placing use cases that rely on an up-to-date view of operations out of reach. Ultimately, this leads to slower decision-making and missed opportunities.
Why mainframe data matters
Accessing mainframe data can be challenging due to its unique formats and structures. Traditionally, connecting it to modern analytics platforms has required time-consuming data engineering practices, like developing custom ETL (Extract, Transform, Load) pipelines to translate the data into formats that BI tools can understand.
This is a lengthy, labor-intensive process that not only delays the delivery of mainframe data into analytics systems, but bogs down engineering teams with the task of constantly maintaining and updating these connections. Eventually, data engineering teams spend more time keeping mainframe pipelines running than delivering new value-add features. For many organizations, the engineering burden alone becomes reason enough to leave mainframe data out of analytics altogether. Slowly, the gap between what analytics shows and what is actually happening in the business widens over time.
Seamless access to real-time mainframe data
For most organizations, the assumption has been that integrating mainframe data into analytics tools requires lengthy projects, specialized skills, or settling for outdated data. With CONNX, this doesn’t have to be the case. There are two primary ways to access live mainframe data in real time, both without touching your source systems:
- Direct access connects analytics tools like Power BI directly to the mainframe using standard SQL via ODBC, JDBC, or .NET interfaces. Queries execute against the live source system in real time, with no copies, staging, or delays. Analysts can use their existing tools and workflows without needing mainframe expertise.
- Virtualisierung von Daten takes this further by creating a unified, real-time view across both mainframe and non-mainframe sources. CONNX builds a Metadaten model that maps each source’s native structures into a single, coherent relational model. To any querying tool, these disparate systems appear as one federated database. The data remains in place, while Power BI and other tools query the virtual layer as if it were a single, unified warehouse.
This flexibility makes real-time mainframe data accessible to any analytics environment, setting the stage for tools like Power BI to unlock its full potential.
Real-time mainframe data in Power BI
Power BI is already the dashboard of record for many organizations. But even the best-designed dashboard that lacks access to critical mainframe data is built on an incomplete picture. For fast-paced operations in business environments where seconds matter, this could mean winning or losing high-value customers.
The impact becomes concrete when seen in practice, as one of Canada’s largest banks knows this firsthand. Managing over $1.1 trillion CAD in wealth management assets, the bank’s advisors faced a specific version of this problem. Advisors were taking calls from high-net-worth clients while their dashboards showed end-of-day data, sometimes hours behind reality. By deploying CONNX across its mainframe environment, which included Adabas, Sybase, AS400, and Oracle, the bank created a real-time, unified view of every client portfolio. No data was moved. No production system was touched. Advisors could answer client questions with confidence. Customer trust improved, and new clients followed.
Powering an AI-first analytics strategy
The relationship between analytics and AI is shifting. For years, the conversation focused on AI for analytics and using machine learning to make dashboards smarter. Today the framing is reversing, as analytics for AI becomes the priority. Analytics for AI means using the same pipelines, governance, and integration work that power your reports to feed the data that trains and informs AI models, copilots, and agentic workflows. The quality, freshness, and completeness of the data feeding your dashboards today is the same data that will feed your AI initiatives tomorrow.
This is where mainframe data becomes a forcing function. Organizations that have not solved the mainframe Datenzugriff problem for analytics will hit the same wall when they scale into AI. Any blind spots in analytics turn into systemic blind spots in AI. The gap does not disappear in an AI-first world; it compounds as more decisions and workflows depend on that missing data.
The organizations investing in real-time mainframe data access today are not just improving their dashboards. They are quietly building the data infrastructure that every AI use case coming next will rely on.
How CONNX bridges the gap
CONNX is purpose-built for organizations that need real-time access to mainframe data without the complexity of traditional integration. It provides real-time SQL access to mainframe and legacy data sources, including Adabas, VSAM, IMS, RMS, and more. Implementation is measured in days, not months, and does not require specialist mainframe skills to maintain.
For analytics teams, this means tools like Power BI can query live mainframe data directly. That data can be joined with sources from cloud platforms, relational databases, and other enterprise systems in a single unified view.
Mehr erfahren CONNX can seamlessly bridge the gap between your mainframe and your analytics tools in days, not months.
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