- \n
- Direct access<\/strong> 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. <\/li>\n\n\n\n
- Virtualisierung von Daten<\/strong> takes this further by creating a unified, real-time view across both mainframe and non-mainframe sources. CONNX builds a metadata 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. <\/li>\n<\/ul>\n\n\n\n
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.<\/p>\n\n\n\n
Real-time mainframe data in Power BI <\/h4>\n\n\n\n
Power BI is already the dashboard of record for many organizations.\u00a0But\u00a0even\u00a0the best-designed\u00a0dashboard\u00a0that lacks access to critical mainframe data is\u00a0built on an incomplete picture. For fast-paced\u00a0operations\u00a0in\u00a0business environments where seconds matter, this could mean\u00a0winning or losing high-value customers.\u00a0\u00a0<\/p>\n\n\n\n
The impact becomes concrete when seen in practice, as one of Canada’s largest banks<\/a> 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<\/a> 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. <\/p>\n\n\n\n
Powering an AI-first analytics strategy <\/strong><\/h4>\n\n\n\n
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. <\/p>\n\n\n\n
This is where mainframe data becomes a forcing function. Organizations that have not solved the mainframe data access 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. <\/p>\n\n\n\n
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.<\/p>\n\n\n\n
How CONNX bridges the gap <\/h4>\n\n\n\n
CONNX<\/a> 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. <\/p>\n\n\n\n
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. <\/p>\n\n\n\n
Mehr erfahren CONNX<\/a> can seamlessly bridge the gap between your mainframe and your analytics tools in days, not months. <\/p>\n\n\n\n
\n\n\n\n\n\n<\/h6>\n
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\n <\/div>\n - Virtualisierung von Daten<\/strong> takes this further by creating a unified, real-time view across both mainframe and non-mainframe sources. CONNX builds a metadata 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. <\/li>\n<\/ul>\n\n\n\n