Mainframe technical data is a great source of information for how well a business is operating. As customers and internal resources interact with products and services, the system logs that activity and stores it. Hiding in plain sight are millions of data points — rich with actionable insight — just waiting to be analyzed.
Understand the mainframe’s contribution to the business, determine the resources used by business services for auditing and internal chargeback, and identify any unseen mainframe issues that might be impacting business performance. All of these real-world insights can be gained through analyzing operational data.
Given all the business insights mainframe data can offer, why do companies rarely leverage operational data as an analytical tool?
The issue with connecting operational data with business services
Several obstacles inhibit the full analytical potential of IT teams. When it comes to making operational data applicable to day-to-day operations, three major obstacles stand in the way:
- Logs volume: As mentioned above, data is generated after every single interaction with the mainframe. This means millions of logs are generated daily, and there is no default way to manage, organize, or analyze this insurmountable volume of logs.
- Complexity: Even trained IT professionals struggle to make sense of endless strings of uncontextualized data. Overwhelmed by the complexity of logs analysis, IT experts and novices alike might decide mainframe analysis is more trouble than it’s worth.
- Connecting operational data with business operations: By default, there is no intuitive way to bridge the gap between mainframe data and business services. There is no one-to-one analytics framework for understanding the direct impact the mainframe has on the services of a business.
For many IT teams, mainframe data analysis is simply unrealistic. Communication breaks down, and the true value of the mainframe for optimizing business services is never realized. All because IT teams lack an intuitive, affordable way to apply raw data to business strategies.
Why analyze operational data from a business perspective?
The value of technical data for improving business services may not be immediately apparent. To even the most experienced mainframe professionals, technical data can seem like an impenetrable collection of raw figures — lacking any intuitive business framework or analytical model.
Mainframe activity can be understood as a record of insight into business activity. When deciphered, it offers a look into the mainframe’s contribution to the business. Understand the efficiency of applications, define business services in terms of technical components, encourage transparency among departments, easily understand SLA compliance and hold business units accountable — all of these real-world benefits and more can be achieved by aligning mainframe data with business services.
Unlock new analytical potential with ZETALY
ZETALY helps bridge the gap between the technical and the practical. By intuitively collecting relevant data with AI algorithms, and displaying this information in business-relevant dashboards, this all-in-one analytics platform helps make sense of raw mainframe data and apply it to the real world.
Through its Service Intelligence features, ZETALY helps transform raw data into relevant business insights, and makes it easy to decipher the contents of logs, uncover real-world business cases for mainframe activity, and relay this information to other stakeholders.
Anyone can be a mainframe expert thanks to ZETALY business dashboards. Learn more about analyzing operational data from a business perspective in our guide How to Align Mainframe Operational Data with Business Initiatives.
Analyze Operational Data
From a Business Perspective
A preeminent challenge for IT teams is conveying actionable information to non-technical stakeholders. Our guide teach you how to unlock the true value of mainframe analytics by approaching operational data from a business perspective.
