IT Operational Efficiency Achieved with Ironstream Mainframe IT and Business IT Both Get Big-Data Benefits
Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- Ironstream
- Splunk Enterprise
Tech Stack
- IT Operations Analytics (ITOA)
- SMF Data Processing
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Cost Savings
- Digital Expertise
Technology Category
- Analytics & Modeling - Real Time Analytics
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
Use Cases
- Predictive Maintenance
- Process Control & Optimization
- Remote Asset Management
Services
- System Integration
- Data Science Services
About The Customer
The customer in this case study is a major insurance company that operates on a large scale, likely within the United States. As a large corporate entity, this company deals with vast amounts of data generated from its IT infrastructure, particularly from its IBM z/OS mainframe systems. The company is focused on improving its operational efficiency by leveraging the data it generates to gain insights into system performance and to ensure adherence to service level agreements (SLAs). The insurance industry, being data-intensive, requires robust systems to manage and analyze data effectively. This company, like many others in the industry, faces challenges in processing and analyzing the System Management Facility (SMF) records, which are crucial for operational intelligence. The company is looking to move away from traditional, labor-intensive methods of data processing and is exploring modern solutions like IT operations analytics (ITOA) to enhance its data analysis capabilities. By adopting advanced analytics platforms, the company aims to streamline its operations, reduce costs, and improve service delivery.
The Challenge
Organizations constantly look to extract more value from the operational data generated within their IT infrastructure, and to analyze that information to determine how their systems and applications are performing. A primary source of operational intelligence for IBM z/OS mainframe users lies in the SMF (System Management Facility) records. These are recorded for just about every event and activity on the system. In order to extract such valuable information, organizations typically are saddled with the time-consuming manual processes of offloading the data, extracting the relevant records and fields, and then transforming the remaining subset with expensive tools like SAS. Even then, essential questions remain unanswered, such as: What is happening now? Is what is happening now different than what was happening this same time last week or the same time one month ago? Can I predict or even prevent issues from impacting performance or adherence to service level agreements (SLAs)? This process is then repeated across the multiple LPARs that exist in most organizations, making it even more time-consuming and increasingly difficult to get complete visibility across the enterprise.
The Solution
One major insurance company had been dealing with this challenge in the same way as most other organizations. They were offloading SMF data daily, extracting the required records and fields, then doing post processing using SAS to generate reports on the desired information. As a possible alternative, however, they were intrigued by the concept of ITOA (IT operations analytics), by which their own data could be empowered to let them better understand and ultimately to improve their operations. And so they researched the leading ITOA vendors. They then began to use Splunk® Enterprise for analytics and visualization of critical IT components. But they were still relying on those antiquated, labor-intensive processes to get z/OS SMF data loaded into Splunk Enterprise. Discussing the problem with Precisely, and seeing a demonstration of Precisely’s Ironstream for Splunk® solution, they quickly realized that Ironstream would enable them to process and forward SMF data to the Splunk Enterprise analytics platform in real-time, eliminating the manual process.
Operational Impact
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