Digital Transformation is amplifying mainframe as mission critical to business growth more than ever before. With 70% of the world's corporate data and over half of the world's enterprise applications running on mainframe computers, they are at the core of just about every transaction. A single transaction can, in fact, drive up to 100 system interactions. The continued increase in mainframe transaction volumes, growing on average 7-8% a year for 78% of customers, has even led to a new buzzword: The Connected Mainframe.
According to IDC's research, connected mainframe solutions generate almost $200 million in additional revenue per year while simultaneously improving staff productivity and cutting operational costs. Over 50% of the benefit value comes from higher transaction volumes, new services, and business expansion. Businesses rely on mainframes to:
Perform large-scale transaction processing (thousands of transactions per second)
Support thousands of users and application programs concurrently accessing numerous resources
Manage terabytes of information in databases
Handle large-bandwidth communication
The growth of transaction volumes and diversity of applications connecting into the mainframes can lead to significant operational challenges. With more mobile to mainframe applications to manage and more data to transact, including eventually blockchain data, organizations need to improve their mainframe operations model drastically. Reactive approaches to mainframe management just can't keep up with the velocity of change and dramatic growth. Enterprises are losing an average $21.8 million per year from outages and 87% of these enterprises expect this downtime cost to increase in the future. An astounding 66% of enterprises surveyed admit that digital transformation initiatives are being held back by unplanned downtime.
Improving the enterprise's ability to support increased mainframe workloads is why machine learning, augmented intelligence, and predictive analytics are critical to the CA Mainframe Operational Intelligence solution. Embedded operational intelligence proactively detects abnormal patterns of operation by ingesting operational data from numerous sources. This helps to anticipate and avoid problems through:
Detecting anomalies quickly and delivering proactive warnings of abnormal patterns
Using advanced visualization and analysis that accelerates issue triage and root-cause analysis
Deploying multiple data collectors that work synergistically to provide broad visibility, more in-depth insights and increased accuracy of predictions
Delivering dynamic alerts that improve mean time to resolution (MTTR)
Combining simplified visualization of time-series data with deep-dive analysis tools
Clustering alerts automatically to correlate related alerts and symptoms
Removing irrelevant data points from reports to provide more actionable insights
CA Mainframe Operational Intelligence consumes data from multiple CA solutions and directly from the IBM® z Systems® environment through SMF records. Raw alerts from performance, network and storage resource management tools are automatically correlated to surface specific issues and provide predictive insights for each issue. With machine learning and intelligence, wide data sets lead to more accurate predictions, and better relationship and pattern analysis. This insight also includes drill-down and probabilities which can also trigger automated problem remediation. This capability is uniquely embedded into the management environment to more proactively optimize mainframe performance and availability with fewer resources.
This modern approach to operational management will help organizations on-board new IT staff to manage the mainframe moving forward, while also protecting limited mainframe experts to focus on essential tasks. Using machine learning and advanced analytics, your entire team can now acton potential issues much earlier, isolate the real root-cause faster and ultimately remediate issues before they become revenue-impacting incidents.
(This content is being syndicated through multiple channels. The opinions expressed are solely those of the author and do not represent the views of GovCloud Network, GovCloud Network Partners or any other corporation or organization.)
About Kevin Jackson Kevin Jackson, founder of the GovCloud Network, is an independent technology and business consultant specializing in mission critical solutions. He has served in various senior management positions including VP & GM Cloud Services NJVC, Worldwide Sales Executive for IBM and VP Program Management Office at JP Morgan Chase. His formal education includes MSEE (Computer Engineering), MA National Security & Strategic Studies and a BS Aerospace Engineering. Jackson graduated from the United States Naval Academy in 1979 and retired from the US Navy earning specialties in Space Systems Engineering, Airborne Logistics and Airborne Command and Control. He also served with the National Reconnaissance Office, Operational Support Office, providing tactical support to Navy and Marine Corps forces worldwide. Kevin is the founder and author of “Cloud Musings”, a widely followed blog that focuses on the use of cloud computing by the Federal government. He is also the editor and founder of “Government Cloud Computing” electronic magazine, published at Ulitzer.com.
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