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Digital Transformation Drives Mainframe’s Future

Transformation Infrastructure

By pwsadmin | September 26, 2020

Hybrid IT enables a composable infrastructure which describes a framework whose physical compute, storage, and network fabric resources are treated as services. Resources are logically pooled so that administrators need to physically configure hardware to support a specific software application, which describes the function of a composable architecture. This type of transformative infrastructure is foundational…

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By pwsadmin | March 6, 2020

Earlier this year, GC GlobalNet launched a new breed of B2B e-commerce sites. Curated by Kevin L. Jackson, SourceConnecte (with an “e”) went live with three strategic goals in mind: Efficiently leverage modern social media technologies to facilitate value-based interactions between enterprise buyers and vetted suppliers; Establish a protected interactive environment capable of supporting high-value B2B e-commerce negotiations…

Potential vs. Reality: Is Edge Computing Real?

By pwsadmin | January 19, 2020

Edge computing provides compute, storage, and networking resources close to devices generating traffic. Its benefits are based on an ability to provide new services capable of meeting stringent operational requirements by minimizing both data latency and the need for bandwidth. Based on Google trend data, searches for the term has also grown substantially over the…

Enabling Digital Transformation

By pwsadmin | December 22, 2019

Digital transformation integrates technology into all areas of an organization’s business or mission. Its fundamental purpose is to create and deliver innovative and industry-changing products and services to a global customer base. This outcome requires the seamless two-way flow of data and information between internal business processes and external processes that interact with customers, business…

The ThinkShield Story Part 1: The Challenge

By G C Network | October 24, 2019

The cybersecurity challenge seems to be growing daily. Threats are becoming more sophisticated, and attacks are becoming more destructive while the corporate world’s response seems to resemble a deer in headlights. Recent examples of this dangerous state of affairs include[1]: A data breach of a US Customs and Border Protection surveillance contractor that led to…

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 ciomes 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 tio 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 advancedvisualization 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.)

Cloud Musings

( Thank you. If you enjoyed this article, get free updates by email or RSS – © Copyright Kevin L. Jackson 2017)

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