Operational Reliability

Achieving operational reliability

Our customers expect operational excellence with improved predictability, proactive issue prevention, responsive support, and better recovery in the event of an issue. ARGO responds by detecting and addressing issues before they affect application performance, impact end users, or interrupt solution functionality.

ARGO’s Early Detection Monitoring Service (EDMS), a key component of our subscription service offering, brings a team of specially trained technical experts together to provide the knowledge needed to monitor mission-critical operations and reduce potential impact to revenue. EDMS performs real-time monitoring on ARGO application solutions in customer production environments to ensure operational efficiency, with insight to over 250 risk-point KPIs that enable thorough analysis.

We create mission-critical software,
and we keep it that way.

Find out how

We create mission-critical software,
and we keep it that way.

Find out how

What does EDMS do?

Predict

  • Monitors 250+ operational metrics
  • Predicts and eliminates issues leading to outages
  • Verifies the health, status, and connectivity of production applications

React

  • Immediately detects issues
  • Eliminates an average 5.5 hours of diagnostic time
  • Provides direct access to diagnostic data

Recover

  • Isolates problems
  • Resolves short-term or long-term issues
  • Ensures processing of queued data
  • Resolves problematic server processes

Report

  • Provides data for trend analysis and peer comparisons
  • Tracks key operational and performance metrics
  • Supports hardware and network planning, device replacement, and software maintenance

Why EDMS matters to our customers

Case studies show how EDMS supports operational reliability by employing techniques such as analyzing trends, running environmental health checks, detecting performance degradation, and monitoring data quality.

Case Study #1

Proactive prevention using trend analysis

Third-party security and monitoring software installed in a customer production environment resulted in a delayed response time. For the non-EDMS customer, the response time increased for weeks, until it resulted in a catastrophic outage. For the EDMS customer, ARGO specialists detected the issue through trend analysis and mitigated the issue before users were impacted.

Case Study #2

Health check of environment

An external event resulted in a production server issue. For the non-EDMS customer, this led to a significant user outage caused by transaction timeouts. For the EDMS customer, ARGO specialists detected the issue during the first morning health check and resolved the issue before it impacted users.

Case Study #2

Health check of environment

An external event resulted in a production server issue. For the non-EDMS customer, this led to a significant user outage caused by transaction timeouts. For the EDMS customer, ARGO specialists detected the issue during the first morning health check and resolved the issue before it impacted users.

Case Study #3

Reaction and recovery

An issue originating in the relational database caused performance degradation and timeouts in the production environment. This issue occurred without warning and could not have been predicted. For the non-EDMS customer, the issue was handed off from the users to the support help desk, to the IT department, who reported the issue to ARGO support. For the EDMS customer, ARGO specialists detected the issue and quickly reacted to isolate it and initiate the recovery process.

Case Study #4

Host performance degradation

EDMS detected degraded mainframe host performance through weekly trend analysis. At a transactional level, the team saw functions such as customer searches taking up to 50 percent longer. Because EDMS pinpointed the exact day and time when performance started degrading, the customer was able to find two changes made around this time—one to the middleware and one to the host—and take action. Based on historical information, it could have taken a non-EDMS customer up to six months to identify the issue due to the inability to identify the day and time performance degradation started. This could lead to significant user impacts and complaints as well as potential outages. Actual support cases for similarly-sized customers provide a basis for projecting non-EDMS customer statistics.

Case Study #4

Host performance degradation

EDMS detected degraded mainframe host performance through weekly trend analysis. At a transactional level, the team saw functions such as customer searches taking up to 50 percent longer. Because EDMS pinpointed the exact day and time when performance started degrading, the customer was able to find two changes made around this time—one to the middleware and one to the host—and take action. Based on historical information, it could have taken a non-EDMS customer up to six months to identify the issue due to the inability to identify the day and time performance degradation started. This could lead to significant user impacts and complaints as well as potential outages. Actual support cases for similarly-sized customers provide a basis for projecting non-EDMS customer statistics.

Case Study #5

Performance degradation by third-party software

Updating third-party cybersecurity software on servers caused automated tasks – sending of adverse action letters and generating real-time forms – to be delayed or time out. Tasks normally completed in 30 seconds did not finish and timed out after two to three minutes. For this EDMS customer, the ARGO team caught the issue on the first day, enabling the bank to immediately make changes to improve performance and avoid regulatory compliance issues. For the non-EDMS customer, the response times would continue to increase for weeks resulting in an outage and a backlog of mandated notices, followed by possible compliance violations and fines. Actual support cases for similarly-sized customers provide a basis for projecting non-EDMS customer statistics.

Case Study #6

Data quality monitoring

EDMS monitors data quality to ensure analytical and functional systems operate efficiently and accurately. Each month, ARGO measures and trends data quality
issues, such as potential overlays, duplicates, or overlapping source system records in its EMPI solution. In this case, disparate data sources contributing patient records exceeded the ARGO data quality threshold for potential duplicate rates. ARGO identified the offending sources and provided cleanup tasks to remove the inaccurate records.

Case Study #6

Data quality monitoring

EDMS monitors data quality to ensure analytical and functional systems operate efficiently and accurately. Each month, ARGO measures and trends data quality
issues, such as potential overlays, duplicates, or overlapping source system records in its EMPI solution. In this case, disparate data sources contributing patient records exceeded the ARGO data quality threshold for potential duplicate rates. ARGO identified the offending sources and provided cleanup tasks to remove the inaccurate records.