AIOps

AIOps: Why Developing Trust In Automation Takes Time

Nastel Technologies®
February 27, 2019

AIOps: Why Developing Trust In Automation Takes TimeBuilding trust in AIOps may take time, but not because enterprise users are uninterested in artificial intelligence and machine learning (AI/ML) solutions.

Why do enterprises need AIOps? Because complexity is exploding. Scale is massive. A single interaction on your smartphone, for instance, may touch dozens of computers and services—and millions of lines of code—across the internet.  Anecdotally, I’ve also found this to be true. As a CTO, I’ve met with many customers who request the same thing: An ecosystem of connected tools that enables them not only to monitor application traffic going to/from the public or private cloud, but also to third-party systems. The message I heard was that enterprises want to ingest data from a variety of tools— not just the application—to make sure all the components in their ecosystem are working well together.

The First Steps to Establishing Trust in Automation

But to tell the truth, when it comes to identifying and remediating problems, many customers aren’t ready to fully automate the process. Rather, they often say: “Tell me what the problem is, in non-technical terms, so that I know what to do. But don’t fix the problem right away—just tell me what to do.”

Why the hesitation? It seems that while customers are onboard with AIOps, many aren’t quite ready to let automaton fly by itself. Of course, to fully automate the loop, trust is essential. Trust takes time, though, particularly when remediation is required.  I believe this wariness—not uncommon with groundbreaking technologies—will be short-lived, particularly as the benefits of AIOps become evident. It’s conceivable, for instance, that after 25 or 50 times of receiving the right recommendation, a hesitant customer will allow an automated system to repair a problem without human intervention. But to reach this level of trust, the user will need to receive the right recommendation again and again, without fail.

 

 

 

 

This article originally appeared on Forbes.com.  To read the full article, click here.

 

 

 

 

 

Nastel Technologies uses machine learning to detect anomalies, behavior and sentiment, accelerate decisions, satisfy customers, innovate continuously.  To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s AutoPilot® for Analytics fuses:

  • Advanced predictive anomaly detection, Bayesian Classification and other machine learning algorithms
  • Raw information handling and analytics speed
  • End-to-end business transaction tracking that spans technologies, tiers, and organizations
  • Intuitive, easy-to-use data visualizations and dashboards

If you would like to learn more, click here

Nastel Technologies is the global leader in Integration Infrastructure Management (i2M). It helps companies achieve flawless delivery of digital services powered by integration infrastructure by delivering Middleware Management, Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate, to answer business-centric questions, and provide actionable guidance for decision-makers. It is particularly focused on IBM MQ, Apache Kafka, Solace, TIBCO EMS, ACE/IIB and also supports RabbitMQ, ActiveMQ, Blockchain, IOT, DataPower, MFT and many more.

 

The Nastel i2M Platform provides:

  • Secure self-service configuration management with auditing for governance & compliance
  • Message management for Application Development, Test, & Support
  • Real-time performance monitoring, alerting, and remediation
  • Business transaction tracking and IT message tracing
  • AIOps and APM
  • Automation for CI/CD DevOps
  • Analytics for root cause analysis & Management Information (MI)
  • Integration with ITSM/SIEM solutions including ServiceNow, Splunk, & AppDynamics

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