IntelliNet
Root-Cause Analysis
US Bank
Alert Correlation
Barclay's Bank
End-to-End Monitoring
The Future of AIOps
Empowering User Experience
With AIOps
Firsthand Insights From Leading Companies
Empowering User Experience
With AIOps
Firsthand Insights From Leading Companies
Empowering User Experience
With AIOps
Firsthand Insights From Leading Companies
Slow is the New Down
In today’s digital economy, you’re one poor user experience away from a lost customer. Downtime is costly, and slow is the new down.
As you increase the volume, velocity and variety of data, it gets more difficult for your IT operations team to manually correlate it, analyze it, and solve any problems that arise before they impact user experience. This is especially true for companies with tools dispersed across different data siloes.
In response, AI and machine learning have emerged as a means of relieving some of the manual intervention required to maintain effective IT operations and protect the user experience. This shift is key going forward, because some of the main threats to user experience call for automated actions and decision-making processes that are just beginning to gain adoption. The challenges include:
Proliferation of
monitoring tools
of IT organizations rely on up to nine different IT monitoring tools to support modern applications 1
Need for
predictive analytics
of companies say the most important AIOps capability is analytics that predict probable future events that could impact availability and performance 2
Alert
overload
of companies experience over 50,000 alerts on average per month 3
In the pages to come, you’ll learn about how industry-leading companies are using machine learning and AI to tackle some of the biggest challenges in IT Ops today, including:
IntelliNet
How AIOps Accelerates Root-Cause Analysis
At a time when you place such a high premium on user experience, rapid root-cause analysis has never been more important. But it’s made difficult by the proliferation of disparate tools and data siloes, which can make it hard for you to efficiently understand the source of an issue. With AI and machine learning, IT teams can:
• Understand the root cause of a problem affecting one or more services
• Contextualize the information relevant to the issue at hand
• Execute appropriate remediation to minimize impact on user experience
Learn how IntelliNet, a managed service and cloud solutions provider, has made an early investment in AI and machine learning to improve user experience by speeding up root-cause analysis and mean time to repair.
U.S. Bank
Reducing Correlation Pains With AI and Machine Learning
As data increases in volume and complexity, so does the challenge of manually correlating alerts and resolving them before user experience suffers. No wonder that correlation takes up such a large part of the triage process with many IT Ops teams. Which of these top monitoring challenges have you struggled with?
Detecting Issues
Proactively
of IT leaders say the same
Collaboration Across
Teams
of IT leaders identify collaboration as a critical challenge
Alert Correlation Across
All Tools
of IT leaders cite alert correlation as a critical challenge)
Learn how US Bank is using AI and machine learning to analyze large, monitoring-driven data sets to make alert correlation a smaller part of their triage process and deliver better up time today.
Barclays
More Visibility, Better Experiences
New distributed and microservice-style architectures bring more complexity and new monitoring challenges with them. The use of disparate monitoring tools makes it difficult to get end-to-end visibility across an entire IT estate. And that means it’s even harder to identify and negate issues before they impact up time and user experience.
What AIOps Capabilities do You See as Most Important?
Predictive Analytics
agree that predicting probable future events that may impact availability and performance is the most important AIOps capability
Service Analytics
agree that highlighting the potential impact to key services is the most important AIOps capability
Business Value Dashboards
agree that analyzing both IT and business data showing patterns of behavior to detect positive business outcomes is the most important AIOps capability
Learn how Barclay’s Bank believes AI and machine learning will play a key part in automation by requiring you to look at your full infrastructure, end-to-end, and the various monitoring tools you’re using across that application stack, to create optimal user experiences.
The Future of AIOps
Today, 97 percent of executives are investing in building or launching big data and AI initiatives.4
That’s because if you want to not only survive but thrive in today’s digital economy, you must consider the use of AI in IT operations. Now is the time to start assessing and implementing AIOps-powered solutions to drive the superior user experiences your customers have not only come to expect, but will increasingly demand.
To learn more about AIOps and how to get started, check out our Definitive Guide to AIOps
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1-3 TechValidate, AIOps IT industry survey, May 2018
4 Randy Bean, MIT Sloane Management Review, “How Big Data and AI Are Driving Business Innovation in 2018,” February 2018