Discover Key Trends in Network Observability for IT Success
Emerging Trends in Network Observability
Join experts as they discuss how IT leaders can adapt observability tools to meet modern network challenges.
Enterprise Management Associates (EMA) is hosting an insightful webinar titled "Your NetOps Tools Are Aging Faster Than Your Network: 5 Emerging Trends That Are Reshaping Network Observability." Shamus McGillicuddy, Vice President of Research at EMA, and Brandon Hale, Chief Architect at IBM SevOne, will share the crucial trends redefining network observability, which are essential for IT leaders.
Network observability tools are vital for modern digital operations, enabling IT teams to monitor, troubleshoot, and enhance network performance. Despite the maturity of these tools, emerging technologies continually challenge their effectiveness. A recent report highlighted that only 43% of IT organizations feel their current observability strategies are fully effective. With evolving business requirements, it is essential for organizations to adapt their observability solutions accordingly.
As McGillicuddy points out, "Most IT operators indicate they will replace their network observability tools in the next couple of years. These transitions can be resource-intensive and need careful consideration. Our discussion will outline how to effectively initiate this process."
Key Trends Shaping Network Observability
During the upcoming webinar, McGillicuddy and Hale will explore five pivotal trends in network observability:
1. Shift to Cloud-Based Solutions
Companies are increasingly moving from traditional on-premises network management tools to cloud-based alternatives. This shift allows for more flexibility, scalability, and cost efficiency.
2. AI-Driven Approaches
AI-enabled network observability is becoming integral as it helps automate many processes, providing deeper insights and enhancing decision-making capabilities.
3. Application-Aware Operations
There’s a growing need for application-aware network operations to effectively manage the performance of applications within the network, ensuring optimal user experiences.
4. Importance of End-to-End Observability
End-to-end observability is critical across hybrid, multi-cloud networks and SD-WAN, facilitating comprehensive visibility and control.
5. New Requirements from AI Workloads
The rise of AI traffic and workloads brings forth new observability requirements, necessitating advancements in how networks are monitored and managed.
These insights aim to assist IT leaders in modernizing their observability strategies, optimizing network performance, and staying abreast of digital demands.
The webinar is scheduled for Wednesday at 1:00 PM Eastern time. Interested participants can register for the webinar online.
About EMA
Founded in 1996, EMA is recognized as a leading research and consulting firm within IT. Dedicated to providing actionable insights in a rapidly evolving technology landscape, EMA helps organizations make informed technology decisions. With a team of seasoned analysts,EMA conducts independent research and market analysis to aid clients in achieving their strategic goals. To learn more about EMA, including its research and consulting services, visit their official site and follow them on social media platforms.
Media Contact
Raleigh Gould
Enterprise Management Associates
303-543-9500
Frequently Asked Questions
What is the webinar about?
The webinar discusses emerging trends in network observability and how IT leaders can adapt to modern demands.
Who are the speakers at the webinar?
The speakers include Shamus McGillicuddy from EMA and Brandon Hale from IBM SevOne.
When is the webinar scheduled?
The webinar is set for Wednesday at 1:00 PM Eastern time.
How can I register for the webinar?
Registration for the webinar is available online through EMA's official channels.
What are the five key trends discussed in the webinar?
The webinar will cover the shift to cloud solutions, AI-driven approaches, application-aware operations, end-to-end observability, and new requirements from AI workloads.
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