And while automating your IT operations and conquering the complexity of your IT environment can sometimes seem like you’re climbing Everest, we’re here to help—by sharing with you the latest in AIOps, ITOps, and IT monitoring.
- Knowing the top barriers in your IT operations can help you forge a path to excellence.
According to this article in Chief Executive, to forge ahead with digital transformation—especially in the current climate—you should know the following obstacles:
- IT staff lacks time to learn and implement technology.
According to this Forrester report, only 12% of enterprises have fully transitioned to modern tools for monitoring IT infrastructure and applications. Some 86% are using at least one legacy tool, and 37% are using only legacy tools. This can leave your staff in survival mode, not in the progressive environment that affords time and flexibility for learning, scaling, and capitalizing on insights and visibility.
- Integrating new technology quickly requires organizational and cultural solidarity.
Moving forward with digital transformation in today’s climate requires many different people, roles and teams coming together from different corners of an organization. Understanding where organizational divisions are losing cohesiveness and how siloed teams think and work differently dismantles internal barriers and exposes shortfalls. This cultural solidarity is needed to break down silos because the difference between success and failure of adopting new technologies can come down to overcoming functional departmental challenges.
- There is a lack of support for hybrid IT.
While forward-looking goals and legacy systems don’t usually align, the dichotomy underscores the need for hybrid IT to facilitate both innovation and security, both essential legacy tools and new capabilities, and both the present and the future.
- Complexity stifles IT’s role as a catalyst for change.
According to Forrester, a third of companies use 20 or more tools in their IT operations. This piles on complexity in tools and strategies that can fail your organization. IT complexity also increases the cost to support the environment; degrades services, performance and availability; and creates security risks.
- There’s an inability to understand business impact in order to prioritize tasks.
Without a big-picture view of aggregate service performance, it’s hard to determine the best IT investment or the most high-priority tasks. But, with an analytics-driven, service-level view, companies can achieve predictive, real-time outcomes and resolutions. This can better position you to meet and exceed today’s crucial demands, instead of merely staying relevant
- Combining artificial intelligence with SD-WAN can transform network operations—enabling automated operations and business agility.
SD-WANs are used to increase application availability, reduce costs, and improve performance. AIOps infuses machine learning (ML) into ITOps to increase the level of automation—reducing errors and enabling businesses to make changes at digital speeds. According to this article from NetworkWorld, combining these two technologies will transform network operations.
Why? Because administrators can use their time to focus on strategic initiatives instead of fixing problems. After all, 90% of the time taken to fix a problem is spent identifying the source. And now that applications reside in the cloud and can run on mobile devices, increasing complexity, identifying the source of a problem has gotten harder. Combining AIOps and SD-WANs can give you the ability to spot even the smallest anomaly, even if it hasn’t yet begun to impact business.
Although SD-WANs are designed so that all routing rules are managed centrally by administrators and can be transmitted across a network, combining the two technologies takes it a step further—enabling administrators to anticipate problems before they happen through fault prediction. It may even adjust network glitches on its own before users are affected, which can improve network performance.
- AIOps and DevOps are better together.
According to this blog on DevOps.com, although DevOps is an attempt to scale technology with humans, AIOps is the ultimate answer. Why? Because companies generate too much data for humans to monitor and understand manually, and with legacy monitoring tools. This provides an opening for AIOps, which helps streamline and automate IT monitoring, especially in complex, microservices-based IT environments
Now DevOps teams work together on their own microservice, each focused on their organization’s desired customer experience and business goals. But as incidents occur in real time, there is a major challenge with DevOps teams to achieve insights and awareness across their applications, infrastructure, and business services. AIOps helps to address this challenge by helping you free your team to focus on mission-critical tasks so they can build better services for better customer experiences, instead of constantly trying to fix emergencies.
- AI tools are changing the face of unified communications for businesses.
This article from insideBigData.com outlines the following principle areas where artificial intelligence is impacting unified communications:
- More Efficient Meetings and Conferences
AI can identify both who is taking part in the meeting and what is being discussed. And thanks to machine learning and natural language processing, translation engines are showing improvement so that meetings can soon be held in dual languages.
- Streamlined Workflows and Internal Communications
AI can help organizations collect, disperse, analyze, and act upon data which can assist intelligent, automated scheduling, among other processes. And if an AI platform is integrated with your UC platform, it can recognize when meetings get discussed before adding them to your schedule.
- Advanced Analytics and Integration
By processing and analyzing data, you can make more informed decisions that affect your company’s bottom line. Having an integrated unified communications platform generates a lot of data (ex: logs, recorded calls, emails). And AI combined with machine learning provides the context to help you use that data by identifying patterns in all of the data, giving you actionable insights for better decision making.
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