Dave Link, Founder and CEO of ScienceLogic.
“By identifying and understanding patterns in massive and varied sets of machine data, companies can equip themselves to find, fix and prevent performance issues within their organization,” said Dave Link, Founder and CEO of ScienceLogic. The global leader in AIOps and other hybrid cloud IT management solutions recently announced the expansion of its global footprint to South Africa. ScienceLogic’s presence in South Africa is represented by a local value-added distributor, Corr-Servewhich will enable the company to further expand and support its growing customer base in Southern Africa.
When digital transformation overwhelms IT performance management capabilities (and when hybrid and multicloud infrastructures bring complexities), more problems are created than solved and it becomes impossible for organizations to keep pace. This is where artificial intelligence for IT operations (AIOps) will prove incredibly useful. By infusing artificial intelligence (AI) into IT operations, it becomes possible to use machine learning (ML) and deep learning to help IT operations make proactive and intelligent decisions.
Digital transformation aimed to solve business problems by making work faster, easier and less resource-intensive. However, advances in distributed architectures, multiclouds, containers, and microservices (to name a few) have resulted in large multidimensional data streams generating excessive noise that hampers IT teams’ ability to identify and resolve service incidents. As computing systems have evolved from static, predictable physical systems to dynamic software-defined resources capable of change and reconfiguration on demand, this has created a need for equally dynamic technologies and processes to manage them. This requirement for dynamism translates into a complexity experienced at three levels:
- System: At the heart of the problem is the complexity created by modular, distributed and dynamic systems with transient components.
- Data: The second level of complexity concerns the data generated by these systems on their internal operations. Logs, metrics, traces, event records and more, this data is very complex due to its volume, specificity, variety and redundancy.
- Tools: The third level is the complexity of the tools needed to monitor and manage data and systems. As more and more tools become available (with increasingly narrow functionality), these often present interoperability issues that can create operational and data silos.
Today’s dynamic IT environments cannot be managed with yesterday’s tools and outdated information. There is a profound need for a management approach that can create order out of chaos and provide real-time visibility and predictability. Organizations need a way to intelligently balance critical workloads between humans and machines to empower teams to properly manage their most precious resource: time.
Reactive to proactive
AIOps can address this need by helping IT teams anticipate and resolve issues before they arise by collecting these large amounts of operational data, separating signal from noise, and generating suggested actions to Automatically solve problems that would otherwise cripple entire IT departments. True AIOps is a combination of machine learning and automation capabilities that enable teams to filter out noise, while identifying and contextualizing information faster to speed resolution and proactively identify issues before they arise. occur.
Leverage complex data
Combining AI and ML, AIOps uses these massive volumes of historical incident data, change data, and other operational data such as metrics, logs, and events to highlight and isolate anomalies. before they escalate into larger failures. Without the ability to make intelligent recommendations, automation tools on their own are limited in what they can accomplish, but by pairing automation with AI and ML, businesses can remove manual tasks and take the guesswork out of decision-making to truly increase human skills and abilities.
Expect. So what is AIOps?
AIOps enables operations teams to harness the overwhelming complexity and volume of data generated by modern IT environments and use it to maintain availability by preventing outages and ensuring continuous service. In other words, AIOps means using ML and data science to solve IT operational problems.
What’s wrong with AIOps?
AIOps is not a silver bullet to all operational headaches, but it will provide a specific set of benefits to organizations. These benefits include:
- Finally achieve simplicity: The complexity is a result of digital transformation and the need to foster remote working, especially through hybrid cloud adoption. AIOps can restore simplicity by aggregating information across distributed deployments.
- Alleviating the skills shortage: Given the scarcity of qualified IT specialists, the use of their time must be optimized. It’s not about using automation to replace human labor, it’s about optimizing What humans pass their time. By automating certain tasks, IT resources are freed up to focus on other high value-added tasks.
- Enable visibility and predictability: AIOps extracts actionable insights from vast pools of monitoring data gathered from disparate IT applications that provide operational insights across different layers of IT infrastructure.
- Cost reduction and time saving: Reducing the complexity and time an IT team has to spend on certain tasks translates into resource efficiency and cost savings that every business can benefit from.
Where digital transformations have stalled due to overwhelming complexity or resource issues, AIOps can restart the journey and organizations can finally achieve the speed and stability they dreamed of. The ML and data science embedded in AIOps can give IT operations teams a true, real-time understanding of all issues, including new, unforeseen issues that affect the availability and performance of digital services.
To learn how to turn ITOps into AIOps, download the eBook: ‘Your guide to getting started with AIOps‘.