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For all the advantages multicloud brings to business, managing and monitoring applications isn’t always easy. Mix on-premise, private cloud and public cloud operations with the rapid growth of data, more complex applications and the introduction of new IT architectures, and you’re looking at a challenging IT environment. Recent research from IBM Market Development & Insights (MD&I) found that only one quarter of IT leaders felt that they could effectively monitor and manage their end-to-end IT operations, while 44% saw fragmented visibility into those operations as a primary challenge.
This is a serious concern. The more IT operations become critical to both line of business and the customer experience, the more crucial it is to optimise performance and minimise downtime, but the trend towards complexity works against this.
As Padraig Byrne, senior director analyst at Gartner puts it, “IT operations is challenged by the rapid growth in data volumes generated by IT infrastructure and applications that must be captured, analysed and acted on.”
“Coupled with the reality that IT operations teams often work in disconnected silos” he continues “this makes it challenging to ensure that the most urgent incident at any given time is being addressed.”
The answer lies in automation, and in the coupling of artificial intelligence to operations data in AIOps platforms. AIOps applies machine learning and data analytics to IT operations, in platforms that can ingest operations data from both historical and real-time sources, then analyse it in ways that produce useful, actionable insights. Correlations and patterns become usage trends, signs of performance bottlenecks and warnings of potential failure.
The key benefits of AIOps are twofold. Firstly, AIOps provides visibility and clarity for the most complex multicloud environments, bringing together data from disparate sources so that IT teams can see and understand both what has happened and – crucially – what is happening. When an application is failing or running slowly, they can isolate the probable cause, while linking patterns or anomalies to events.
Secondly, AIOps provides control. By applying machine learning to historical data and then real-time data streams, it can use the insights generated to forecast issues before they can affect the customer or impact the business. With automation, it can even initiate a fix. The strategic end-goal is systems with their own feedback loops, that can monitor the volume, velocity and variety of data and continuously optimise applications and infrastructure and even, where possible, self-heal. This augments and partially replaces functions normally provided by the IT team, but this in turn gives them space and the visibility they need to make further optimisations and long-term strategic choices.
Making AIOps work requires a shift in thinking and the acquisition of new skills, while Gartner research director Viv Bhalla also recommends a phased approach, where organisations identify the strategic use cases that could show the most benefits first, then find the tools and vendors best-equipped to handle them. But as businesses extend their use of multicloud and handle ever growing quantities of data, it’s only natural that their operations will grow more complex. AIOps has obvious potential to meet that complexity and help IT teams regain both visibility and control in the multicloud world.
To find out more about AIOps and how it can help firms increase visibility, performance and manage the complexity of IT operations, click here to download this IBM whitepaper.
Alternatively, if you're interested in how IBM can help you, click here to schedule a consultation.