Multicloud complexity is jeopardising organisations’ digital progress
Cloud presents somewhat of a paradox to Australian and New Zealand enterprise and public sector leaders pursuing digital transformations.
On one hand, cloud is an omnipresent enabler considered a crucial component to delivering any transformation agenda, yet research shows that it introduces operational complexity to such an extent that it becomes difficult to deliver outstanding digitally-powered customer experiences.
And so, it is simultaneously streamlining and complicating digital experience delivery.
There is, of course, some nuance to this. It may be easier to run a successful digital transformation in the cloud when the cloud is a singular ecosystem. However, most organisations are already working with more than one cloud. The more clouds and providers there are, the harder it can be to manage this infrastructure in a standardised, cohesive manner. Our research indicates that the average multicloud environment spans approximately 12 different platforms and services, each with its own set of configurations, APIs, and performance metrics.
There is also complexity for organisations within all of the different clouds as well. Each cloud has its own set of management consoles, dashboards and monitoring tools. On top of that, cloud-native applications have to be structured in a certain way, often disassembled into microservices that run inside of containers that are stitched together with orchestration software such as Kubernetes. So there can be many 'moving pieces' in one cloud, and when that is multiplied by the number of clouds in operation, the complexity increases exponentially.
Put another way, whether an organisation undergoing transformation is in one cloud or multiple clouds, they're likely to be dealing with the same management complexity that, if given the choice, they'd prefer to abstract away. That complexity is present in the way digital applications and experiences are architected for the cloud, and compounded by the deployment of a vast array of different monitoring tools to try to maintain visibility and control. Each of these monitoring tools provides a different version of 'the truth', which makes it difficult for teams to work together to rapidly identify and resolve the cause of issues that arise within their digital applications and experiences.
With so much data and potential conflicts within those datasets, making informed decisions becomes increasingly problematic. This can put digital transformation timelines and ambitions at risk.
Chasing advantage and meaning with AI
Australian and New Zealand organisations constantly want to innovate and embrace digital transformation, but they can't remain reliant on various tools that are difficult to manage and produce conflicting data. Cloud-native technology stacks are producing an explosion of data that is beyond 86% of leaders' and teams' ability to manage.
Without a single source of truth, teams often find themselves operating in isolation, unable to harness cross-functional expertise or insights to address common challenges effectively. This means valuable time and resources are often wasted on manual processes and inefficient ways of working, and ultimately just lead to more complexity that delays transformation.
The reality is that regional organisations will fall behind their peers if their technology stack becomes too cumbersome to manage.
To avoid this, teams need to improve the maturity of their AI, analytics, and automation strategies so they can drive smarter decision-making and more efficient ways of working. IT and security teams must have real-time data that provides the insights needed to anticipate problems before they arise. Without this, they cannot visualise or solve ongoing occurrences or issues within their environment.
Many organisations have tried to use traditional AIOps to achieve better insights. However, not all AI is created equal. These attempts often fall short as they are reliant upon probabilistic and training-based learning models, which are time-consuming and often not very accurate.
For teams to unlock the full benefits of AI for digital transformation, they need to move beyond traditional AIOps and probable answers. This requires an observability platform that is supported by multiple learning models, encompassing causal, predictive, and generative AI.
Causal and predictive AI amplify the value of generative AI. Generative AI is only as good as the underlying data that feeds its algorithms. Causal AI is a technique that identifies the precise cause and effect of events or behaviour, delivering the precision needed to drive generative AI when, for example, engineers need to query a system using it. Predictive AI, on the other hand, introduces machine learning, data analysis, statistical models, and AI methods to predict anomalies, identify patterns, and create forecasts about future events.
Together, these three forms of AI enable greater insight, furnishing teams with actionable insights that can be used to gain deeper visibility into their environments, and proactively anticipate and address issues across their transformations.
This is the 'extra pair of hands' that multicloud operations teams so urgently need to free themselves to focus on efforts that accelerate broader digital goals.
