Digital transformation remains a key priority for organizations seeking to maintain competitiveness and drive operational efficiency. Spanning cloud computing, infrastructures, change management strategies, and organizational alignment, a successful digital transformation has many crucial components. Multicloud environments, in particular, are recognized as instrumental in propelling digital transformation forward, offering organizations enhanced scalability, speed, and agility.
However, managing these environments poses various challenges. For instance, the vast volumes of data they generate can be difficult to capture, analyze, and act on. For example, recent research has shown that 87% of technology leaders say multicloud complexity makes it more difficult to deliver outstanding customer experience. To solve these challenges, organizations need to identify how to tame the complexity of multicloud environments, before they spiral out of control.
Digging into the multicloud conundrum
As organizations increasingly adopt multicloud strategies, they often find themselves managing a complex ecosystem. In fact, the average multicloud environment spans approximately 12 different platforms and services, each with its own set of configurations, APIs, and performance metrics. This complexity is compounded when organizations deploy a vast array of different monitoring tools in an attempt to maintain visibility and control. These monitoring tools each provide 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 applications.
With so much contradicting data, making informed decisions becomes increasingly problematic. In fact, 86% of technology leaders say cloud-native technology stacks produce an explosion of data that is beyond humans’ ability to manage. Without a single source of truth, teams often find themselves operating in isolation, unable to leverage collective 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.
VP EMEA Solutions Engineering, Dynatrace.
Kubernetes creates more headaches
The advent of Kubernetes is adding another layer of complexity to technology stacks and digital transformation. IT and security teams are attracted to Kubernetes as the platform of choice for running cloud native services, as it streamlines the deployment, scaling, and management of containerized applications. However, despite its advantages, the dynamic nature of Kubernetes means there are many complexities that organizations must navigate.
One of the main challenges lies in the deployment process itself. Implementing Kubernetes introduces layers of intricacy, particularly within monitoring and security. Traditional monitoring tools designed for more conventional architectures often struggle to adapt to the dynamic nature of Kubernetes. This means organizations may encounter blind spots and vulnerabilities within their deployments, potentially leaving a back door open for cybercriminals. To monitor Kubernetes environments effectively with these approaches, developers must also possess detailed knowledge of its underlying architecture and operational dynamics. This can be a serious hurdle for those lacking specialized Kubernetes expertise.
Unlocking the AI advantage
To effectively reduce the complexity of cloud, organizations need to prioritize advanced AI, analytics, and automation capabilities. IT and security teams must have real-time data that provides the insights needed to anticipate problems before they arise. Without this they cannot visualize or solve ongoing occurrences or issues within their environment.
Many organizations have tried to use traditional AIOps to achieve better insights. However, these attempts often fall short as they are reliant upon on 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 require an observability platform that is supported by multiple learning models, encompassing causal, predictive, and generative AI. When combined with these types of AI, observability data gives teams actionable insights that can be used to gain deeper visibility into their environments, and proactively anticipate and address issues across their transformations.
Look to minimize complexity
Organizations 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. This will ultimately just lead to more complexity that delays transformation. 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.
The reality is that organizations will fall behind competitors if their technology stack becomes too cumbersome to manage. AI can act as the extra pair of hands that teams so urgently need to reduce this burden and free themselves to focus on efforts that accelerate innovation. However, it’s important to remember that not all AI is created equal. Organizations need to move beyond traditional AIOps and probable answers.
To keep pace with cloud-native delivery, organizations require precise, predictive AI analytics that enable them to automate multicloud infrastructures at scale and supercharge the success of their digital transformation journey.
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