摘要: Inevitable change is paving the way toward flexible organizations -- from IT, to operations, and the business itself.


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When creating structures out of toy bricks and blocks, there are many combinations. Just two blocks can create more than two dozen combinations, and as few as six blocks create over 900 more possibilities.

This toy analogy also applies to the concept of a “composable enterprise” where the company is designed with the user at the center, while having the flexibility and versatility to make adjustments as needed. Gartner refers to the term as “creating an organization made from interchangeable building blocks.” Monolithic and rigid businesses can only be steered through a few future-state scenarios, but composable capabilities -- modularity, autonomous, orchestration, and auto-discovery allow organizations to be resilient enough to navigate unpredictability and the disruptions of the future.

With every organization becoming more digitally driven, the boundaries between IT operations and business operations are fading away at an accelerated pace. This is where AIOps comes into play -- making IT operations more autonomous and well-orchestrated within the business. Every enterprise has data, and data is what AIOps feeds on -- making sense of and utilizing it to empower operations to become more resilient, adaptive, and agile. As AIOps provides continuous visibility to real-time performance of digital operations, microservices and cloud computing provide added value to drive composability.

When forming a composable enterprise, here are three ways AIOps can be leveraged as a foundational piece of the equation:

1. Data collection and analysis

AIOps can be broken into two fundamental blocks: data collection and data analysis. Data collection requires microservices and APIs that can facilitate the intake and integration of data from thousands of devices, hundreds of applications, and infrastructure nodes. With API capabilities, AIOps becomes a true plug-and-play modular architecture within IT operations. Data analysis is massively algorithmic in nature and brings further composability to operations. ML and AI algorithms used for AIOps can be created from the raw data or metadata to derive contextual sense from the numbers.

2. Monitoring as a code

With a “Monitoring as a Code” approach, AIOps breaks the traditional barrier between ITOps and DevOps -- and extends the interoperability all the way to product and business owners. Site reliability engineers in operations roles who are responsible for delivering stability and reliability are partnering with DevOps teams to embed monitoring into the design and development stages. This covers monitoring and observability along with continuous integration and continuous deployment in the DevOps lifecycle. With AIOps, enterprises can provide real-time information on the performance of their applications and how that is impacting the customer experience.

3. Orchestration

A critical piece of the composable enterprise is to have a seamless orchestration of data and information exchanges, process flows, and a skilled team. AIOps has demonstrated promising stories in bringing together ITOps, AppOps, SecOps, DevOps, and BizOps. AIOps collects data, creates context from the combined data, and serves up the actionable insights to the different personas in the value chain. This has never been possible until the digital age of big data. Next-gen operations also require seamless interaction between automation and humans -- with a feedback loop in real time. AI and ML-based automations will constantly be learning from the training data with supervised guidance from specialists who have been working in these areas for years and have built up substantial knowledge. This enables intelligent operations that optimally utilize the resources as needed. Some work can be automated, but for anything that is strategic and proactive in nature, ensuring the team members are positioned to be successful is crucial.

Organizational leaders looking to create a composable business can start by looking at the parts of the organization where data is richly available. When AI-based operations are activated, there is a strong possibility for autonomous operations to pave the way for resilient organizations to be prepared for an unpredictable future. A recent study from Transposit shows 68% of IT leaders have seen cost increases due to system downtime, making the need for autonomous operations more important now than ever to decrease the impact from future unknowns.

轉貼自: Information Week

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