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What Will the AIOps Landscape Look Like in 2019?

As the enterprise technology stack continues to grow in complexity and scale, the coming year brings with it ongoing challenges to keep pace with the rate of business today.

Artificial intelligence, machine learning, and big data have all been in the IT lexicon for several years now. Nevertheless, the past year saw a marked shift in their treatment among technologists. With digital transformation races taking place in every industry, AI, ML, and data analytics have moved from the stuff of ideas and the future to the stuff of today.

But what then, does the future look like? As the enterprise technology stack continues to grow in complexity and scale, the coming year brings with it ongoing challenges to keep pace with the rate of business today.

The key prediction is that AIOps, which uses advanced analytics through AI and ML to help operations move at the pace of business opportunity, is going to gain a lot of added traction in the coming year. Here are the top trends in 2019 that will drive enterprises towards AIOps.

Well-designed data lakes take precedent over algorithms.

Since AIOps crosses multiple discrete functional areas, it’s critical that enterprises are able to ingest data at high fidelity and from multiple disparate sources, then contextualize it for use in AI and ML applications. To do this, data must be stored in modern data lakes that allow it to be free of traditional silos. Topological mapping models enabled by graph technology harken inspiration to social graph technology pioneered at Facebook, making it possible to analyze relationships across multiple data sources in the ecosystem in real-time. This makes the output immediately available for the next step in the workflows.

More partnerships and integrations will increase the power of the ecosystem.

Even the largest enterprises are struggling with data accuracy. Every aspect of an IT ecosystem, from various application elements to networking componentry and storage has their own data structures. That complexity puts pressure on IT operations teams who struggle to visualize and manage their application health. In 2019, we will see a greater volume of technology partnerships between companies answering customer demands for better APIs and other ways to create seamless integrations.

The automation of menial tasks will help Ops move faster.

As a Forrester analyst recently stated – [automation helps to] remove the drudgery of IT so staff can work on higher-order issues. There is so much opportunity for organizations to gain efficiencies by automating the simple stuff. This will ultimately lay the groundwork for more complex automation to come. Automation doesn’t have to be overly targeted at cognitive replacement of humans or be emboldened by sci-fi ideas at this point. Ops teams should start scripting the best practices and automate those menial tasks to free up resources and become more agile. 

Automation will move cloud to the edge.

The volume of data that must be processed in order to automate can be overbearing. If an IT service can be processed at the edge, close to service deployment, and tied to specific technologies, enterprises can see increases in resource and time efficiencies. An IT service that can be processed at the edge means Ops can take enrichments and action in near real-time.

Enterprises with multi-cloud environments will require a centralized view of infrastructure and performance.

Enterprises are quickly finding multi-cloud solutions provide the most flexibility and cost-efficiency. However, they are also finding that without proper tools to monitor them, multi-cloud can be challenging to manage.  Enterprises that streamline their processes, people, and tools to provide a single, de-siloed overview will reap the benefits while those that do not will be mired by additional management work. Having visibility and control between on-premise, private, and hyper-scale cloud environments is critical to issue remediation and root cause analysis.

Ephemeral workloads, driven by DevOps, continue to grow exponentially.

Containers and serverless application adoption are faster than ever before. This, in turn, can quickly create a new set of issues as writing code can quickly become unsupported and complex to manage. The accelerated pace will bring with it a challenge of resource and code development and the need for tools to manage the increased complexity it brings.

Maturation is needed before AI/ML becomes truly useful beyond corner cases.

AI/ML has become commonplace in technology strategies. However, despite AI and ML finding their ways into corner use cases, they still have a ways to go before becoming mainstream. The ability to ingest and use data in real-time is one barrier enterprises will work to overcome in 2019. Akin to doing an MRI and coming up with a diagnosis months later, old data is useless to the market. Clean and accurate training data is critical for organizations to harness AI/ML for immediate anomaly detection and event correlation.

A lack of data scientists will force IT to look for self-discovery tools.

The importance of data scientists is growing as they continue to become extremely valuable to enterprises looking to use more data science in their AI and ML operations. Also, with everything being programmatic and software-defined, an IT staff without the requisite coding skills is going to be increasingly difficult to relegate to dinosaur status. With such competition for data scientists in the job market, and to address these staffing issues, IT will have to look towards tools that don’t require supervision or human intervention. 

2019 is the year of real-time data accuracy.

If there is a common theme to these predictions, it is the importance of quality data and the visibility, ingestion, and contextualization that goes to support it. Fragmented, siloed data – as has been the norm in enterprises for years – is simply unsustainable in modern IT management, especially as enterprises embark on digital transformation and AI/ML initiatives.

Fortunately for enterprises, AIOps is helping solve many of those problems. It allows for greater consolidation of toolsets. It allows enterprises to break down data silos. And it allows for the contextualization of data so that enterprises can fully benefit from AI/ML.

With so many changes and trends expected to take hold in the coming year, businesses should take a serious look at how AIOps can prepare their organizations for a successful 2019. Check out our latest whitepaper for some of those insights: Unlock the Power of AIOps: From Vision to Reality





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