News Roundup, January 31, 2020: What’s Happening in AIOps, ITOps, and IT Monitoring
February is upon us. There’s no denying we are deep into Q1, our plans for the year are underway, and the future is as clear and bright as a crisp winter air.
January 31st is Backward Day, a bizarre, informal holiday with a surprisingly long history. Ancient Romans called it Saturnalia. During no other time was free speech permitted for the slaves, but during Saturnalia, all manner of insult was allowed—which probably lead to February 1st being referred to as, “Me and My Big Mouth” day. Hopefully, your Backward Day won’t lead to regrets—and one thing you will never regret is catching up on the latest news in AIOps, ITOps, and IT Monitoring.
1. There are five ITOps obstacles to overcome for business growth.
According to an article by DevOps.com, five IT obstacles have the power to slow business growth. IT has changed, and with it, solutions have become less straightforward as ITOps deals with the present and increasingly complex data deluge. So, what obstacles will you need to overcome for continued business growth?
- IT Staff Lacking Time to Learn New Technologies—With greater and greater demand on IT teams, there is not always the opportunity or the resources to amend operations for new tools, as upgrading requires learning new APIs, UI/UX, and deployment architectures.
- Difficulty Supporting New Technologies Quickly—Keeping pace with IT complexity and managing IT is increasingly difficult. New services, applications, and infrastructures are constantly added. While there are undeniable benefits to upgrading, the difficulty lies in understanding the complexity of these systems and how to leverage them most effectively.
- Lack of Support for Hybrid IT—Hybrid-cloud and legacy systems, including multi-cloud, on-prem, and private/public clouds, are complex and challenging to manage. Understanding different architectures and their components, as well as understanding how to manage your data, is a must.
- IT Complexity—IT is becoming increasingly complex—there’s always something new, and the amount of information to process exhausting.
- Inability to Understand Business Impact—The only way that can be done is to see and contextualize the data associated with those issues.
2. The financial industry looks to AIOps to restructure data applications.
According to an article by Fintech, the financial industry is one of an increasing number of sectors that have seen the need for migrating their data workloads to the cloud. Now, with the financial sector beginning to move their on-premise, legacy workloads to the cloud, many major banks are finding that cloud migration has direct relevance to their business use cases.
This migration to the cloud is driving an interest in the adoption of AIOps and automation that is better suited to the cloud and hybrid cloud infrastructures where they are expected to revolutionize cloud workloads. AIOps applies AI and machine learning to reduce expensive, challenging, and time-consuming problems in big data deployments. When AIOps diagnoses root causes, it eliminates the need to manually sort through data. What’s more, as automation is also able to provide notifications for specific failures, issues can be addressed and resolved automatically.
For many financial institutions, the most significant obstacle to transitioning to the cloud is planning. Mapping out how applications are transferred, as well as how they should be configured, is new territory for companies not already on the digital transformation journey. However, these preliminary decisions are necessary to support accurate cost and consumption forecasting. Critical, data-driven insights are vital for a successful and nimble cloud migration.
3. “Observability” will be a hot concept in 2020.
According to an article by TechHQ, observability, the ability to know what’s going on with data, from the of uncharted, unstructured data in the data lake, up through infrastructure layers and onward to the user interface level, is expected to be the buzzword for 2020.
Observability is a comprehensive concept. Since observability is about understanding the requests that happen throughout live operations, it will incorporate log file analytics, including every piece of software that performs any single move in any form, as well as a higher-level understanding of how the cloud services that support apps are responding to limitations and spikes in demand. According to Wikipedia, “observability is a measure of how well internal states of a system can be inferred from knowledge of its external outputs. The observability and controllability of a system are mathematical duals.”
The scope of observability includes contributions from what is called the four pillars of the observability:
- Distributed systems tracing infrastructure
- Log aggregation/analytics
Observability is, then, a superset of monitoring–a set formed from a collection of other sets, that uses newer intelligence aids including artificial intelligence (AI) and machine learning (ML) to help build the observed vision of the system. It is expected that this approach to data management will develop over the year with applications yet to be determined.
4. We’re debunking 8 myths surrounding AIOps.
Humans have been wary of AI for a long time. Maybe it’s because they’ve seen it abused in the tear-jerker AI (“Please make me a real boy”), or they fear Hal the homicidal computer from 2001:A Space Odyssey (“I’ve made some very poor decisions recently”), or the world of the entire Terminator franchise (“Come with me, if you want to live”), or even the early, mechanized nightmare of Metropolis (from 1927).
An article by InfoWorld lists eight myths generated by human brains and explains the realities, so we can all breathe easier when AIOps is adopted and improves our lives.
- Myth: AI is going to take away jobs. Reality: AI will change existing jobs and create new ones. AI is being used to automate many tedious, repetitive functions. This does not mean people will be out of a job—it means they are free to work on more challenging tasks.
- Myth: AI is smarter than people. Reality: AI is as smart as you program it. There is no artificial intelligence without people, the people who create the algorithms and information that make up AI. We build it, teach it, and give it the tools to make certain decisions on our behalf.
- Myth: AIOps is predominantly based on event management and correlation. Reality: Maybe at first, but it is evolving. The initial wave of AIOps did revolve our event management systems to perform noise reduction based on correlating alerts, but even greater benefits are on the horizon. The next wave has broadened to other areas of ITOps, such as automation and monitoring/observability.
- Myth: Companies don’t need an AI strategy. Reality: Oh, yes, they do. It is a risky proposition not to have an AI plan because your competition certainly will — and they will be able to respond to market changes much quicker.
- Myth: AI will make medical decisions and diagnoses. Reality: Yes, but AI won’t have the last word. AI can scan millions of images to interpret them faster and more thoroughly than any human ever could. However, a doctor or radiologist will still have the final call in determining a diagnosis. It’s just that a diagnosis may come in minutes instead of days or weeks.
- Myth: I have no idea what the AI is doing and if I can trust it. Reality: AI is much more transparent now. Early on, AIOps was perceived as being a mysterious “black box” system that generated output without providing insights into what the underlying algorithm did and why. Over time we have seen these solutions mature and more “white box” approaches that are gaining trust and adoption.
- Myth: I need a data lake to train my AI. Reality: Kinda’—You need to drain the data swamp. Unstructured data is worse than structured data because it takes up space. AIOps helps fix this by enriching data and turning data swamps into clean data lakes.
- Myth: Modeling determines the outcome. Reality: You can’t be certain of that. You may get excellent results during the testing phase but find that your model is far less accurate when you deploy it into production. That’s because AI and machine learning models must be trained on data, and that training data must be representative of the real data, or results will suffer.
Today may be Backward Day, but there’s no turning back from digital transformation. Whether you are exploring observability, tackling IT obstacles, or moving financial institutions to the cloud, the only way business is moving is forward. Digital transformation isn’t the future, it’s here. AIOps is the right path to be on—and that’s no myth.
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