We don't often enough look back at the prior year’s predictions to see if they actually came to fruition. That is the purpose of this analysis. I have picked out a few key areas in APMdigest's 2017 Application Performance Management Predictions, and analyzed which predictions actually came true.
Start with Looking Back at 2017 APM Predictions - Did They Come True? Part 1, to see which predictions did not come true.
The following predictions were spot on, and outline key shifts in the landscape for 2017:
Confusion around AIOps
GARTNER RENAMED IT, WHICH WAS THE PLAN ALL ALONG
AIOps tools today are not a reality, but hopefully it will happen over time
Any time there is a shift in technologies, where vendors are moving from an older technology concept to a newer one Gartner adapts the market definition. In the case of ITOA, as the core concept was reporting on data, which needed to, and eventually moved towards automated analysis of data via machine learning (ML). At the time of advancements in ML Gartner shifted the definition from ITOA to Algorithmic IT Operations (AIOps). Vendors began adopting and applying these new capabilities, and AIOps was becoming a reality. The next phase is automating these analyses and taking action on the data and insights. Hence Gartner changed it to Artificial Intelligence for IT Operations and expanded the scope significantly. AIOps tools today are not a reality (see reasons above), but hopefully it will happen over time. This shift was always the plan at Gartner, but something which needed to evolve over a couple of years. The adoption of ML has been rapid, but we are a far cry from true AI today, even when vendors claim they may have it. They do not, at least not unless they are IBM, Google, Facebook, or a very small handful of other companies. Most vendors in the IT Operations space are not yet taking advantage of public cloud providers’ AI platforms.
Better predictive analysis and machine learning
This one was spot on, we've seen a speedy adoption of more advanced ML, and better predictive capabilities in most products on the market. Although some vendors have had baselining for over a decade, now all products do some form of baselining in the monitoring space. Much more work is being done to improve capabilities, and it's about time!
APM products increasing scale
BUT STILL LACK MARKET LEADING TIME SERIES FEATURES
In 2017 APM products have begun to scale much more efficiently than in the past (with a couple of exceptions), but there is still a lack of market-leading time-series features in APM products, especially when looking at granular data (second level). There is yet another set of tools used for scalable and well-visualized time series from commercial entities and open source projects. I expect this to change eventually, but for now, we have fragmentation in this area.
APM tools evolve to support serverless
This prediction came true in 2017, but defining what "support" of serverless (which I prefer to call FaaS) entails is a nebulous term. Most APM tools support collecting events from the code, which require code changes. Code changes are not ideal for those building or managing FaaS, but that's the current state. FaaS vendors are quite closed in exposing the internals of their systems, and some have provided proprietary methods of tracing them. I predict this opens up in 2-3 years to allow a more automated way of monitoring FaaS.
APM in DevOps Toolchain
This one has been true for the last 4+ years in fact, but as toolchains increase in complexity the integration of APM into both CI and CD pipelines continues to mature. In the CI/CD space, more advanced commercial solutions include better integration with APM tools as part of their products. Increased polish is needed, and will continue over the coming years.
Hybrid application management
HAS BEEN TRUE FOR YEARS
Hybrid has been typical for a while now and hence is not a prediction but a historical observation. APM tools running at the application layer have been managing across infrastructure for years, I would guess 8+ years, in fact. Today's applications are increasingly hybrid, meaning they encompass several infrastructures, languages, and frameworks. Due to this diversity, APM is critical in managing highly distributed interconnected applications.
APM + IoT
BUT HAS BEEN HAPPENING FOR YEARS, AND NOW PRODUCTS BEGIN TO EMERGE
Please provide feedback on my assessment on twitter @jkowall or LinkedIn, and if you enjoyed reading this let me know and I’ll be happy to provide my analysis of the 2018 APMdigest predictions next year!
Achieving audit compliance within your IT ecosystem can be an iterative process, and it doesn't have to be compressed into the five days before the audit is due. Following is a four-step process I use to guide clients through the process of preparing for and successfully completing IT audits ...
Network performance issues come in all shapes and sizes, and can require vast amounts of time and resources to solve. Here are three examples of painful network performance issues you're likely to encounter this year, and how NPMD solutions can help you overcome them ...
"Scale up" versus "scale out" doesn't just apply to hardware investments, it also has an impact on product features. "Scale up" promotes buying the feature set you think you need now, then adding "feature modules" and licenses as you discover additional feature requirements are needed. Often as networks grow in size they also grow in complexity ...
Network Packet Brokers play a critical role in gaining visibility into new complex networks. They deliver the packet data and information IT and security teams need to identify problems, recognize security issues, and ensure overall network performance. However, not all Packet Brokers are created equal when it comes to scalability. Simply "scaling up" your network infrastructure at every growth point is a more complex and more expensive endeavor over time. Let's explore three ways the "scale up" approach to infrastructure growth impedes NetOps and security professionals (and the business as a whole) ...
Loyal users are the key to your service desk's success. Happy users want to use your services and they recommend your services in the organization. It takes time and effort to exceed user expectations, but doing so means keeping the promises we make to our users and being careful not to do too much without careful consideration for what's best for the organization and users ...
What's the difference between user satisfaction and user loyalty? How can you measure whether your users are satisfied and will keep buying from you? How much effort should you make to offer your users the ultimate experience? If you're a service provider, what matters in the end is whether users will keep coming back to you and will stay loyal ...
What if I said that a 95% reduction in the amount of IT noise, 99% reduction in ticket volume and 99% L1 resolution rate are not only possible, but that some of the largest, most complex enterprises in the world see these metrics in their environments every day, thanks to Artificial Intelligence (AI) and Machine Learning (ML)? Would you dismiss that as belonging to the realm of science fiction? ...
Of those surveyed, 96% of organizations have a digital transformation strategy, with 57% approaching it as an enterprise-wide priority, with a clear emphasis on speed of business, costs, risk, and customer satisfaction, according to IDC’s Aligning IT Strategies and Business Expectations for Digital Transformation Success, sponsored by EasyVista ...
One of my ongoing areas of focus is analytics, AIOps, and the intersection with AI and machine learning more broadly. Within this space, sad to say, semantic confusion surrounding just what these terms mean echoes the confusions surrounding ITSM ...