I enjoy the end of the year. Getting some downtime from the constant phone calls and meetings allows me to reflect and plan for a new year ... Planning for a new year often includes predicting what’s going to happen. However, 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.
Many of the 2017 predictions were not particularly predictive, but instead "observations" of what is happening and how it will accelerate or shift. Without better ground rules for what a prediction is, it's hard to say what the list is and is not. I have picked out a few key areas which encompass several of these predictions. I'm not calling out the individual predictions, which often do not align with Application Performance Management (APM) trends, but instead, serve a specific vendor (gotta love Marketing).
Review the 2017 APM Predictions on APMdigest
The first four key areas below are the predictions that didn’t come true. In addition to the the ones I have called out here, there were many predictions which have been failing for years that vendors wish would come true. Unfortunately for them, that didn't happen in 2017. Hopefully, they don't post the same prediction for 2018. :)
The following few 2017 predictions had several predictions which fell into a category:
The convergence of infrastructure metrics and APM metrics
Although many tools combine APM and infrastructure metrics, most enterprises still use separate tools due to organizational silos
Although many tools combine APM and infrastructure metrics, most enterprises still use separate tools due to organizational silos. The enterprises which run significant infrastructure continue to deploy monitoring tools for each team — meaning teams running data centers still buy tools for DCIM, network, servers, storage, and other infrastructure technologies. Those implementing cloud infrastructure often use cloud provider tools or other infrastructure monitoring tools which do a better job in those environments, and better handle time series. APM tools, have for a while had lightweight capabilities for infrastructure monitoring, and often may replace more capable infrastructure monitoring technologies, especially when migrating to managed data centers or public cloud.
Adoption of platforms to tie monitoring data together
While there are many products which attempt to tie together data coming from multiple monitoring tools, the integration posts challenges for most. Although some of these products are gaining adoption, it's not impactful to the market today. We haven't seen a shift in this market in 2017, and I doubt it will change in 2018.
AI replacing manual analysis
My perspective on AI is documented. AI is still not prominent in solutions today, even though people have been predicting it for years. The advances in making RCA faster have been helpful to users, but the manual analysis of data continues to be standard today, and likely will be for the foreseeable future. We didn't see a shift in 2017, but as I talk about below, predictive analysis and machine learning are becoming more prominent.
Data-centric to event-centric
BUT WILL BECOME TRUE AT SOME POINT
This one was ahead of its time and is a trend which has been occurring for several years. Instead of event-based systems replacing data based systems, I believe there will be an augmented platform to handle both request-response systems and data-based systems. There are many business reasons to have both capabilities within software architectures, and monitoring will naturally have to evolve to handle both types of software systems. Today most of the "event-based" monitoring systems are done with log analysis tools, as tracking events over time is often a challenge for most monitoring tools. There are some which have been able to show change over time, but not managing of event-based architectures. This will be a continuing trend driven by event-based programming and push models for notifications commonly found in technologies built on top of technology such as Node.js or other event-based frameworks.
Read Looking Back at 2017 APM Predictions - Did They Come True? Part 2, outlining the 2017 APM Predictions that came true.
The Latest
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 ...
