For businesses today navigating hyper-connected and hybrid environments, the competitive differentiator to set them apart is the ability to deliver stellar digital experiences to every user, everywhere. That’s not a trivial task, though, given the continued move to cloud and SaaS has left organizations dependent on external networks and the public Internet itself, as the service delivery mechanism that stands between the brand and the ultimate end user experience for their customers and employees.
We all know the fickleness of an Internet connection. Using the Internet to connect with customers and employees means there’s zero certainty in knowing if your app and its multitude of components distributed across multiple cloud environments is actually performing up to scratch.
So, when it comes to improving the end user experience, which will ultimately protect customer loyalty, brand assets, and even worker productivity, how can the quality of application experiences be assured? Additionally, for organizations aiming to be more proactive and automated in how they operate and manage their environments, can a predictable experience be delivered over an inherently unpredictable environment?
Internet and cloud networks may sit beyond our control, but new technologies allow us to see them like we own them—and now, even proactively managing them.
Group Product Manager for EMEA at Cisco ThousandEyes.
From reactive to preventative
Rising expectations with always-on digital experiences from users, means it’s no secret that IT operations are challenged right now. Firstly, being dependent on external environments running in the cloud and over the Internet means that anyone tasked with troubleshooting performance issues has become blind to disruptions that sit outside the enterprise perimeter and beyond their control.
What’s more, today’s networks are growing rapidly and the sheer volume of data points across thousands of sites, networks, paths, applications, and remote users has become difficult to assess and analyze. Even if you gain visibility into what’s happening under the hood of your ecosystem, sifting through the noise to understand what’s important and what requires immediate attention can be a challenge within itself.
So, what’s the solution? We need to bring in as much predictability as possible into these inherently unpredictable environments. Predictive intelligence is all about the ability to move from reactive to preventative troubleshooting, locating issues before they begin to impact the user experience. It has the potential to completely transform the daily lives of IT operations teams. For example, proactively identifying a single service affecting fault and remediating it — such as by switching providers and paths that carry app traffic during peak periods — could save a single employee hours of downtime or degraded performance.
Visibility across domains
Imagine if a major streaming service could predict possible service disruptions to its content delivery network (CDN) provider between 10am and 11am next Wednesday? Or better still, what if said streaming service could understand the best network to re-route its customer traffic and automate this to avoid customer and employee disruption before they even happen?
To predict the beginnings of a degradation or performance deterioration with a high degree of accuracy, you need to be able to look back far enough into the past at the historical data that has been collected. While storing large amounts of data isn’t necessarily a problem anymore, deciding on how far back we need to go when analyzing the data in order to gain some predictive power isn’t always straightforward. To make things even more complicated, we usually have to work with multiple datasets coming from different parts of the network in order to understand what is driving the service degradation and those datasets could have varying degrees of quality and cleanliness. Poor data quality will always have follow-on effects in the predictive models that are being built and without quality data, granular assessments and actionable recommendations are not possible.
Leveraging a rich, high quality data set together with visibility, helps analyse historical patterns across owned and third party networks, which provides the ability to proactively prevent degradation, before the end user digital experience is impacted. In turn, IT teams are better equipped to move away from the cycle of ‘fix and repeat’ by enhancing network visibility, establishing historical benchmarks and even expediting root cause isolation.
A new paradigm
Today’s hybrid work environments are incredibly complex and made up of highly sophisticated applications that are distributed across multiple networks and accessed by various end users across the world. With an ever increasing reliance on the performance of these applications, any disruption can have a direct impact on users’ digital experience and therefore, their productivity and satisfaction. So, in other words, the longer it takes to remediate an issue, the bigger the impact can be to the end users.
Ultimately, operating a reactive model no longer fits the bill. Taking a proactive and preventative approach is the new paradigm required. This starts with visibility and ends with trust. Visibility into cloud and Internet environments but also insights into past events to predict future ones, is fundamental. And trust in the data that a predictive-led recommendation will trigger an intended action. In turn, this data will help businesses thrive in today’s Internet-centric world.
