Digital Experience Monitoring: A Lakeside Software Webcast Featuring Gartner

Featuring Will Cappelli (Research VP, Gartner) & Patricia Diaz (Head of Product Marketing, Lakeside Software)


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VIDEO Today, complex IT environments are inevitable. The boundaries between IT and service providers, applications and services, and enterprise and consumer hardware are blurring. Although this complexity has helped IT become more productive, it is also a concern for groups who understand the quality of digital interactions between end-users’ processes and these technologies have direct correlations to business productivity. That is why organizations are turning to digital experience monitoring technologies for endpoint visibility into the state of IT. But what is digital experience monitoring and how can it help you enable productivity? In this webcast, find the answer to these questions and more, from Gartner Research VP, Will Cappelli and Lakeside Software’s head of Product Marketing, Patricia Diaz.

Will: 00:01:12 – Digital experience monitoring is a collection of technologies and processes that are targeted at the observation and analysis of the interactions between an agent, a human agent, or possibly even a purely digital agent, with the services provided by an enterprise. And it is critical to understand that we’re not talking about the interaction between an agent and a single service, we’re talking about the interaction between an agent and the entire array of services and applications that are being delivered by an enterprise within the context of a digital business process.

00:02:22 – Why is digital experience monitoring important to the overall digitalization of business processes? Well, these are the technologies and processes that allow an enterprise, both at the technical level and at the business decision-making level, to observe how agents are executing digital business processes, or also to observe and analyze and understand how agents that are recipients of the products of a digital business process, the services, which are the outcomes of a digital process, are consuming those services and most importantly, the quality of the experience that they have while those services are being consumed.

00:03:31 – Digital experience monitoring began as a subset of the functionalities that are typically included under application performance monitoring. Historically, enterprises would monitor the experience of end users of applications, in order to get a good sense of how an application performed end to end. Unless they could see what the end user was experiencing, they would not be able to truly see what was going on in the last-mile, so to speak, of that application’s execution.

00:04:27 – Over the last two years or so, Gartner has seen that increasingly, enterprises are segregating their choices with regard to digital experience monitoring technology, from their choices regarding application performance monitoring and technology. But it’s not just the question of separating out products or services and buying them separately, there has also been a major shift in the reasons why enterprises are interested in capturing this experience, or capturing metrics that describe the quality of these experiences. Once again, within the context of application performance monitoring, understanding that experience as a digital agent was one element in understanding how a given application was performing.

00:05:31 – Within the context of digital experience monitoring, enterprises are interested in understanding the totality of a digital agent’s experience. Not just that digital agent’s experience of a single application, but that digital agent’s experience of the entire portfolio of applications and services that the enterprise is presenting to it. And by doing so, by focusing on the integral experience of the digital agent, one is able to get a good understanding of how that agent is placed within and reacting to the digital business processes, which multiple applications support.

00:06:26 – So once again, end-user experience monitoring within the context of application performance monitoring is about a one-to-one relationship between the end user, a digital agent, and the application. Digital experience monitoring is about the many-to-many relationships between communities of digital agents, on the one hand, and whole portfolios of applications and services offered by an enterprise on the other hand. As a consequence of the shift in focus from application to the digital agent, different kinds of monitoring technologies are required, more importantly, different kinds of analytics are required in order to build that integrated comprehensive portrait of the digital agent’s entire experience.

00:07:37 – Digital experience monitoring interacts with a number of other very important trends in the market today. Workspace analytics, the technologies and processes which are targeted at observing and understanding user productivity in the workplace, is greatly enhanced by digital experience monitoring technology. Digital experience monitoring technology is able to capture fine-grained information about user behavior. It’s able to provide the analyses that allow a business decision-maker to understand which services that are supplied to the end user by the enterprise, are leading to greater productivity, which are not making a difference and which are negatively impacting productivity.

00:08:45 – Without digital experience monitoring, workspace analytics has to deal with a very thin stream of data. And indeed, Gartner believes that in the future, digital experience monitoring technologies and processes will come to be seen as an absolutely vital component of a successful workspace analytics strategy.

00:09:14 – The relationships between digital experience monitoring and artificial intelligence, AI, are somewhat more complex and subtle. AI means many things today. Within the context of our discussion, the most important elements of AI are those algorithms which are able to discover patterns in large volumes of data and then use those patterns to determine what are the root causes of events that are taking place and also to use those patterns to determine what events are likely to take place in the future.

00:10:06 – Now one of the most complex agents of behavior are these digital agents that are interacting with digital business processes. And so AI becomes tremendously useful in helping digital experience monitoring technologies, and the associated processes, develop an understanding of how digital agents are likely to act within the context of a digital business process. So, digital experience monitoring technologies are likely to embed within themselves quite a lot of AI expertise. And in order to effectively use digital experience monitoring technologies and to use those processes, enterprises need to develop a good understanding of the kinds of things that AI can bring to the table. This is not to say that you need to be an AI expert in order to use these technologies, but it does say that you need to appreciate the role that AI plays in delivering the results that these technologies can deliver, and that you want to make digital experience monitoring part of an enterprise’s overall AI strategy.

00:11:53 – There is another element, which is important to keep in mind, that many of the agents that are interacting with a digital business process, are not necessarily human agents. They could very well be software bots, for example, and the more sophisticated of these software bots, will themselves be programmed based upon AI principles. So in order to understand the agents, whose experience you’re trying to monitor using digital experience monitoring and technology and processes, you will once again have to understand the basic principles of AI, this time, not in order to get the most out of your tools and processes, but to actually understand the objects that you are trying to monitor.

00:13:00 – So here too, it is important to keep in mind that as an enterprise is developing their overall AI strategy, that digital experience monitoring needs to be a very core element of that strategy.

00:13:29 – Digital experience monitoring brings multiple technologies into play, in order to build that comprehensive picture of the experience of the digital agent. From the perspective of data collection or ingestion, there are four fundamental technologies. First of all, there is the use of synthetic transaction generation. In this case, a technology or service will be taken by an enterprise that will allow that enterprise to build, effectively, synthetic behaviors that can be played out against a particular service, a particular application. Those synthetic behaviors run, the results are recorded and then analyzed.

00:14:31 – Secondly, an enterprise can use technologies that will extract data directly from the network, usually in the form of packets consumed through spam ports, and the HTTP content of those packets will be observed and inferences will be made from that HTTP content, to metrics that describe the end-user’s experience.

00:15:04 – Thirdly, it is possible to inject into webpage code, little snippets of JavaScript, so that when that webpage code is rendered on any kind of endpoint device, those little snippets of JavaScript begin to act like mini agents and send back signals to some central point for observation and analysis.

00:15:36 – Finally, it is possible to insert a full-blown agent directly into the endpoint device, and once again, this full-blown agent extracts data and sends it back to some central location for observation and analysis.

00:15:55 – So those are the four types of technology that are used, typically, to ingest data, and it’s important to state that the digital experience monitoring capability need not use all four types of data ingesting mechanism, but usually it is advisable that at least two types of data ingestion mechanism are used in order to triangulate results.

00:16:25 – Beyond the data ingestion, the most important elements are the abilities to aggregate that data and aggregate those different types of data streams and come to some kind of triangulated summarization of that data. And then beyond that, and very importantly, the ability to apply analytics to those aggregated data streams. It’s really within the analytics that the magic happens, so to speak. It is the analytics that will turn those streams of data, typically very large streams of data, streams of data that are varying very, very rapidly, and will extract the meaning from those steams of data and allow you to understand both what is going on at that point in time, but also allow you to go backwards into time to pick out the causes of what is taking place now, and to go forward into time to predict the kinds of things that are likely to happen.

00:17:44 – Those analytics are very typically assisted by some kind of machine learning or AI algorithm, as we’ve discussed before, because the patterns within those data streams can be so complex and so subtle, and the datasets so large, that even a very experienced human observer simply can’t see those patterns. 00:18:16 – So once again, when you think of the digital experience monitoring capability, think of it in terms of three layers – the data ingestion layer, which itself may be broken down into one, two, three, or four different types of data streams; the data aggregation layer, and then most importantly, the analytics and machine learning layer that will allow you to extract the patterns from those large complex streams of digital agent experience data.

Patricia: 00:19:08 – Lakeside Software has been a leader in workspace analytics for 20 years, and our success has been due to our long-held belief that the end user experience is the most important metric in IT. Now why is that? Well, we have found that as critical IT functions are being managed by and outsourced to third parties, the employee workspace has become the most privileged point of view for IT. As a matter of fact, over the past few years, we have seen an increased interest in digital experience monitoring technologies. An interest that is due in large part to organization’s decreased control over and, therefore, decreased visibility into, the technology estate. That is network transactions, datacenter transactions and the overall IT infrastructure.

00:20:05 – So what was once a seemingly simple question as how has some technology we have deployed impacted business productivity, has become an increasingly more difficult question to answer. And it is because of that increased complexity and decreased visibility into IT.

00:20:25 – So when it comes to digital experience monitoring, we believe that there is a strong benefit to a holistic workspace analytics solution, one that is invisible to the end user, that has nominal impact on endpoint performance, and that provides that visibility into the health and the state of IT.

00:20:55 – Workspace analytics is the framework that finds meaningful insights into business productivity, by analyzing end users, the business processes that encompass their jobs, and the technology they use to get work done. Now, a holistic workspace analytics solution is one that is able to account for those three key main areas – people, processes, and technologies – and measure and optimize business productivity accordingly.

00:21:27 – So when it comes to tracking, analyzing and optimizing those three key areas, a workspace analytics solution should have three main key technologies.

00:21:42 – The first technology I wanted to highlight today is the topic of our discussion, which is digital experience monitoring. Now digital experience monitoring tracks the health of the business by monitoring and most importantly analyzing end-user experience and the end-user’s interaction with, and usage of, all the technologies and business processes provided for by an organization.

00:22:07 – So a great example of digital experience monitoring is what we call proactive support, where the user experience is monitored and given an end-user experience score. That if below a certain threshold, IT can work to improve by solving any issues the user is having before they file or request help from IT support, and many times even before they even know they are experiencing poor performance.

00:22:39 – The next technology I wanted to highlight is asset optimization. And asset optimization ensures that the cost of technology is optimized for the needs of the users. It functions under the understanding that technology should always enable and never inhibit business productivity. So a great example of asset optimization, in practice, comes from when organizations are looking to decide on Office 365 licensing, where we have seen many of our customers overspend on E5 licenses, when they could have, in fact, invested in E3 licenses, based on user need.

00:23:26 – So what this tells us is that there is a very important difference that needs to be understood between licensing that is installed and licensing that is utilized, because this could translate to millions in licensing costs to some larger organizations.

00:23:46 – The last technology I wanted to highlight today is event correlation and analysis, which we like to think of as the IT time machine. Now, event correlation and analysis within workspace analytics solutions have the end-goal of solving IT problems quickly and ideally, automatically. So some great examples of event correlation and analysis include being able to find the root cause of system failures and be able to predict and resolve them the next time they happen; being able to know the first time ransomware infects an organization and be able to record and report on its footprint; and finally, one of our most popular use cases is being able to know who is accountable at the moment of isolated _____, be it the user, IT or the service provider. These are all capabilities that a well integrated event correlation and analysis solution should be able to provide.

00:24:56 – Our customers justify the return on investment for digital experience monitoring solutions with quantifiable improvements to business productivity and workspace availability. Our SysTrack solution features a unique end-user experience score that, as you would think, measures and quantifies an employee’s end-user experience. And what we have found through interactions with our customers is that this end-user experience has a positive correlation with business productivity. So if we ask ourselves what is the cost of reduced employee productivity or downtime, whatever the line of business, that cost is probably more than an organization would like to bear.

00:25:49 – So when it comes to financially justifying investments in digital experience monitoring, our customers understand it very clearly and it is that the better insight they have into their employee’s end-user experience, the better insight they have into their employee’s productivity within the digital workspace.

00:26:19 – Just as we are living in the era of the quantified self, we at Lakeside Software believe the enterprise is entering the era of the quantified user. Now the end-user experience score is able to quantify the end-user experience on a scale of 0 to 100. It is composed of several key performance indicators that may be impacting the end-user’s experience, from networking, to resources problems, to infrastructure and system configuration issues. The result is a normalized score that is able to be compared across teams and groups of users, and that feature two key benefits.

00:27:04 – The first one is a broad and granular visibility into what is impacting business productivity, and the second is improved business visibility into the health and the performance of IT investments, be it immediately, or historically.