|By Sebastian Kruk||
|March 24, 2014 07:00 AM EDT||
User experience can boost or kill your revenue. Unhappy users are likely to abandon a service they struggle with and go to your competitors. To effectively manage the experience of your users, you need to efficiently monitor and understand their transactions in your mobile, web and enterprise applications. More important and often overlooked, the practice of User Experience Management (UEM) does not end in the client application. Common user experience tools fail to ensure holistic UEM the same way many think performance management is only based on analyzing server logs. Neither approach will shed light on the true user experience.
UEM is one of the key aspects of the Application Performance Management. It can be realized through distinct technologies. In this article, we discuss why none one them can be the single solution to ensure enough visibility. We argue that a true, end-to-end UEM should rely on data gathered using different monitoring technologies.
Technologies for End-User Experience Monitoring
Although UEM is only one of the dimensions realizing APM, it is the one that gets most attention. End-user experience is the point where the business processes interact with the technology stack. We can define three types of technologies for UEM from different perspectives (see Figure 1) as:
Figure 1: Comparison of three perspectives of UEM
- Synthetic, Transaction-based UEM: Using synthetic scripts we can monitor web and intranet applications from different locations on both internet and internal WANs, using mobile or web client applications. This allows us to effortlessly simulate multiple end-user environments during off-peak or non-business hours to check service levels against the SLA. This gives you constant, repeatable and comparable measurements ready for comparison and baselines. It resembles testing car safety with dummies: you learn a lot about car safety but it might not directly translate to the experience of the actual passengers in real life situations (see Figure 2).
- Endpoint instrumentation: When we look at user experience from the perspective of the endpoint (mobile client or web-based) instrumentations we even get data to analyze user behavior. Nevertheless, performance analysis based on the endpoint instrumentation alone is like looking only at the car speedometer and a few other gauges at the dashboard in your car: it gives you plenty of information about your speed, RPMs and gas level but if the engine blows a piston stopping your car, your dashboard alone won't tell you the source of the problem... it just says your speed is zero. Endpoint instrumentation is also very dependent on the client's technology stack with which it has to interact seamlessly.
- Network Packet Capture and Analysis within the Data Center: Analyzing network traffic across the whole data center all the way to the end user application enables you to correlate end-user experience with the actual state of the network and services; it remains the only feasible UEM solution when the client front-end cannot be instrumented, but still uses TCP to connect to the data center. This perspective however, remains blind to the impact of everything that happens outside of Data Center, e.g., third-party components. Similar to Endpoint Instrumentation, all the car dashboard instruments might not always accurately correlate to the quality of our journey.
Figure 2: Automotive analogy for UEM: each perspective tells only part of the story
To read more, and learn additional insights, click here for the full article.
The essence of data analysis involves setting up data pipelines that consist of several operations that are chained together – starting from data collection, data quality checks, data integration, data analysis and data visualization (including the setting up of interaction paths in that visualization). In our opinion, the challenges stem from the technology diversity at each stage of the data pipeline as well as the lack of process around the analysis.
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