Speed is increasingly defining the user experience from B2C to B2B. No matter how attractive the application, if it does not perform from a speed perspective, it might as well be ugly because that is the sentiment increasingly attached to slow, plodding applications on the web, mobile and in the enterprise.
To be fair, speed comes in different flavors. There is speed from the perspective of eliminating clicks or reducing the number actions to achieve an outcome. There is also raw speed, the speed of the response. One is a user experience challenge, the other a software development task. Ultimately, however, they need to overlap to achieve the right business outcomes.
Great UX is no longer just about great design, its is about the speed at which a user can render, interpret or complete their task. UX certainly plays a role in guiding the user to the right outcome, but the speed at which that happens matters greatly.
If a user has to wait on the application to process at each step, the user experience degrades considerably.
The Financial Times recently injected small delays into their story loads and found that, not only did viewers engage less, but that engagement continued to deteriorate over time. The persistence of the annoyance, whether re-enforced by other web experiences or not, drove less and less usage of the site.
This led them to conclude:
While the preceding statistics and case studies deal predominantly with B2C use cases - the research is even more emphatic when it comes to database performance and visual analytics.
In the seminal paper by Heer and Liu we find that as little as 500ms latency can have a significant impact on visual analytics and that those delays impact how much of the dataset is explored, the speed at which insights are ultimately gleaned and that the negative externalities associated with the delay persists beyond the elimination of latency.
Underpinning this research is the fact that while any experience can be slow, experiences that involve large amounts of data are far more susceptible to degradation - which is exactly the problem faced by enterprise analytics applications today.
With data sizes growing at such a tremendous pace, responsiveness has been impaired. Query times balloon, render response lags. The front end blames the backend, the backend blames the frontend, everyone blames the network.
The truth of the matter is that most analytics and visualization platforms are all ill-suited to the world we live in today. They were built for the gigabyte economy. We don’t live in that world today. We haven’t for a few years now.
More importantly, organizations are not fully appreciating the impact of latency.
They make incremental improvements and claim success. Reducing query time from twenty minutes to twelve results in a great percentage to put in front of the leadership, but it has not really moved the needle. Your best analytical assets are still waiting far too long.
Latency creates a series of bad behaviors in the analytics function, behaviors that compound over time and are yet further exacerbated by the emergence of the citizen data scientist within organizations.
Here are the big impacts of enterprise application latency that need more attention and appreciation inside of organizations:
When it takes too long to get answers from their analytical tools, analysts and other consumers of data start to ask only those questions for which they know they can get an answer for in a reasonable amount of time. Further, both the question-maker and the answer-getter will fight hard to defend the “rightness” of those questions, because they represent success. This leads to myopia. Myopia was bad when Theodore Levitt wrote about it. It is even worse today. We don’t always know what we are looking for, which is why the promise of unsupervised learning is so powerful. But even with unsupervised learning, it narrows the playing field, it doesn’t magically provide the answer.
