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Analytic Application Performance: Lessons from the Gaming Industry

By Natasha Callender
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On October 13, 2019, Fortnite, the popular online video game, went unexpectedly dark for a period of 36 hours. Instead of simply managing an outraged user base, Epic Games, Fortnite’s developer, orchestrated a media event that generated intrigue and excitement that prompted more curious onlookers to log on to see what the hype was about.

The three questions I previously outlined continue to apply here.

Is the application available?

First, we ask, is it available? This depends on who is asking and why they are asking. Epic Games knew Fortnite was not going to be available and had a plan for its users.

Fortnite’s users were not informed of the planned blackout beforehand. Instead, they logged on and were entertained with visions of a black hole tearing into their worlds in a celestial spectacle. The Epic Games Twitter account and website were completely silent on what was happening. Users were scrambling to find answers, creating more buzz and generating a frenzy of media attention.

During unexpected blackouts, other major game outlets typically face disgruntled users and the wrath of social media. The genius in Fortnite’s strategy is that they understood their users well enough to orchestrate an unforgettable user experience during an otherwise mundane, routine maintenance. Users come to Fortnite expecting to be entertained, and Epic Games delivered on the promise.

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Similarly, knowing who your users are and what’s important to them will inform how you optimize the performance of your analytics application and what you focus on. This, in turn, saves you time, gives you clear priorities, helps you anticipate issues, and minimizes the impact of unexpected problems on your end users.

The end users of your application are busy, time-starved, and don’t want to break out of their workflow—they are primed to work in a fast-paced and seamless environment. You need to think of your applications’ performance in terms of what is important to your users and their use cases. This brings us to the next question.

Is the application fast?

Assuming your application is up and running, is it fast? In an extensive survey of gamers, fast performance was ranked as the top priority. Speed is essential for the ultimate gaming experience. To the gaming industry, low latency is critical to success. Likewise, for your analytic application, speed is a non-negotiable feature in terms of user experience. A fast application will yield better retention, greater user engagement, and higher conversion rates.

The Neilsen Norman group (NN/g), leaders in research-based user experience, released a study showing that users have between .01 second and 10 seconds before they start losing attention spans. For analytics applications, this window may be a little wider as users are accustomed to the wait time for reports to run or download. Nevertheless, NN/g suggests that in cases where there is a lag in response time, you should provide progress indicators, or load other components to make the wait less painful.

Can your users do what they need to do, seamlessly?

Finally, your user groups and user personas have specific stories and journeys—can they follow the journey as expected? Once the server maintenance was complete and the game was up and running, Fortnite, no doubt, had to ensure that players could return to business as usual.

For your analytic application, consider the following guidelines:

  • Whole system thinking: Design, build, and test your application with the whole system in full view. This means that even as you organize your application into its logical layers, consider how each layer affects the end user.
  • Get the right metrics: Every layer in the stack has its own monitoring tools and methodology. Paying attention to the wrong metric can quickly lead you down a rabbit hole. You may work hard to reduce your server response time, but if the page has too many components and it takes forever to load, the end user will not care that your server speed is optimal.
  • Test early and often: You can’t start designing and testing for optimization early enough. In your design and build phase, consider optimizing the analytics database and queries to make data retrieval faster. Does your application architecture incorporate performance best practices? Are your static assets being managed in the best way possible for your use case? Work through each layer identifying what makes the biggest difference from your user’s viewpoint and design and test for it throughout your build.

Remember, your end users are primed by their web consumer experiences. They expect a fast, seamless, and even delightful experience as they book flights, purchase goods, shop for houses, play games, and seek to be entertained. They are bringing similar expectations to the workplace, and, specifically how they interact with your application. Unscheduled downtime with visions of black holes will not necessarily excite and entertain business users, but it’s still worth emulating Fortnite’s insight on the importance of knowing your end users and prioritizing their experience.

Every data-driven project calls for a review of your data architecture. Download the white paper to see the 7 most common approaches to building a high-performance data architecture for embedded analytics.

Originally published December 5, 2019; updated on December 15th, 2019

About the Author

Natasha Callender is a Solutions Engineer at Logi Analytics. She has devoted her career to exploring how technology can transform the way the we live, work, and do business.

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