The 5 Best Methods for Drawing Insight out of Machine Data

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Event Tracking

This is when a user interacts with a website or application, and their actions trigger events. For example, an application can be set up so that when a user clicks “submit” on a registration form, they trigger the “registration” event, which is then captured by the tracking service. Data includes any events that have been defined for the application. Generally a JavaScript tag is installed on a web app, and an SDK is used for native applications.

Why is this important? Event data is valuable to the product and marketing teams for understanding how users navigate through an application as well as any areas of friction in the UX (user experience). Generally the event data is visualized within UX flows as a funnel, with the wide top of the funnel representing the area of the application where most users start their interaction and the narrow part at the bottom of the funnel being the desired user action.

The pursuit of data-driven decision making has put tracking, logging and monitoring at the forefront of the minds of product, sales and marketing teams. Engineers are generally familiar with gathering and tracking data to maintain and optimize infrastructure and application performance. However, with the power of data, other business groups are clamoring for the latest tools and instrumentation. Quite often the expense of implementation is undersold as merely placing a tag on a site or adding a library, and doesn’t take into account the additional expense of tracking unique aspects of the application. To complicate matters, there is usually quite a bit of confusion on the types of data being captured by the tools a company already has in place.

In this slideshow, Thomas Overton, from the Sumo Logic Developer Community, offers the five best methods for gaining insight from machine data.


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