Sumo Logic Adds Location Context to Machine Data Analytics


SumoLogic builds out its machine data visualization capabilities by teaming up with Mapbox for their location context.

Modern analytics applications running in-memory are now able to analyze data in real-time. But a component often missing is an ability to visualize that data within the context of a specific location. To address that issue Sumo Logic, a provider of a real-time analytics platform for analyzing machine data that is delivered as a cloud service, has teamed up with Mapbox, a provider of a dynamic, interactive maps that are created from vector tiles, to make it simpler to use location to add more context to an analytics application running in real-time.

The JavaScript library that Mapbox creates is accessed via an application programming interface (API) that Sum Logic intends will be employed across a range of industry use cases spanning cybersecurity to digital marketing, says Michael Marfise, senior director of product management at Sumo Logic.

“We’re trying to make it easier to drop mapping capabilities into a machine data analytics application using APIs,” says Marfise.

See also: Business use of machine data analytics growing faster than expected 

Armed with that capability it becomes simpler for business users to track a wide variety of key performance indicators (KPIs) that are informed using machine data that gets captured within the logs generated by the systems and applications that make up a digital business process, explains Marfise.

While IT operations and security teams have long relied on log analytics to make sense of machine data, there’s now a growing trend to extend those analytics out to digital business processes. Business executives are starting to realize there is now a direct correlation to IT events and end customer experience, which affects everything from customers satisfaction to the amount of revenue an organization may be generating from an online application. Visualization tools such as Mapbox make it simpler for end users to make sense of log data that would otherwise be too arcane for them to consider as a source of actionable intelligence.

Machine data being captured by logs provides a source of data and metadata that enables business users to analyze in what might be occurring in real time in a way that could then be used to surface anomalies in time to resolve an issue either before the customer even knows it happened or, potentially worse yet, might adversely impact a stream or revenue.

The machine data visualization challenge

Typically, most analytics applications rely on systems or record such as enterprise resource planning (ERP) and customer relationship management (CRM) applications as their primary source of data. But in the digital age, those applications are not as potentially current as the data streaming through an analytic application focused on machine data. The challenge has always been how to visualize that data in way end users and IT staffs can both appreciate using dashboards specifically designed to address their respective interests. The Mapbox libraries represent a significant step towards achieving that goal, says Marfise.

It may be a while longer before digital business process transformation drives greater appreciation for the value of machine data. But as more processes become digitized it’s now only a matter of time before the logs that keep track of machine data become a primary source for real-time analytics.

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