Follow Datanami:
February 7, 2017

AI Comes to Operational Analytics


Artificial intelligence is making inroads as a way of helping developers and IT managers oversee the delivery of distributed applications.

Loom Systems said its AI-based operational analytics platform adds an “intelligence layer” to data collection for real-time detection and resolution of problems associated with application delivery. The San Francisco-based company said this week its AI-based platform analyzes logs and semi-structured machine data to provide IT managers with greater visibility into business operations.

The real-time capability also is intended to reduce the cost and complexity of working with emerging operational analytics tools. Those tools are designed to provide DevOps team a measure of automation in deploying and managing enterprise applications.

The AI platform is also promoted as generating operational insights from raw data without the need to reconfigure IT stacks. That feature also covers “homegrown applications,” the company said.

Loom Systems and other operational analytics vendors are betting that emerging AI-based automations tools will gain traction among harried development teams struggling to keep pace with enterprise demand for new business applications. Loom said its automation tool is designed to replace manual techniques used for standard approaches such as log analysis. The company argues that standard approaches fail to squeeze as much value out of company data as would an operational analytics approach.

Hence, the Loom Systems platform automatically collects and presents application log data, enabling a real-time analytics capability along with the aforementioned intelligence layer to link log data with infrastructure fixes.

According to the company, its AI approach leverages “complex modules” to monitor signals generated by IT infrastructure. The system is designed to detect if a signal has shifted, along with signal shift characteristics. “Signals are then automatically tracked in ways that complement their expected behavior,” the company said.

Gabby Menachem, founder and CEO Loom Systems, added in a statement that its operational analytics platform seeks to help developers cope with rapidly evolving IT environments along with operational complexity. “We build cognitive intelligence and expertise into a new set of tools that analyze logs, metrics and machine-generated data—just like DevOps application managers and IT professionals do every day,” Menachem asserted.

Along with faster root-cause analysis of IT glitches, the AI-based platform also is designed to detect potential problems lurking in operational data, and then correlating the problems is discovers between enterprise applications and services.

Emerging AI-based tools are helping operational analytics gain traction as more companies recalibrate their digital efforts to back-office processes, according to a industry study released last year. Cognitive computing is expected to help enterprises make sense of greater volumes of structured and unstructured data, concluded a study by Paris-based business consultant Capgemini (EPA: CAP). Meanwhile, the consultant predicted machine learning and artificial intelligence would aid decision making and “operational optimization.”

Recent items:

Survey: Operational Analytics Gaining Traction

Rethinking Operational Analytics on Relational DBs