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IT OPS USE CASE :

Proactively Identify Issues and Performance Problems

Overview

COMPANY TYPE

img-alertCable and internet services provider

USE TYPE

img-it-opsIT Ops

KEY BENEFITS

  • Analyze data too complicated to set thresholds for
  • Reduce the noise of alerts
  • Rank alerts by severity of impact

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"Our level 2 support specialists hit the ground running when we get an alert from Prelert. If there are a broad mixture of metrics in the Prelert Anomaly Alert, then we immediately know the scope and the probable cause of what’s happening."

~VP of Technical Operations

The Company

Servicing customers in over 40 states, one of the largest cable and internet service providers (ISPs) in the United States holds customer satisfaction as a top priority, as millions of customers across the country rely on their entertainment and communication services.



The Problem

Wrangling Thousands of Alerts a Day to a Meaningful Few Was Impossible

This ISP's operations team is in charge of monitoring the status codes of millions of cable set-top boxes across the country, but the sheer volume of possible status codes (ranging from simple notifications to error codes), users, and geographical areas made it impossible to manually baseline the data. The team was tasked with putting thresholds on the data but quickly realized that it wasn’t possible.

Bombarded by tens of thousands of alerts each day, they were struggling to identify and prioritize the alerts that represented issues which could impact a larger number of users and impact customer satisfaction. For example, if video On Demand failed in one geography, it would represent a potential huge loss in income both from customers unable to purchase shows or movies, as well as from disgruntled customers overwhelming the customer support hotline.

The bottom line: The ISP could not physically threshold their data in any realistic manner or sort through the clutter of status code alerts to identify alerts that represented real service issues.



The Solution

Automatic Anomaly Detection Busts Through the Noise

Since 2013 this ISP's operations team has been using Prelert to better understand status codes and errors that have a wider impact on their user base, reducing the noise of these alerts to more accurately identify problems and improve customer satisfaction.

The ISP’s VP of Technical Operations was excited from their first trial of Prelert: “We knew that Prelert was on to something when we saw their ability to automatically plunk out the anomalies in our real-time data without any effort. But we were really excited by its ability to pull related anomalies out of multiple data types simultaneously.”



The Results

Prelert Reduces Noise and Identifies High-Impact Anomalies in Near Real-Time

Prelert helped this ISP's operations team significantly reduce the noise of their status code alerts and prioritize and rank problems by severity, impact, and rarity so the team knows where to focus their time.

Going a step further, Prelert even identified rare events and status codes that the support team didn’t know existed.

Prelert allowed them to see:

  • Fewer, more accurate, and more important alerts:
    • The VP of Technical Operations explains, “Our level 2 support specialists hit the ground running when we get an alert from Prelert. If there are a broad mixture of metrics in the Prelert Anomaly Alert, then we immediately know the scope and the probable cause of what’s happening.”
    • The team reduced time spent guessing at the problem and can now focus on the truly valuable and important tasks.
  • Correlated anomalies across multiple sources:
    • Prelert was able to quickly and easily flag unusual behavior across multiple dimensions of data, making it easier to identify performance issues clustered by geography.
  • Actionable troubleshooting data included in each alert:
    • Prelert reports other correlated anomalous events in each anomaly alert, allowing the team to quickly find the root cause of service problems without hours of troubleshooting.
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