Argyle Data vs Pepperdata comparison

Argyle Data has had the privilege of working with global leaders and visionaries on their strategies for revenue threat analytics, big data, and machine learning. What consistently comes up is that best-in-class carriers know the revenue threats that they have been attacked with in the past. What they don’t know is how to prepare for future attacks that will likely incorporate new types and methods of revenue threats.

What is critical to understand is that a) criminals are continually innovating; b) each subscriber will have many devices, many channels, and many potential attack points; and c) we need a better way to detect new fraud and protect customers and carriers in this new world – today in 2015, not in 2020.

This requires an effective strategy for the use of big data and machine learning in the areas of:

Fraud Threats

Analytics apps for identifying threats from various types of domestic fraud and roaming fraud

Profit Threats

Analytics apps for identifying threats from arbitrage, negative margin, high usage, and bill shock

SLA Threats

Analytics apps for identifying threats from network vulnerabilities and from roaming partners not meeting their SLA windows

Forensic Threats

Graph analysis application for analyzing 1st to 5th degrees of separation between data assets

As big data stacks increase in scope and complexity, most data-driven organizations understand that automation and observability are necessary for modern real-time big data performance management. Without automation and observability, engineers and developers cannot optimize or ensure application and infrastructure performance, or keep cost under control. Pepperdata helps some of the most successful companies in the world manage their big data performance in the cloud and in the data center. These customers choose and trust Pepperdata because of three key product differentiators: autonomous optimization, full-stack observability, and cost optimization.