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SOASTA Announces Fall 2016 Release of Digital Performance Management Platform

SOASTA announced its Fall 2016 Release, extending its Digital Performance Management (DPM) platform with the introduction of deep data science integration across the product portfolio; correlation with performance metrics and key marketing analytics tools; expansion of predictive analytics into new verticals; and enhanced Single-Page Applications (SPA) support for performance monitoring and load testing.

The Fall release also illustrates SOASTA’s commitment to “BizOps,” breaking down silos that exist between technical and business teams and allowing them to frictionlessly pursue a common goal – delivering an excellent customer experience and incremental revenue.

Fall Product Release Highlights

- Deeper data science integration and expanded capabilities designed for analyzing desktop and mobile traffic. With this release, mPulse also becomes formally powered by SOASTA’s data science engine, which allows for RUM-based testing and integration merged with SOASTA mPulse dashboards, real-time campaign performance, and real-time visibility and correlation with key marketing analytics tools – Adobe Analytics, IBM Coremetrics and Google Analytics.

- Already a key part of mPulse predictive analytics, the “what-if” dashboard now offers expanded functionality that enables users to select any conversion or revenue metric, such as session length or duration, bounce rate, and non-revenue-related conversions. This takes predictive analytics to a new level for verticals outside of e-commerce/retail. Most importantly, IT operations can now accurately forecast the value of their web and mobile performance improvements.

- With SOASTA’s new SPA support for performance testing, customers can easily create performance load test for SPA sites using the Chrome browser recorder extension. It records SPA apps, catching the “page” changes that are no longer full HTML downloads but instead are smaller region changes that deliver a faster end user experience.

- Measuring SPA is also improved with JavaScript error tracking, alerting and analytics in mPulse and provides, at a glance, critical error information, such as error rate, session experiencing errors, errors broken down by build, and recent error stack. “I’ve run into issues with other software and service providers not being able to fully support the Meteor framework. SOASTA was ahead of the curve in already supporting SPA-based applications. The implementation was a piece of cake,” said Dustin Behr, IT Development Supervisor at HOM Furniture.

- SOASTA can isolate first-party resources to determine the impact from third-party resources to remediate problems and establish SLAs for third- party vendors.

The SOASTA Fall 2016 Release is now available to all SOASTA customers in a limited release, and general availability will be in late October.

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SOASTA Announces Fall 2016 Release of Digital Performance Management Platform

SOASTA announced its Fall 2016 Release, extending its Digital Performance Management (DPM) platform with the introduction of deep data science integration across the product portfolio; correlation with performance metrics and key marketing analytics tools; expansion of predictive analytics into new verticals; and enhanced Single-Page Applications (SPA) support for performance monitoring and load testing.

The Fall release also illustrates SOASTA’s commitment to “BizOps,” breaking down silos that exist between technical and business teams and allowing them to frictionlessly pursue a common goal – delivering an excellent customer experience and incremental revenue.

Fall Product Release Highlights

- Deeper data science integration and expanded capabilities designed for analyzing desktop and mobile traffic. With this release, mPulse also becomes formally powered by SOASTA’s data science engine, which allows for RUM-based testing and integration merged with SOASTA mPulse dashboards, real-time campaign performance, and real-time visibility and correlation with key marketing analytics tools – Adobe Analytics, IBM Coremetrics and Google Analytics.

- Already a key part of mPulse predictive analytics, the “what-if” dashboard now offers expanded functionality that enables users to select any conversion or revenue metric, such as session length or duration, bounce rate, and non-revenue-related conversions. This takes predictive analytics to a new level for verticals outside of e-commerce/retail. Most importantly, IT operations can now accurately forecast the value of their web and mobile performance improvements.

- With SOASTA’s new SPA support for performance testing, customers can easily create performance load test for SPA sites using the Chrome browser recorder extension. It records SPA apps, catching the “page” changes that are no longer full HTML downloads but instead are smaller region changes that deliver a faster end user experience.

- Measuring SPA is also improved with JavaScript error tracking, alerting and analytics in mPulse and provides, at a glance, critical error information, such as error rate, session experiencing errors, errors broken down by build, and recent error stack. “I’ve run into issues with other software and service providers not being able to fully support the Meteor framework. SOASTA was ahead of the curve in already supporting SPA-based applications. The implementation was a piece of cake,” said Dustin Behr, IT Development Supervisor at HOM Furniture.

- SOASTA can isolate first-party resources to determine the impact from third-party resources to remediate problems and establish SLAs for third- party vendors.

The SOASTA Fall 2016 Release is now available to all SOASTA customers in a limited release, and general availability will be in late October.

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Outages aren't new. What's new is how quickly they spread across systems, vendors, regions and customer workflows. The moment that performance degrades, expectations escalate fast. In today's always-on environment, an outage isn't just a technical event. It's a trust event ...

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...