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Anomalo Partners with Google Cloud

Anomalo announced a partnership with Google Cloud to help organizations trust the data they use to make decisions and build products.

The combination provides customers with a way to monitor the quality of the data in any table in BigQuery’s platform without writing code, configuring rules or setting thresholds.

Today’s modern data-powered organizations are using BigQuery to perform real-time, predictive analytics on their centralized data and build and operationalize machine learning (ML) models at scale. However, dashboards and production models are only as good as the quality of the data that powers them. Many data-powered companies quickly encounter one unfortunate fact: much of their data is missing, stale, corrupt or prone to unexpected and unwelcome changes. As a result, companies spend more time dealing with issues in their data rather than unlocking that data’s value.

Anomalo addresses the data quality problem by monitoring enterprise data and automatically detecting and root-causing data issues, allowing teams to resolve any hiccups with their data before making decisions, running operations or powering models. Anomalo uses ML to automatically assess for a wide range of data quality issues, including deep data observability that learns when there’s an unexpected trend or correlation inside the data itself. If desired, enterprises can fine-tune Anomalo’s monitoring using no-code key metrics and validation rules or by defining any custom SQL check.

With Anomalo, organizations can now begin monitoring the quality of their data in less than five minutes. They simply connect Anomalo’s data quality platform to their BigQuery account and select the tables they wish to monitor. No further configuration or code is required.

“Organizations using data to make decisions or as an input into ML models need to ensure accuracy and quality. With Anomalo’s continuous monitoring, customers can ensure their data is always accurate, even as it evolves over time,” said Naveen Punjabi, Director, Analytics & Data Science Partnerships, Google Cloud.

“I have always been a fan of Google Cloud’s customer centric approach to building products. BigQuery has allowed customers to democratize access to data and connect more source systems than ever before to unlock new BI and ML use cases. But next-generation ML and analytics solutions are only as good as the data they’re built on. Enterprises need deep data observability tools like Anomalo that can help them detect and resolve complicated data issues, before issues affect BI dashboards and reports or downstream ML models,” said Elliot Shmukler, Co-founder and CEO of Anomalo.

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Anomalo Partners with Google Cloud

Anomalo announced a partnership with Google Cloud to help organizations trust the data they use to make decisions and build products.

The combination provides customers with a way to monitor the quality of the data in any table in BigQuery’s platform without writing code, configuring rules or setting thresholds.

Today’s modern data-powered organizations are using BigQuery to perform real-time, predictive analytics on their centralized data and build and operationalize machine learning (ML) models at scale. However, dashboards and production models are only as good as the quality of the data that powers them. Many data-powered companies quickly encounter one unfortunate fact: much of their data is missing, stale, corrupt or prone to unexpected and unwelcome changes. As a result, companies spend more time dealing with issues in their data rather than unlocking that data’s value.

Anomalo addresses the data quality problem by monitoring enterprise data and automatically detecting and root-causing data issues, allowing teams to resolve any hiccups with their data before making decisions, running operations or powering models. Anomalo uses ML to automatically assess for a wide range of data quality issues, including deep data observability that learns when there’s an unexpected trend or correlation inside the data itself. If desired, enterprises can fine-tune Anomalo’s monitoring using no-code key metrics and validation rules or by defining any custom SQL check.

With Anomalo, organizations can now begin monitoring the quality of their data in less than five minutes. They simply connect Anomalo’s data quality platform to their BigQuery account and select the tables they wish to monitor. No further configuration or code is required.

“Organizations using data to make decisions or as an input into ML models need to ensure accuracy and quality. With Anomalo’s continuous monitoring, customers can ensure their data is always accurate, even as it evolves over time,” said Naveen Punjabi, Director, Analytics & Data Science Partnerships, Google Cloud.

“I have always been a fan of Google Cloud’s customer centric approach to building products. BigQuery has allowed customers to democratize access to data and connect more source systems than ever before to unlock new BI and ML use cases. But next-generation ML and analytics solutions are only as good as the data they’re built on. Enterprises need deep data observability tools like Anomalo that can help them detect and resolve complicated data issues, before issues affect BI dashboards and reports or downstream ML models,” said Elliot Shmukler, Co-founder and CEO of Anomalo.

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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 ...