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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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Being able to access the full potential of artificial intelligence (AI) and advanced analytics has become a critical differentiator for businesses. These technologies allow for more informed decision-making, boost operational efficiency, enhance security, and reveal valuable insights hidden within massive data sets. Yet, for organizations to truly harness AI's capabilities, they must first tap into an often-overlooked asset: their mainframe data ...

The global IT skills shortage will persist, and perhaps worsen, over the next few years, carrying a collective price tag of more than $5 trillion. Organizations must search for ways to streamline their IT service management (ITSM) workflows in addition to, or even apart from, hiring more staff. Those who don't find alternative methods of ITSM efficiency will be left behind by their competitors ...

Embedding greater levels of deep learning into enterprise systems demands these deep-learning solutions to be "explainable," conveying to business users why it predicted what it predicted. This "explainability" needs to be communicated in an easy-to-understand and transparent manner to gain the comfort and confidence of users, building trust in the teams using these solutions and driving the adoption of a more responsible approach to development ...

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