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.
The Latest
The journey of maturing observability practices for users entails navigating peaks and valleys. Users have clearly witnessed the maturation of their monitoring capabilities, embraced DevOps practices, and adopted cloud and cloud-native technologies. Notwithstanding that, we witness the gradual increase of the Mean Time To Recovery (MTTR) for production issues year over year ...
Optimizing existing use of cloud is the top initiative — for the seventh year in a row, reported by 62% of respondents in the Flexera 2023 State of the Cloud Report ...
Gartner highlighted four trends impacting cloud, data center and edge infrastructure in 2023, as infrastructure and operations teams pivot to support new technologies and ways of working during a year of economic uncertainty ...
Developers need a tool that can be portable and vendor agnostic, given the advent of microservices. It may be clear an issue is occurring; what may not be clear is if it's part of a distributed system or the app itself. Enter OpenTelemetry, commonly referred to as OTel, an open-source framework that provides a standardized way of collecting and exporting telemetry data (logs, metrics, and traces) from cloud-native software ...
As SLOs grow in popularity their usage is becoming more mature. For example, 82% of respondents intend to increase their use of SLOs, and 96% have mapped SLOs directly to their business operations or already have a plan to, according to The State of Service Level Objectives 2023 from Nobl9 ...
Observability has matured beyond its early adopter position and is now foundational for modern enterprises to achieve full visibility into today's complex technology environments, according to The State of Observability 2023, a report released by Splunk in collaboration with Enterprise Strategy Group ...
Before network engineers even begin the automation process, they tend to start with preconceived notions that oftentimes, if acted upon, can hinder the process. To prevent that from happening, it's important to identify and dispel a few common misconceptions currently out there and how networking teams can overcome them. So, let's address the three most common network automation myths ...
Many IT organizations apply AI/ML and AIOps technology across domains, correlating insights from the various layers of IT infrastructure and operations. However, Enterprise Management Associates (EMA) has observed significant interest in applying these AI technologies narrowly to network management, according to a new research report, titled AI-Driven Networks: Leveling Up Network Management with AI/ML and AIOps ...
When it comes to system outages, AIOps solutions with the right foundation can help reduce the blame game so the right teams can spend valuable time restoring the impacted services rather than improving their MTTI score (mean time to innocence). In fact, much of today's innovation around ChatGPT-style algorithms can be used to significantly improve the triage process and user experience ...
Gartner identified the top 10 data and analytics (D&A) trends for 2023 that can guide D&A leaders to create new sources of value by anticipating change and transforming extreme uncertainty into new business opportunities ...
