Bigeye provides a data observability platform designed to help businesses maintain the quality and reliability of their data. The platform features automated monitoring and alerting tools that enable data teams to quickly identify and fix data issues, ensuring that the data used for decision-making is accurate and trustworthy. Unlike many competitors, Bigeye focuses specifically on data observability, offering a subscription-based model with tiered pricing that adjusts based on the amount of data being monitored. The primary goal of Bigeye is to minimize data downtime and enhance the trustworthiness of data, ultimately improving operational efficiency and decision-making for its clients.

Costanoa Ventures

Point72 Ventures

Sequoia

Coatue Management

Data & Analytics

Enterprise Software

Bigeye

SAN FRANCISCO--(BUSINESS WIRE)-- #datamonitoring--Bigeye announces $17 million in funding in a Sequoia-led round to help organizations build trust in their data.

Bigeye Raises $17 Million Series A to Build Trust in Data

Toro, a San Francisco, CA-based data quality monitoring solution provider, raised $4m in seed funding

Toro Raises $4M in Seed Financing

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More. Data is critical to every business, but when the volume of the information and the complexity of pipelines grow, things are bound to break!According to a new survey of 200 data professionals working in the U.S., instances of data downtime — periods when enterprise data remains missing, inaccurate, or inaccessible — have nearly doubled year over year, given the surge in the number of quality incidents and the firefighting time taken by teams.The poll, commissioned by data observability company Monte Carlo and conducted by Wakefield Research in March 2023, highlights a critical gap that needs to be addressed as organizations race to pull in as many data assets as they can to build downstream AI and analytics applications for business-critical functions and decision-making.“More data plus more complexity equals more opportunities for data to break. A higher proportion of data incidents are also being caught as data is becoming more integral to the revenue-generating operations of organizations. This means business users and data consumers are more likely to catch incidents that data teams miss,” Lior Gavish, co-founder and CTO of Monte Carlo, tells VentureBeat.

Data Downtime Almost Doubles As Professionals Struggle With Quality Issues, Survey Finds

SAN FRANCISCO, March 25, 2024 /PRNewswire/ -- Today Bigeye announces the availability of Bigeye Dependency Driven Monitoring. With this launch, Bigeye becomes the first data observability platform to allow enterprise data teams to connect their analytics dashboards, map every dependency across modern and legacy data sources, and deploy targeted data observability to ensure they stay reliable by default.According to Bigeye's 2023 State of Data Quality Report, 70% of business leaders don't trust analytics dashboards to make critical decisions due to regular data quality incidents."Bigeye's new capabilities will let us surface the health of critical Power BI dashboards in real-time." Post this Announcing Bigeye Dependency Driven Monitoring. Optimized data observability for every column powering critical analytics dashboards and data products.Regarding this challenge Kyle Kirwan, CEO and Co-founder of Bigeye, said, "We've spoken with hundreds of enterprise data leaders and, despite investing heavily in data quality tools and processes, they still struggle to deliver reliable data analytics to business users. Something the data observability industry hasn't yet solved is how to handle the complexity and size of large enterprise data pipelines. This is because enterprise dashboards have a long list of dependencies that span modern and legacy technologies and data observability platforms have yet to offer true support for the types of hybrid environments nearly all Fortune 500 companies have."Bigeye Dependency Driven Monitoring uniquely solves this challenge by combining enterprise-grade lineage technology with data observability to automatically trace the entire enterprise data pipeline at column-level precision through traditional and modern technologies, ETL stages, and even across the boundary from cloud to on-premises environments.Bigeye Dependency Driven Monitoring improves data observability ROIWithout a clear understanding of which columns are critical dependencies for business operations and which are not, data engineering teams are left with no choice but to deploy broad, blanket monitoring across a large number of tables and columns to reach adequate coverage. While this approach helps engineers identify anomalies anywhere they occur in the data environment, it also requires them to manage higher compute costs, unwanted alert noise, and additional overhead as a result of deploying unnecessary monitoring.Bigeye Dependency Driven Monitoring turns this paradigm on its head by allowing data analysts and business users to start from their critical dashboard and—in just a few clicks—enable data observability on each column that matters, and none that don't.When compared with other data observability approaches, Bigeye Dependency Driven Monitoring provides:Faster time to value and improved trust for data consumers.Clear visibility into the health of the entire analytics data pipeline for analysts.Reduced alert noise and faster issue resolution for data engineers.Lower total cost of ownership and less compute overhead for data leaders.When a data issue is detected, Bigeye will instantly notify each data source owner impacted through Slack or Microsoft Teams

Bigeye Launches Bigeye Dependency Driven Monitoring To Ensure Reliable Analytics

Bigeye, a NYC-based provider of a data observability platform, received a strategic investment from Alteryx Ventures

Bigeye Receives Strategic Investment From Alteryx Ventures

As companies create machine learning models, the operations team needs to ensure the data used for the model is of sufficient quality, a process that can be time consuming. Bigeye (formerly Toro), an early stage startup is helping by automating data quality.

Bigeye (formerly Toro) scores $17M Series A to automate data quality monitoring

Bigeye, a San Francisco, CA-based data observability company, acquired Data Advantage Group, a provider of MetaCenter, a metadata collection and data lineage platform.The amount of the deal was not disclosed.The integration of Data Advantage Group’s extensive data lineage capabilities allow Bigeye to automatically map data lineage across transactional databases, ETL platforms, data lakes, data warehouses, and business intelligence tools. This acquisition gives Bigeye customers a view of data pipelines available to date from a data observability provider. The Data Advantage Group team will be retained by Bigeye. New capabilities will gradually become available to Bigeye enterprise customers through regular product launches.Founded by Geoffrey Arone, Data Advantage Group is building a comprehensive metadata collection and data lineage platform, MetaCenter. The team leverages more than 20 years of domain expertise to manage metadata, change control, and data governance for enterprises across a range of sectors, from financial services to industrial to government and healthcare.Led by CEO Kyle Kirwan, Bigeye is a data observability platform that helps data engineering and science teams ensure data reliability. Organizations like Zoom, Confluent, Unity, and a U.S

Bigeye Acquires Data Advantage Group

Data observability platform for enterprises

Bigeye

Data observability platform for enterprises

Bigeye

Data observability platform for enterprises

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