Bigeye Launches Dashboard and Issues to Create a Complete Data Quality Workflow

Dashboard gives data team leaders a bird’s eye view of data health, making it simple to monitor data quality coverage, impact on SLAs, time to resolution, and other key analytics. Underpinning the insights that Dashboard provides are Issues. Issues make it easier than ever for data engineers and analysts to dig in and resolve data quality issues.

Dashboard: Dashboard provides a single view of the most important information about a company’s data quality. From surfacing the most pressing data quality issues to highlighting opportunities to improve monitoring coverage, Dashboard gives everyone – from data engineers to executives – a deeper understanding of the state of the health of the company’s data and how it’s improving over time. Dashboard enables data teams to:

• Track and report on data health: View the health of data in one place and gain insight into which issues are impacting business-critical data and SLAs.
• Monitor coverage: Gain an understanding of which data sources are being monitored and how much coverage each data source has.
• Measure ROI: See how many data quality issues Bigeye has detected, how many issues have been resolved, and how much time was spent resolving issues.
• See progress: Measure improvements in your data reliability and gauge changes in the stability of your data over time.

Issues: Fueling the analytics insights captured in Dashboard, Issues enables data teams to resolve data quality issues faster and more effectively by threading data quality alerts into a single timeline with valuable context on related issues, past remediation, and the current status of the issue. Issues can be accessed from anywhere in the Bigeye platform, including the catalog or when viewing an SLA, making it easy to document past fixes and speed up future resolution. Issues help data teams more effectively resolve data quality issues, including:

• Resolve issues faster: Acknowledge, resolve, and close Issues with a single click.
• Receive fewer notifications: Related alerts are aggregated into a single Issue, making it easier to access data quality issues.
• Improve alerts: Mark alerts as good or bad to improve Bigeye’s anomaly detection over time.
• Document with ease: Leave notes and context on closed Issues to help your team resolve similar issues faster the next time.

“With Bigeye, we have created a powerful data observability platform that enables data teams at companies like Clubhouse, Instacart, and Udacity to create more reliable data applications and support thousands of users with trusted data. We’re excited to build more intelligence into our data observability platform to give customers even more tools to understand the health of their data and resolve issues fast,” said Egor Gryaznov, CTO and cofounder, at Bigeye.

To learn more about how Dashboard and Issues work together to provide data teams with an end-to-end understanding of the health of their data, read the Bigeye blog.

Bigeye enables data teams to proactively resolve data quality and pipelines issues before the business is affected – enabling them to scale increasingly complex data platforms with the reliability the business demands. Customers are able to proactively address data quality and pipeline issues before the business is affected. Powering Bigeye’s data observability platform are the ability to automatically instrument their data with more than 60 pre-built data quality metrics, get continuous real-time monitoring, and advanced anomaly detection that dynamically adapts to changes in the business.

About Bigeye

Bigeye is a data observability platform that brings data engineers, analysts, scientists, and stakeholders together to build trust in data. Companies like Instacart, Clubhouse, and Udacity use Bigeye to automate monitoring and anomaly detection and create SLAs to ensure data quality and reliable data pipelines. With complete API access, a user-friendly interface, and automated yet flexible customization, data teams can monitor quality, proactively detect and resolve issues, and ensure that every user can rely on the data. Visit www.bigeye.com.

Source: Bigeye