Guide

House of Data Series

A strong data program is built like a house. Seven domain pillars rise from the Data Architecture support, each one essential to a complete data program: Data Quality, Privacy, Data Security, DataOps, Compliance, Data Enablement, and Data Consumption. With Data Leadership and Literacy protecting and enabling the program itself.

Guide

Gartner® Market Guide for Data Observability Tools

Guide

Gartner® Market Guide for Guardian Agents

Guide

EU AI Act: Evaluate GPAI Vendor Readiness

A comprehensive vendor due diligence questionnaire for GPAI compliance.

Research

BARC Research Study: Successful Delivery of AI/GenAI

Whitepaper

AI Trust at Scale: Your Guide to Managing Agentic AI in the Enterprise

Guide

Build vs Buy: Choosing a Data Observability Platform

Make a confident decision with this essential guide to building vs. buying observability solutions.

Guide

Ultimate Guide To Data Observability

This is your one-stop shop for all things data observability. Learn what data observability is, who it’s for and who uses it, how it benefits organizations, how it works, key terms, use cases, and best practices.

Guide

Data Observability RFP

Data observability vendors vary widely in their capabilities. Use this template to accelerate your evaluation process and improve your chances of partnering with the right vendor for your unique needs.

Guide

Team Deployment Guide for Data Observability

In this guide, we have taken our collective learnings and created a path to building observability according to the unique structure of your data team.

Guide

Data Quality Survey Template

Prior to making an investment in data observability products or data quality initiatives, companies often want to conduct a data quality survey. Below, we present a template for such a survey that can be filled out for your own use.

Whitepaper

Autothresholds: Intelligent Anomaly Detection

A detailed overview of how Bigeye Autothresholds alert data teams to the issues that matter most.

Guide

How to Evaluate a Data Observability Platform

This guide is the end result of thousands of hours and dozens of experts and customers, distilled into the best way to run a thorough data observability evaluation. Check it out!

Guide

Trustworthy Data in Snowflake: A Field Guide

In this guide, we have taken our collective learnings and created a path to building trust in your Snowflake data.

Research

Bigeye's Growth and Vision in Data Observability

From 451: 'Data observability' specialist Bigeye is treating data quality more like a continuous engineering and monitoring challenge rather than an ad hoc remediation challenge.

Case study

Scaling enterprise data management with Bigeye

Learn how Bigeye is helping Freedom Mortgage achieve their mission of helping veterans and low-income borrowers buy homes.

Case study

How NOV built data confidence across eight data warehouses

Case study

Delivering trusted data products to the enterprise

Learn how Bigeye is helping Zoom achieve their vision of delivering trusted data products to business decision makers.

Case study

Driving marketing performance with reliable testing data

Learn how JustAnswer uses Bigeye to ensure optimal marketing results and drive customer acquisition and revenue performance for their business.

Case study

Bringing Reliability to CI/CD Data Pipelines

Mayan uses Bigeye to validate data pipeline merges and deliver self-serve analytics dashboards at scale.

Case study

Reliability at scale for highly-sensitive industries

About Crux: Solving tough data problems at scale

Case study

A Strong Data Culture Gets Stronger with Trusted Data

With Bigeye, Udacity has one place to understand data quality and has reduced detection times from 3+ days to under 24 hours.

Case study

Embracing risk without sacrificing reliability

Impira uses Bigeye as the safety net for its dynamic go-to-market strategy — ensuring that the team can move fast with reliable data.

Case study

Managing big data with a nimble team

Learn how the SimpleRose team uses Bigeye to deliver reliable data for large-scale data operations.