Thought leadership
-
July 18, 2025
AI Readiness Audit: The 5 Questions Every Data Leader Should Be Asking | Video Webinar
.png)
Get Data Insights Delivered
Join hundreds of data professionals who subscribe to the Data Leaders Digest for actionable insights and expert advice.
Stay Informed
Sign up for the Data Leaders Digest and get the latest trends, insights, and strategies in data management delivered straight to your inbox.
Get the Best of Data Leadership
Subscribe to the Data Leaders Digest for exclusive content on data reliability, observability, and leadership from top industry experts.
Get the Best of Data Leadership
Subscribe to the Data Leaders Digest for exclusive content on data reliability, observability, and leadership from top industry experts.
Stay Informed
Sign up for the Data Leaders Digest and get the latest trends, insights, and strategies in data management delivered straight to your inbox.
Get Data Insights Delivered
Join hundreds of data professionals who subscribe to the Data Leaders Digest for actionable insights and expert advice.
In this webinar, Kyle Kirwan (Bigeye) and Robert Long (Apptad) cut through the AI hype to share a five-question readiness audit built from real conversations with CIOs and data leaders. From lineage and data sensitivity to infrastructure and measurement, this session offers a practical framework to help enterprises evaluate their AI foundations. Watch the full replay to explore each question, and what it reveals about your org’s ability to scale AI responsibly.
share this episode
Resource
Monthly cost ($)
Number of resources
Time (months)
Total cost ($)
Software/Data engineer
$15,000
3
12
$540,000
Data analyst
$12,000
2
6
$144,000
Business analyst
$10,000
1
3
$30,000
Data/product manager
$20,000
2
6
$240,000
Total cost
$954,000
Role
Goals
Common needs
Data engineers
Overall data flow. Data is fresh and operating at full volume. Jobs are always running, so data outages don't impact downstream systems.
Freshness + volume
Monitoring
Schema change detection
Lineage monitoring
Monitoring
Schema change detection
Lineage monitoring
Data scientists
Specific datasets in great detail. Looking for outliers, duplication, and other—sometimes subtle—issues that could affect their analysis or machine learning models.
Freshness monitoringCompleteness monitoringDuplicate detectionOutlier detectionDistribution shift detectionDimensional slicing and dicing
Analytics engineers
Rapidly testing the changes they’re making within the data model. Move fast and not break things—without spending hours writing tons of pipeline tests.
Lineage monitoringETL blue/green testing
Business intelligence analysts
The business impact of data. Understand where they should spend their time digging in, and when they have a red herring caused by a data pipeline problem.
Integration with analytics toolsAnomaly detectionCustom business metricsDimensional slicing and dicing
Other stakeholders
Data reliability. Customers and stakeholders don’t want data issues to bog them down, delay deadlines, or provide inaccurate information.
Integration with analytics toolsReporting and insights
Get the Best of Data Leadership
Subscribe to the Data Leaders Digest for exclusive content on data reliability, observability, and leadership from top industry experts.
Stay Informed
Sign up for the Data Leaders Digest and get the latest trends, insights, and strategies in data management delivered straight to your inbox.
Get Data Insights Delivered
Join hundreds of data professionals who subscribe to the Data Leaders Digest for actionable insights and expert advice.
