Mastering the Data Quality Playbook for Data Professionals
Shailvi Wakhlu
Data Quality Leader
Stop firefighting bad data: prevent, diagnose, and fix it systematically
Bad data is everywhere—and if you work with data, you’ve felt the pain.
📉 Broken dashboards
🔁 Conflicting metrics
🧯 Late-night pipeline debugging
🗣️ Re-explaining numbers you know are wrong
Over time, this erodes trust in your data—and in your work.
Most data professionals are stuck in reactive mode. You fix issues after they’ve already caused damage, only to see the same problems resurface weeks later. That’s not a skill issue—it’s a systems issue.
This course helps you change that.
You’ll learn how to approach data quality systematically, not ad hoc:
🔍 Identify where and why data quality issues originate
🧪 Diagnose problems quickly using structured SQL-based techniques
🛡️ Put guardrails in place so errors are caught before they impact decisions
Just as importantly, you’ll learn how to make data quality visible and valuable. You’ll practice communicating the business impact of bad data so this work gets prioritized—not ignored.
The result?
✅ Less firefighting
📊 More trust in your data
🚀 Greater confidence in the insights you deliver
If you’re ready to stop chasing bad data and start preventing it, this course gives you the playbook.
What you’ll learn
Become the go-to data professional who prevents bad data, not just fixes it
Framework to identify root causes across the data lifecycle
Techniques to design proactive guardrails at data creation points
Case studies of prevention vs. costly downstream fixes
Structured SQL-based methods to trace anomalies and inconsistencies
Debugging playbooks for pipelines, transformations, and reports
Hands-on demos of real-world data quality investigations
Data quality dimensions and risk assessment frameworks
Tools to evaluate ingestion, transformation, and consumption stages
Exercises to pinpoint weak links in existing data systems
Patterns for validation checks and anomaly detection
Best practices for alerts, thresholds, and monitoring
Examples of scalable automation that reduce manual effort
Methods to prioritize high-impact data quality issues
Habits for ongoing visibility and accountability
Metrics to track improvements in data reliability
Frameworks to quantify business impact of poor data quality
Language to align stakeholders on ownership and accountability
Case studies of successful data quality advocacy
Learn directly from Shailvi
Shailvi Wakhlu
Data leader at Meta with two decades improving data quality
Who this course is for
Data Analysts & Data Scientists who want to prevent bad data instead of constantly fixing errors and dealing with misleading insights.
Data Engineers who want to build reliable pipelines with automated quality checks to ensure a trustworthy, high-integrity data environment.
Data Practitioners who need to secure company-wide buy-in for data governance initiatives that drive better business decisions.
What's included
Live sessions
Learn directly from Shailvi Wakhlu in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
Course syllabus
2 live sessions • 21 lessons • 2 projects
Week 1
Feb
21
Mastering the Data Quality Playbook: Preventing Data Quality Issues [Modules 1-3]
🤔 What is Bad Data and Why It Matters
🔁 Fundamentals of the Data Lifecycle
🚧 Preventing Data Quality Errors
Project 1: Assessing Data Health 📊
Week 2
Feb
28
Mastering the Data Quality Playbook: Diagnosing & Fixing Data Quality Issues [Modules 4-7]
🔍 Diagnosing & Debugging Bad Data
🛠️ Fixing Bad Data Issues
📊 Coding & Visualization Best Practices for Data Quality
✅ Getting Buy-In for Data Quality
Project 2: Diagnosing, Fixing & Pitching Data Quality 🛠️
Free resource
Debiasing Data: Types, Impact & Practical Fixes
Recognize Key Types of Data Bias
Learn the most common biases that distort data and decision-making.
Understand the Business Impact
See how biased data affects business outcomes and why fixing it matters.
Apply Practical Debiasing Strategies
Get concrete techniques to reduce bias throughout the data lifecycle.
Schedule
Live sessions
3-4 hrs / week
Sat, Feb 21
5:00 PM—8:30 PM (UTC)
Sat, Feb 28
5:00 PM—8:30 PM (UTC)
Projects
1-2 hrs / week
Async content
1 hr / week
Testimonials
- Bad data is like a virus—spreading errors, misleading model quality, causing bad decisions, and slowing down your business. The earlier you catch and fix it (time wise and figuratively along your data pipelines) the easier it is to unlock real value. I highly recommend my friend Shailvi Wakhlu’s course on data quality to anyone working with data!
Riccardo Baron
Vice President of Artificial Intelligence @ Codoxo - Wow, to all those in my network who care about DQ/DO, here's a course just on this topic!
Emmanuel Paraskakis
CEO @ Level 250
Frequently asked questions
$695
USD