Dirty data isn't just an annoyance; it's a hidden tax on your entire business.

From dashboards no one trusts, to AI models trained on flawed inputs, to analysts spending hours fixing the same problems over and over again, poor data quality quietly drains time, money, and confidence.

In this episode of Mavens of Data, we're joined by Susan Walsh (data quality expert and The Classification Guru!) to unpack what "dirty data" actually looks like in the real world.

We talk through the most common types of dirty data, the downstream problems they cause across analytics, AI, and operations, and Susan's COAT framework for tackling data quality in a way that actually sticks.

This isn't about perfection or endless clean-up projects; it's about building smarter processes, preventing problems at the source, and saving yourself (and your team) countless hours down the line.

Whether you're an analyst, data engineer, analytics leader, or just someone tired of fixing the same broken fields every week, this conversation will change how you think about data quality.

• The most common (and most expensive) types of dirty data

• Why dirty data is a business process problem, not a tooling problem

• Susan's COAT framework and how to apply it in practice

• How small design choices (like dropdowns) can prevent massive downstream issues

• Real-world horror stories and how they could have been avoided

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In this episode of Mavens of Data, we sit down with Howard Friedman, author of "Winning with Data Science", educator, and practitioner, who has spent years helping organizations turn messy data problems into measurable business wins.

We explore the models and decision frameworks used to identify where data science can create the biggest lift inside an organization, from data infrastructure and modeling to reporting, training, and software validation.

If you've ever wondered what effective data science looks like in the real world, this episode is your playbook!

We'll break down three high-impact, real-world transformations:

1. Site Selection Modeling That Eliminated Costly Mistakes

   • How one company improved its revenue forecasts and stopped expensive cannibalization across its retail locations. Howard will walk through what went wrong, how the right product requirements were defined, and what factors were the turning point in improving the forecasts.

2. Loyalty Program Analytics That Drove Double-Digit Growth

   • 1M+ loyalty members, but the organization wasn't sure how to monetize them. Learn how cross-functional collaboration and analysis led to targeted offers, higher engagement, and the reactivation of 50,000+ dormant accounts.

3. LLM Automation in Healthcare - Cutting Documentation Time in Half

   • Howare will tell us about a machine-learning use case at one healthcare company reduced documentation time by 2.5X while maintaining 97% accuracy, freeing clinicians to focus on patients, not forms.

These case studies highlight how data science becomes a profit center, not just a technical function.

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Most organizations are drowning in dashboards, KPIs, and visualizations, but starving for insights.

In this episode of Mavens of Data, we'll chat with Brent Dykes, Founder of AnalyticsHero and author of Effective Data Storytelling, to break down one of the biggest misconceptions in analytics: a chart is not a story, and a data point is not an insight.

Whether you're a data scientist, business analyst, product manager, or analytics leader, this episode will reshape how you communicate with data. If you've ever wondered why your dashboards aren't driving decisions, this conversation will hit home.

• What actually qualifies as an insight

• How to distinguish reporting ("the what") from storytelling ("the why")

• Why answering "so what?" is the key to influence

• The role AI will play in generating insights and telling stories

🤝 Follow Brent on LinkedIn!

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