Poor data quality slows processes, creates rework, erodes trust and costs time and money. Many dashboards become diagnostic tools with little impact. In this session I’ll take you inside the Power BI dashboard we built at Daikin Europe that drives improvement. You’ll see how we highlighted what matters, enabled prioritization, and added context, ownership and accountability. You'll learn design decisions, scoring logic and UX patterns that made teams act.

Target Audience: Data professionals who want to understand how to build data quality dashboards and make them practical, scalable and impactful in real business environments.
Prerequisites: Familiarity with data quality challenges and KPI-style reporting. Basic Power BI knowledge is helpful, but the session focuses more on design and adoption than technical build details.
Level: Basic

Extended Abstract:
Poor data quality doesn’t just frustrate data teams – it slows processes, creates rework, erodes trust and ultimately costs the business time and money. Many organizations try to solve this with dashboards, yet they often end up as diagnostic tools with little impact on day-to-day work. 

In this session I’ll take you inside the data quality dashboard we built in Power BI at Daikin Europe that actually drives improvement. We’ll break down how we highlighted what truly matters, made prioritization effortless, and supported cleanup with clear context, ownership and accountability. 

You’ll see the design decisions, scoring logic and user experience patterns that encouraged teams to take action, not just observe metrics. I’ll also share the lessons we learned the hard way – working around data model limitations, aligning stakeholders and demonstrating business value.

By the end you’ll understand how a well-designed dashboard can accelerate data quality improvement across the organization and become a true driver of change, not just another report.