To bridge the gap with non-technical stakeholders when faced with data discrepancies ... ? Translate tech into business: Use a universal semantic layer for all data and AI assets. This creates a common language that allows everyone to “speak data” without having to know the technical intricacies. ? Tell a data story: visualizations are your friend! Show the discrepancies and their impact using dashboards and reports. Make it clear how these issues affect the bottom line. ? Focus on solutions, not blame: Keep the discussion focused on how the problem can be fixed and avoided in the future. Nobody wins with finger-pointing!

Simplify Communication Avoid technical jargon; use clear and concise language. Tailor your message to their level of understanding, focusing on the "why" behind the data discrepancies. Use Visual Aids Incorporate diagrams, flowcharts, and infographics to illustrate complex concepts. Visual tools can help stakeholders grasp the implications of data discrepancies more easily. Provide Real-World Examples Use analogies and relatable scenarios to explain technical issues. Share success stories or case studies that demonstrate how similar discrepancies were resolved in the past.

Effectively addressing data discrepancies with non-technical stakeholders requires a strategic approach. I translate complex findings into actionable insights using dashboards and reports to enhance understanding. In my experience, discrepancies often stem from how different systems structure and present data, rather than indicating errors. For example, sales data may exclude tax details, while financial reports provide a consolidated view that includes taxes, fees, and commissions. To bridge the gap, I analyze patterns across datasets and provide examples to illustrate these differences. By fostering transparency and aligning diverse perspectives, I turn data discrepancies into opportunities for clarity and improved decision-making.

Start by simplifying the issue avoid technical jargon and focus on the impact. Use visual aids like charts or examples to illustrate discrepancies clearly. Collaborate with stakeholders to understand their data expectations and align on key metrics. If needed, set up validation processes to ensure consistency moving forward. Most importantly, maintain open communication, providing clear explanations and actionable solutions to build trust and keep everyone on the same page.

When I come across data discrepancies, my approach is all about clarity and collaboration to ensure non-technical stakeholders understand the issue. ?? I simplify the data—Instead of overwhelming them with raw numbers, I break it down into key takeaways. ?? I make it relatable—Using real-world analogies from their domain helps them grasp the issue quickly. ?? I visualize the impact—Dashboards, charts, and trend analysis make the problem and solution clearer. ?? I collaborate for solutions—Working together, I align the data with business goals and ensure accuracy. By making data accessible and actionable, I help teams make informed, data-driven decisions.