When explaining data anomalies to non-technical team members or clients, I keep it simple and relatable. I start by describing what "normal" data looks like and then highlight what’s unusual in the current data. I use analogies—like unexpected "bumps in a road"—to help convey the issue. Then, I explain possible causes, like system errors or rare events, and outline the steps we’re taking to investigate and correct it. This keeps everyone informed without technical jargon.

Based on my experience, demystifying data for mixed audiences isn’t just about translation—it’s about connection. Here’s how I bridge technical gaps with clarity and impact - 1. Storytelling Impact: Frame anomalies as part of a story to make insights memorable. 📖 2. Scenarios of Impact: Highlight “what-if” scenarios to show the potential impact of anomalies. 💡 3. Root Cause Mapping: Use a visual journey to track back to the anomaly’s origin for clarity. 🔍

Simplify the Issue: Describe the anomaly as an unexpected pattern or irregularity. Use Analogies: Relate it to something familiar, like a misplaced puzzle piece. Highlight the Impact: Explain how it affects decisions or outcomes. Provide Visuals: Use graphs or charts to illustrate the anomaly clearly. Focus on Solutions: Emphasize what’s being done to investigate or fix it. Avoid Jargon: Use plain language to ensure understanding. Reassure: Make it clear the issue is under control and won’t derail goals.

Creo que todos mas o menos coincidimos en que esencialmente hay que buscar un lenguaje común, simplificando al máximo y llevando a través de ejemplos la conversación a territorios que sean afines a nuestros compañeros. No sólo es importante que se entiendan las anomalías, sino su impacto así como qué vamos a hacer para verificar la fuente de dichas anomalías.

Contextualização com Analogias Simples: Tento utilizar analogias como, por exemplo, comparar uma anomalia a uma peça faltando em um quebra-cabeça. Explico que, assim como no que Visualizações Focadas: Em vez de apresentar gráficos complexos, destaco as partes mais impactadas pela anomalia. Assim, chamo atenção para a mudança no padrão, seja através de uma linha divergente ou um marcador em gráficos. Evito dados supérfluos para focar na anomalia e facilitar a compreensão. Impacto para o Negócio: Em vez de detalhar o processo técnico que levou à identificação da anomalia, priorizo explicar as implicações que ela traz para a equipe e o negócio. Por exemplo, aponto como a anomalia afeta a precisão das previsões ou prejuízo.

We expected our sales numbers to increase by 10% this quarter, but there was an unexpected dip in one of the regions. This anomaly suggests that something unusual occurred—perhaps an error in reporting or an external factor that we didn’t account for.

Anomalies can be described as someone is reading response no 30 out of 110 and the person who is waiting at no 85 is confused as to whom the explanation is addressed. The odd-one-out situation during the analysis part.

When explaining data anomalies to non-technical team members or clients, I’d describe them as “unexpected data patterns” that deviate from usual trends. An analogy might help: imagine a production line consistently creating products of a standard size, but occasionally producing a few outliers due to machinery errors. Visual aids, like highlighting anomalies in a chart, make this even clearer. I’d discuss possible causes, such as data entry mistakes, temporary system glitches, or external factors affecting the data’s source. Finally, I’d outline our plan for resolving the current issue and implementing checks to prevent similar anomalies in the future. This way, they see both the problem and our proactive approach.

When we encounter data anomalies, it's like noticing something unusual in a familiar pattern—like suddenly getting twice the usual number of customers in a store. These anomalies are unexpected changes that don’t follow the usual trend. We investigate them to understand if they're due to an error, a system glitch, or a real shift in behavior, helping us ensure the data we rely on is accurate and meaningful.