Para transmitir essa importância, sugiro analogias simples. Por exemplo, podemos comparar os dados a ingredientes em uma receita: se um ingrediente estiver estragado, o resultado final será comprometido. Além disso, podemos demonstrar exemplos concretos de como a falta de integridade nos dados levou a decisões erradas no passado, como campanhas que não atingiram o público-alvo ou investimentos mal direcionados. Um guia visual que explique os principais indicadores de qualidade dos dados, como precisão, completude e consistência pode ajudar também. Isso ajudaria a equipe a entender melhor o que estamos analisando e por que é vital garantir a qualidade dos dados que utilizamos.

To bridge the gap between data integrity and non-technical team members: Relate: Use analogies like recipes (ingredients = data, final product = analysis). Visualize: Create simple diagrams or charts to illustrate concepts. Emphasize: Explain how data integrity issues can lead to incorrect decisions, wasted resources, and brand damage. Storytelling: Share real-world examples of data integrity failures and their consequences. Encourage questions: Be open to questions and provide clear, concise answers. Remember to keep your explanations simple, avoid technical jargon, and focus on the key points.

Use real examples to convey issues with data integrity. If it’s about translating the technical ‘language’, practice some techniques to convey the technicalities such as data storytelling. An easy one would be the before-and-after method. Give an example of : > What was happening before > What was done (testing/clean up/etc) > What happened after.

Let's face it, there'lll be times when the data from certain weeks or months doesn't look favorable. As analysts presenting findings to clients or executives, we often have to deliver this bad news. It's crucial to communicate ownership of the data genuinely and sincerely, which can be beneficial in many meetings. This isn't about the quality of the work produced, but rather about identifying additional elements needed to achieve the desired outcome, as web projects can sometimes take longer or require more resources. This doesn't mean we take responsibility for everything—user behavior can be unpredictable and beyond our control—nor does it mean we deflect the bad and claim the good. Strike a balance between ownership and goodwill efforts.