You're struggling to explain complex data insights. How can you make it clear to non-technical stakeholders?

Here are five concise tips: 1. Use Visuals: Convert data into simple charts/graphs for clarity. Example: A line graph to show sales trends over time. 2. Tell a Story: Create a narrative that explains the data’s impact. Example: “Sales rose after our product launch,” with a chart showing the spike. 3. Provide Comparisons: Relate data to familiar concepts. Example: “700,000 users is like filling 10 football stadiums.” 4. Focus on Key Insights: Highlight critical points only. Example: A 20% customer retention increase. 5. Avoid Jargon: Use clear, simple language. Example: “Correct in 95 of 100 cases” instead of “95% accuracy.”

Tips to explain complex data: Here are some tips for explaining complex data to stakeholders: 1. Understand your stakeholders:their background, interests, and level of understanding of the data. 2. Highlight the key insights: Focus on the most important and relevant findings from the data. 3. Use a structured approach: Start with the big picture, then drill down into the details. Use a logical flow to guide your stakeholders 4. Leverage data visualization: Use charts, graphs, and other visuals to present the data in a clear way 5.. Quantify the impact: Explain how the insights can impact the organization's goals, strategies, and bottom line.

Some tips: 1. Know Your Audience: Start by understanding who you're talking to. Are they business executives, marketing teams, or customer service reps? Each group has different interests and levels of technical understanding. Tailor your explanation to their needs and avoid jargon they might not be familiar with. 2. Define the Goal: Clarify the purpose of the data. What question are you trying to answer? What problem are you trying to solve? 3. Use Analogies and Stories: Analogies are your best friends when explaining complex concepts. 4. Visualize the Data: A picture is worth a thousand words, especially with data. Make sure your visuals are simple, labeled clearly, and highlight the key points.

In my opinion the most effective tips for simplifying complex concepts are: - Use Analogies: Break down complex data concepts by comparing them to everyday scenarios or familiar concepts. For example, explain a dataset as a collection of recipes, where each recipe (row) has a list of ingredients (features). - Avoid Jargon: Replace technical terms with simple language. Instead of saying “regression analysis,” explain it as “a method to predict future trends based on past data.” - Focus on the Big Picture: Highlight the main insights and their implications rather than delving into the details of the analysis. Emphasize what the data tells you about the business or the specific problem at hand.

Tips to Explain Complex Data to a Non-Technical Audience: 1. Provide Context: Explain the background and relevance of the data insights. 2. Tell a Story: Craft a narrative that explains the data's journey and impact. 3. Use Visuals: Flowcharts, mind maps, and graphs provide a helicopter view. 4. Use Analogies: Relate with familiar concepts for easier understanding. 5. Simplify Language: Avoid jargon; use clear wording. 6. Highlight Key Points: Focus on the most important insights and implications. 7. Show Real-World Applications: Demonstrate how the data affects real business. 8. Engage with Questions: Encourage stakeholders to ask questions for clarity. 9. Summarize with Bullet Points: Present a concise summary of key takeaways.

Lembrem da piadinha do "Quer que desenhe?" Na minha experiência, os recursos visuais são ferramentas poderosas para comunicar insights de dados. Criar tabelas, gráficos ou infográficos que destaquem os pontos-chave é uma boa prática. Um gráfico bem projetado geralmente pode contar uma história de forma mais eficaz do que uma página cheia de números, ajudando que as partes interessadas possam entender os dados rapidamente. DICA: O objetivo é ajudar na compreensão, não sobrecarregar com a complexidade.

In my role as a consultant and throughout my career in retail, I've consistently leveraged visual aids to communicate data insights effectively. For instance, I used visually compelling charts and infographics to present sales performance and customer behavior trends. This method not only clarified complex data but also facilitated strategic discussions with stakeholders. By incorporating intuitive color coding and clear labels, I ensured that the visuals were easily understood, which in turn aided in swift and informed decision-making, ultimately driving profitability and enhancing customer satisfaction.

Charts & Diagrams can be used to to represent data you can also insert slicers after pivoting the tables that contains the data and demonstrate it on a bar graph or chart.

" Dibujar " claramente el objetivo con elementos que deben estar presentes ( adjudicarle valor de 100 pc ) y hacer contínuo planificado vs ejecutado / logrado .... Establecer línea de tiempo desde momento cero ... medir pasos de avance ..... sin perder de vista las opciones de corregir el plan...

Creating dashboards and making data tell the story with charts and graphs is a great way to see the big picture. This way the stakeholders can interact with the data and understand it better .

Les données sont des outils de pilotage au service de l'atteinte des objectifs et stratégies de l'organisation. Leur impact doit être la principale raison de leur mise en lumière. Elles doivent: 🟢anticiper les difficultés 🟡mettre en évidence les tendances émergentes et les opportunités d'innovation 🟢identifier les leviers de croissance etc. Les données doivent éclairer des réalités tangibles et compréhensibles par tous ceux et celles qu'elles impactent.

In my previous role, I've found that focusing on impact is key when explaining complex data insights to non-technical stakeholders. Rather than delving into intricate economic theories, I emphasize how our findings affect real-world outcomes. For instance, when presenting economic statistics to regional policymakers, I highlight how changes in these figures could influence local businesses, job markets, and investment opportunities. By framing our data analysis in terms of tangible effects on economic growth, policy decisions, and people's lives, I've been able to engage stakeholders more effectively and facilitate more meaningful discussions about economic strategies and their potential consequences

Na minha experiência, principalmente quanto temos limitações de tempo ou entrentamos atrasos, o primeiro passo deve ser avaliar o impacto de cada tarefa no projeto geral. Identificar as tarefas críticas que afetam diretamente a data de conclusão do projeto e priorizo essas tarefas. Também avalio as dependências entre as tarefas, pois algumas podem parecer não críticas, mas são pré-requisitos para outras. Compreendendo o efeito cascata de cada atraso, posso tomar decisões informadas sobre onde concentrar meus esforços para minimizar os impactos negativos no projeto.

Siempre será necesario evaluar si el entrenamiento llegó.. Una vez " vendido " el modelo , toca ponerle cifras y ejercitarlo con resultados...

I agree with explaining the results, the good and the bad regarding different routes taken. I would recommend explaining the price points of different options as well as the impact that certain choices can have in the future. Other than being polite and patient, I would say think of it like going to see a neurologist or a rheumatologist. They are not going to explain to you everything that took them 12 years to learn in a presentation or a single conversation, etc.

Painéis ou modelos interativos permitem que os usuários manipulem variáveis e vejam como as mudanças afetam os resultados. Essa abordagem prática pode desmistificar os dados e dar um senso de propriedade sobre os insights derivados deles, promovendo uma compreensão e apreciação mais profundas do valor dos dados.

Throughout my career in retail, I've found that implementing interactive tools significantly enhances stakeholder engagement with data. For instance, I introduced interactive dashboards that allowed team members to explore sales metrics and customer trends dynamically. This hands-on approach empowered them to adjust variables and visualize different scenarios, fostering a deeper understanding and appreciation for the data. By demystifying complex information, these tools not only facilitated more informed decision-making but also cultivated a sense of ownership and accountability among the team, ultimately driving better business outcomes.

A eficácia de uma mensagem está em sua capacidade de ser totalmente compreendida, ressoar com o público e gerar insights relevantes. Frequentemente, apresento dados técnicos complexos a fóruns executivos que, embora não possuam profundidade técnica, desempenham um papel crucial nas decisões. Nessas situações, sigo alguns princípios essenciais: - Nada é óbvio. - Exemplos práticos e metáforas facilitam a compreensão. - A soma dos detalhes forma o todo. - Apresentações concisas evitam mal-entendidos. E o mais importante: insights de pessoas com diferentes perspectivas são inestimáveis.

Acredito que bons insights só são válidos quando as pessoas entendem o seu raciocínio. Devemos estar preparados para repassar o mesmo ponto várias vezes ou responder a várias perguntas. O objetivo é educar, não sobrecarregar ou frustrar.

Le rôle de l'analyste ne se résume pas à la collecte et l'analyse de données. En incarnant votre posture de conseiller, vous devez faire preuve de pédagogie et patience en acceptant de revenir plusieurs fois sur le même point et d'apporter de la clarté à des publics non avertis. Vous devez vous placer dans leur perspective et leur montrer ce qui est possible et non le contraire.

When working with nontechnical stakeholders, I have seen how common it is for people to feel intimidated by data and new technology more broadly. It is quite common for people to have questions or concerns about new or complex topics. Ideal data analysts understand that everyone has a unique perspective and are always eager to look at data from different viewpoints. They genuinely enjoy discussing their work with others. Patience should be transmitted through respectful communication to ensure trust within a work relationship. Teamwork is improved when people feel comfortable to discuss things that they are unsure about; therefore, I believe that patience is a critical quality to have when working in this field.

In my experience in retail, particularly in leadership roles, practicing patience has been essential when conveying complex data insights. I often had to explain detailed sales analyses to team members with varying levels of expertise. I found that revisiting key points multiple times and encouraging questions fostered a more inclusive learning environment. By approaching each explanation with a fresh perspective and a commitment to clarity, I was able to educate rather than overwhelm. This patience not only improved team comprehension but also empowered them to make informed decisions, ultimately enhancing overall performance and customer satisfaction.

Les données doivent être sélectionnées et narrées en fonction de l'objectif et de l'audience visés. Si l'analyse est une, la façon de communiquer autour des données doit être multiple. Chaque partie prenante doit sentir que le message lui est exclusivement réservé, cela d'autant plus que vous attendez d'elle une réaction spécifique.

Cada parte interessada pode ter um histórico diferente e, portanto, exigir uma abordagem única para entender os dados. Ser flexível nas explicações e pronto para ajustar a estratégia dependendo de com quem está falando garante que cada parte interessada saia com uma compreensão clara dos insights compartilhados.

Infurtherance to using simple language and diagrams to capture salient point. It is important to conduct surveys for the audience both before and after the presentation. This helps gauge their level of understanding and assess the presenter's ability to communicate effectively. The feedback also helps determine if the communication style was suitable and if the message was easy to understand.

This point actually echoes the first one about the simplification of the complex concepts for the stakeholder. An expert in analytics might have this 'curse of knowledge' bias which results in their overestimation how easy it is to understand complex analytical topics for a non-tech people. Knowing the audience and getting down to their level of expertise is absolutely crucial for communicating ideas and knowledge sharing.

As a stakeholder or a mutual fund holder or a shareholder of any company, figures of dividend as well as profit and loss matter.The definition of non-technicality changes from division to division. A shampoo formula maker can never explain the chemical formula of a shampoo, and even if a celebrity uses the formula of the shampoo in the advertisement, it will definitely not work.