Your team is struggling to interpret your visualized data. How can you effectively handle their feedback? (original) (raw)

Last updated on Oct 19, 2024

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Data visualization is a powerful tool that can transform complex data sets into clear, insightful pictures. However, when your team is having trouble interpreting the visualized data, it's crucial to address their feedback constructively. Understanding their perspective can lead to more effective visualizations that facilitate better decision-making and foster a data-driven culture within your organization.

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• When your team is having trouble with data visualization, just listen to what they're saying. Ask them to explain exactly what’s confusing them. Maybe the legends aren’t clear, the graphs are too cluttered, or the colors make it hard to tell different data points apart. Figuring out these details helps you fix the problems and make your visualizations much clearer and more useful.

• If the team struggles to interpret visualized data, it may signal the dashboard creation process needs improvement. Active listening might not have been fully applied from the start, which is crucial to understanding the team’s true needs. Often, these needs are unclear even to stakeholders initially. An iterative process, with active listening throughout development, helps refine both the dashboard and the understanding of real needs. This cycle ensures the final product meets the team’s expectations. Remember, dashboards are a means to an end. The ultimate goal is to address the team’s data needs, delivering actionable insights that support decision-making.

• One thing I have found helpful (still learning) is : Understand the specific points of confusion or concern without interrupting. Clarify what aspects they find difficult to interpret. Then, try to adjust the visualization to enhance clarity. Use simpler charts, add more descriptive labels, or reduce clutter. Ensure the visual elements align with your team’s preferences and expertise level. Effective communication and openness to changes can ensure that the visualizations are useful for everyone.

• To handle feedback on data visualizations effectively, start by carefully understanding your audience's concerns. Clarify complex information so that it's easy to follow and highlights key insights. While sleek visuals and fancy tools can be impressive, they can also be distracting if the message isn’t clear. Help the team grasp the data behind the visualizations, so they understand the full context. Make smart adjustments to ensure the visual tells a compelling story. Work closely with users to ensure the data fits their needs. Finally, keep refining and improving based on feedback. Though visuals are powerful, combining them with clear explanations makes the message much more effective. Thanks. Raj

• If you want to handle the feedback, listen carefully, understand their perspective, and be open to constructive criticism. Communicate with your team clearly about changes and encourage additional suggestions. Use a variety of visual elements, such as charts, graphs, tables, and infographics, to make data more engaging. Another way is to get feedback from various sources, including your team, colleagues, and experts. Use a data visualization tool that allows for easy changes and continuous monitoring.

• Complex data doesn’t mean your visuals have to be complicated. If your team is feeling overwhelmed, it might be time to simplify things. Instead of using complex charts, break them down into simpler ones, use fewer data points, or split the data into multiple visuals to make it clearer. Focus on what’s most important and use things like color and size to draw attention to the key insights.

• Simplifying complex data is crucial for creating clear, effective visuals. When your team feels overwhelmed, focus on breaking down complicated charts into simpler ones, and limit the number of data points presented. It can help to divide the data into multiple visualizations for better clarity. Prioritize the most important insights and use visual elements, such as color and size, to direct attention to key information. By reducing clutter and focusing on the essentials, you can make your visuals more digestible and impactful for the audience.

• Ao ler o livro storytelling com dados, conseguimos entender que a simplificação e a simplicidade trazem melhores resultados do que a complicação.

• Utilize my experience with 50+ business metrics to identify core data stories Apply my ETL skills to preprocess and simplify complex datasets Use my Python and SQL expertise to create more focused data extracts Leverage my experience with AWS Textract to simplify document data visualization Apply statistical modeling skills to choose appropriate chart types Implement interactive elements using my Tableau and Power BI knowledge Use my experience with 2M+ consumers to prioritize key insights in visuals Apply RFM analysis techniques to segment and simplify customer data visuals Utilize my optimization skills to streamline complex dashboards Leverage my experience with Agile to iterate quickly on simplified versions

• Sometimes, complex data leads to overwhelming visuals. To fix this, I break down complicated charts into simpler parts. I focus on the most important information and remove unnecessary details, making it easier for my team to focus on key insights.

• Sometimes, struggling with visualized data comes from not knowing enough about how those visuals are made. It's worth spending some time teaching your team about the different types of charts and graphs and when each one works best. Make sure they understand things like scale, axes, and why picking the right type of visual is crucial for telling the data story. With a solid grasp of these basics, your team will get better at reading and understanding the visuals you put together.

• When I get feedback on my visualized data, I make sure to really listen to my team’s concerns. I take a moment to clarify what the visuals are supposed to communicate, making sure we’re all on the same page. Then, I tweak the visuals based on their suggestions, simplifying things or adding context where needed. I check in later to see if the changes helped.

• Utilize my experience teaching 100+ COMD students to explain concepts clearly Showcase real-world examples from my work at LEAD School and LikeMinds Explain the importance of data integrity in visualizations, drawing from my audit experience Conduct hands-on workshops using tools I'm proficient in (Python, R, MATLAB) Share my knowledge of cloud platforms (Azure, AWS) for scalable visualizations Explain how I've used visualizations to save C-suite executives 60+ hours monthly Demonstrate how I've applied visualizations to optimize engagement for 2M+ consumers

• Numa corporação com vários times que cuidam de produtos e são especialistas nos mesmos, às vezes é complicado fazer essas explicações, pois os mesmos querem os resultados, mas não querem as explicações por trás das visualizações, acredito que um profissional de dados, tem que fazer a leitura da sua equipe e entender que as vezes a melhor visualização pra ele, não vai ser a melhor para a pessoa que solicitou o dado, então entender o perfil do solicitante e extremamente necessário.

• Es verdad, hay muchas personas que no están familiarizadas con hechos tan simples como los rótulos de una gráfica, sin embargo no es conveniente que por este simple detalle estás personas se pierdan de información importante por solo este hecho lo mejor es fortalecer el equipo enseñando las técnicas de visualización utilizadas

• Categorize feedback using my experience with 50+ KPIs and business metrics Apply my statistical modeling skills to ensure accuracy in revised visualizations Use my experience with AWS API responses to enhance data integration in visuals Leverage my ETL expertise to refine data pipelines feeding into visualizations Use version control tools I'm familiar with, like Git, for tracking visualization changes Draw on my experience optimizing 50+ dashboards to make impactful revisions Use my data integrity skills to ensure revisions maintain data accuracy Apply my UX optimization experience to enhance visual clarity and user engagement

• Etapa importante do processo, remodelagem dos dados. Conseguimos identificar melhorias nos dados e trazer mais insigths de visualizações.

• After getting feedback, I don’t throw out everything, but I do make smart revisions. Maybe it’s adjusting the axis for better detail or tweaking the colors for more contrast. Small, thoughtful changes can make a big difference in how easily the data can be understood.

• Claro es mejor una buena combinación de colores para captar mejor el interés de quién va a leer el dashboard y también proyectar y entender mejor el dashboard

• This is where we need feedback from our team members who know the audience better. This way, we can make sure that the changes we make to the deliverable can show the most up To date information and can aid us telling a story to the audience

• Collaboration is the heart of meaningful data visualization. When you work hand in hand with your team, truly listening to their needs and understanding their vision, something powerful happens. Together, you create visualizations that go beyond just accuracy—they become intuitive tools that resonate with everyone who uses them. The input from your team is like a guiding light, sparking ideas that make the design not only more effective but also more impactful. In the end, it's this shared journey that turns data into a story everyone can understand and embrace.

• When it comes to data visualization, working with your team is everything. You gotta talk to the people who will actually use the data so you know what they really need. That way, you don’t just make something that looks good, but something that actually makes sense to them. Their feedback is gold. It helps you make sure the visualizations hit the mark and do what they’re supposed to do.

• "A comunicação é a chave para o sucesso", não só com a arte da persuasão, mas ser um bom ouvinte, faz toda a diferença, entendendo os contextos, entendendo o negócio por trás do resultado, e realizando com planejamento as conversas e os direcionamentos.

• Use collaborative tools I'm proficient in, like Jupyter notebooks and Google Sheets Create prototypes using my skills in Figma and other design tools Establish a data dictionary, leveraging my experience with diverse datasets Involve team members throughout the process, as I did in my roles at NYU and LEAD School Use my leadership communication skills to facilitate productive discussions Apply my experience in strategic data analytics to guide collaboration efforts Leverage my background in product analytics to ensure visualizations meet business needs Use my experience with C-suite reporting to align visualizations with executive priorities

• I work closely with my team to understand their needs and preferences. This collaboration often leads to better visualizations because their input helps me design charts that are not only accurate but intuitive for them to use.

• Data visualization isn’t something you just do once and it’s perfect. You keep revising, tweaking, and after each change, it’s important to ask for feedback to make sure you’re heading in the right direction. The more you go through this back-and-forth, the better it gets. It’s like finding that sweet spot where your visuals look good and make sense . It’s all about making it clear enough to get the message across, bit by bit.

• Creating effective visualizations is an ongoing process. After making adjustments, I ask for more feedback to ensure it’s working. Iterating like this helps me refine the visuals until they communicate the message clearly and effectively.

• Apply my skills in descriptive statistics to analyze visualization usage patterns Maintain a backlog of improvements, drawing on my product development experience Prioritize enhancements based on my strategic data and analytics background Stay updated on new techniques, leveraging my continuous learning mindset Experiment with new methods, applying my diverse technical skills Conduct peer reviews, utilizing my experience in cross-team collaboration Incorporate suggestions efficiently, using my Agile development experience Foster a culture of improvement, as I've done in previous roles Evolve visualizations to meet changing needs, drawing on my adaptability in diverse roles

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