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You’re managing a data visualization project. How can you keep your workload under control?

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Data visualization is a powerful skill that can help you communicate insights, tell stories, and persuade audiences. But managing a data visualization project can also be challenging, especially if you have multiple stakeholders, complex data, and tight deadlines. How can you keep your workload under control and deliver high-quality results? Here are some tips to help you plan, execute, and evaluate your data visualization project.

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John K.

Program and Project Delivery Excellence
Mohamed Meqlad

Data engineer || 3 X Azure certified || Kaggle master
Cristian Alejandro Lavia

Business Intelligence ? Data & Analytics ? Tutor Power BI

1 Define your goals and scope

Before you start creating any charts or dashboards, you need to have a clear understanding of what you want to achieve with your data visualization project. What is the main question or problem you are trying to answer or solve? Who are your target audience and what do they need to know or do? What are the key messages or insights you want to convey? How will you measure the success of your project? These are some of the questions you should ask yourself and your stakeholders to define your goals and scope. Having a clear and realistic scope will help you avoid scope creep, which is when the project requirements change or expand over time without adjusting the resources or timeline.

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Mohamed Meqlad

Data engineer || 3 X Azure certified || Kaggle master
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start by defining clear objectives and outlining a detailed plan. Allocate resources wisely, Regularly monitor progress and communicate effectively with team members and stakeholders. Manage scope creep by evaluating changes' impact on workload and timelines. Utilize project management tools and data visualization software to streamline workflows and track progress. Delegate responsibilities to team members, empowering them to take ownership. Proactively identify and mitigate risks to prevent disruptions. By following these strategies, you can keep your workload under control and ensure the successful completion of your data visualization project.

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Cristian Alejandro Lavia

Business Intelligence ? Data & Analytics ? Tutor Power BI
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En esta primera etapa: - Establecer objetivos claros : alcance del proyecto, los entregables esperados y las prioridades. - Planificación y programación: Crear un plan que incluya las tareas necesarias, asignando tiempos específicos para cada una y estableciendo fechas límite realistas. - Priorización de tareas: Identificar las tareas urgentes y priorizarlas según su impacto en el proyecto y los plazos de entrega. ?éxitos que ya has dado los primeros pasos para concretar el proyecto!

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2 Choose your tools and methods

Once you have defined your goals and scope, you need to decide what tools and methods you will use to create your data visualization project. Depending on your data sources, formats, and complexity, you may need to use different tools for data collection, cleaning, analysis, and visualization. For example, you may use Excel, SQL, Python, or R for data manipulation and analysis, and Tableau, Power BI, D3.js, or Plotly for data visualization. You should also consider what methods or techniques you will use to design and present your data visualization project. For example, you may use exploratory or explanatory analysis, static or interactive visualization, or narrative or exploratory storytelling. You should choose the tools and methods that best suit your goals, data, and audience.

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Cristian Alejandro Lavia

Business Intelligence ? Data & Analytics ? Tutor Power BI
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En ésta segunda etapa elegiría herramientas que se adecuen al proyecto y clientes en sus inicios. - Herramientas de gestión de proyectos, como Notion, Trello o Jira. - Nube: es útil para colaborar en documentos, hojas de cálculo y presentaciones de forma simultánea, por ejemplo Drive u Onedrive. - Herramientas de visualización: personalmente trabajé con Power BI o Looker Studio. Si bien hay otras combinaciones de herramientas, espero les sea útil las nombradas. ?Saludos!

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3 Create a project plan and timeline

After you have chosen your tools and methods, you need to create a project plan and timeline that outlines the main tasks, milestones, deliverables, and dependencies of your data visualization project. A project plan and timeline will help you organize your work, track your progress, and communicate your expectations to your stakeholders. You should also include some buffer time for unexpected issues, feedback, and revisions. A project plan and timeline will also help you prioritize your tasks and focus on the most important or urgent ones. You can use tools like Trello, Asana, or Gantt charts to create and manage your project plan and timeline.

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Cristian Alejandro Lavia

Business Intelligence ? Data & Analytics ? Tutor Power BI
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En la tercera parte recomendada: - Crear un plan de proyecto y un cronograma para organizar, mi aliado aquí un "Calendar" más las herramientas de gestión de proyectos ya nombradas (Notion hoy mi preferida). - Realizar un seguimiento del progreso y comunicar expectativas a las partes interesadas (definir tareas claves, entregables parciales y reserva de tiempo para imprevistos). Trate de resumir solo en 2 item en etapa del proyecto. ?Buena jornada!

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Vikram Nayak

Data Viz | User-Centric Analytics
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I believe the planning stage is tricky in data viz projects. Most plans make one fundamentally flawed assumption: that everything will go according to plan! A good plan will take a lean approach with lots of validation and iteration: - Replay the requirements to stakeholders - Showing intermediate outputs in the form of sketches / wireframes to get directional inputs - Keep an open communication channel with stakeholders. Eliminating wastage and rework in your projects is the best way to keep your workload under control.

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4 Follow data visualization best practices

When creating a data visualization project, it is important to follow data visualization best practices to ensure the quality, accuracy, and effectiveness of your work. Firstly, you should understand your data and its limitations. Make sure to check the validity, reliability, and relevance of your data sources and data points. Additionally, be aware of any biases, errors, or outliers that may affect your analysis and visualization. Secondly, select the chart type and format that best represents your data and message. Avoid using misleading or confusing charts that distort or hide the data. Furthermore, use appropriate colors, fonts, labels, and legends to enhance the readability and aesthetics of your chart. Lastly, keep it simple and clear. Do not overload your chart with too much information or clutter. Use clear and concise titles, captions, and annotations to explain your chart and its purpose. Additionally, use white space, alignment, and hierarchy to create a visual balance and focus.

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Cristian Alejandro Lavia

Business Intelligence ? Data & Analytics ? Tutor Power BI
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En esta etapa es clave seguir las mejores prácticas de visualización de datos, lo que implica a mi entender: - Comprender a fondo los datos que se van a visualizar, incluyendo su origen, estructura y relevancia para el análisis. - Verificar la confiabilidad de la base de datos, asegurando que los datos sean precisos, completos y actualizados. - Realizar un maquetado cuidadoso de las visualizaciones, considerando aspectos como la claridad, la coherencia y la eficacia en la comunicación visual. Respetando imagen corporativa o pedidos del cliente.

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5 Test and refine your data visualization project

Before you finalize and deliver your data visualization project, it’s important to test and refine it to ensure it meets your goals and expectations. Test the project on different devices, browsers, and platforms to check its functionality and compatibility. Invite your intended audience or some representative users to test the project and ask questions such as what the main message or insight of the data visualization project is, how easy or difficult it is to understand and interact with the data visualization project, what they like or dislike about the data visualization project, and what suggestions or improvements they have for the data visualization project. Use this feedback to refine your data visualization project and make it more effective and engaging.

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John K.

Program and Project Delivery Excellence
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Refer back to the RACI matrix. Ensure that your business, operational and technical stakeholders are in alignment and agreement.

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Cristian Alejandro Lavia

Business Intelligence ? Data & Analytics ? Tutor Power BI
(已编辑)
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última etapa de la gestión del proyecto "visualización de datos": - Prueba del proyecto en diferentes dispositivos y plataformas (celulares móviles tanto IOS como Android) - Solicita retroalimentación a los interesados en el proyecto para identificar áreas de mejora. - Utiliza el feedback para perfeccionar y optimizar la eficacia del proyecto. Si les pareció interesante mi experiencia personal, más arriba comenta en cada etapa consejos que pueden serles útiles. ?Saludos colegas!

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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You’re managing a data visualization project. How can you keep your workload under control?

1

2

3

4

5

6

1 Define your goals and scope

2 Choose your tools and methods

3 Create a project plan and timeline

4 Follow data visualization best practices

5 Test and refine your data visualization project

6 Here’s what else to consider

Data Visualization

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You’re managing a data visualization project. How can you keep your workload under control?

1

2

3

4

5

6

1 Define your goals and scope

2 Choose your tools and methods

3 Create a project plan and timeline

4 Follow data visualization best practices

5 Test and refine your data visualization project

6 Here’s what else to consider

Data Visualization

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