You're striving for innovative visualizations. How do you ensure accuracy without stifling creativity?

Understanding the context of your data is crucial for creating effective visualizations. It helps craft visuals that tell a story, which captivates your audience. A deep understanding of your data simplifies chart creation, making them clear and informative. In the past, I've made complex visuals that were hard to understand, especially for non-technical viewers. By diving deeper into the data, I learned to remove irrelevant parts, enhancing clarity. This deep dive also helps identify root causes, making your analysis more complete. Knowledge of your data transforms complex visuals into engaging stories.

Understanding data involves recognizing its types, sources, and methods of collection. It includes distinguishing between qualitative data, which is descriptive and subjective, and quantitative data, which is numerical and objective. Effective data analysis requires knowledge of how data is gathered, whether through surveys, experiments, or observations. By comprehending the context and methods behind data collection, one can draw meaningful insights, make informed decisions, and contribute to research with accuracy and reliability.

Creating innovative data visualizations requires a balance between creativity and accuracy. Start by defining clear objectives and using reliable data sources. Choose visualization formats that fit your data, and use interactive features and thoughtful design to enhance engagement without misleading users. Validate your work through peer reviews and user testing, and ensure transparency by documenting design choices and providing context. Continuous refinement and staying updated with best practices help maintain this balance, ensuring that your visualizations are both captivating and precise.

Deeply analyze and comprehend the data before visualizing it. Ensure that you grasp the context, source, and implications of the data. Accurate understanding lays a solid foundation for creating innovative yet precise visualizations.

This is a century of object oriented programming. The need of data interoperability has evolved. Primitive types are no longer enough. There is one grand tradeoff that most data people experience today : Frameworks are built to help you create your visualizations better. But there's no standardization to handle data accross multiple platforms. They intend to provide their own way to transform your data into a visualization. So, understanding your own data will help you translate your need into a correct visuals. Even most of the times when we deals with various tools, we are meant to be a language translator accross the platforms.

Select appropriate visualization types and tools that align with the data and objectives. Choose formats that best represent the data’s story without compromising accuracy. Consider how different visualizations can highlight or obscure key insights.

All eventually come down to cost. Let me straight jump into the example of tableau and powerbi. PowerBI has lot more limitations and it’s very challenging to build ‘nicer-looking’ or the most adequate visualizations. It’s full of constraints and hiccups in experience. But most of times, PBI is way more preferred over Tableau due to its price. So even if selections and choosing right visual is important, cost is the main driver unfortunately.

Choose the best visualizations which present the data in best possible manner and show the reflection of how decisions to be made from a glance.

Ce qui sera présenté au travers d'un graphique doit être signifiant. Les rapports de grandeur doivent être clairs et constants, les unités doivent correspondre au langage habituel de l'utilisateur afin que la lecture soit naturelle, fluide et non génératrice d'interprétation

Develop multiple versions of your visualizations and refine them based on iterative feedback and testing. Iteration allows you to balance creativity with accuracy, improving the clarity and effectiveness of the visualizations.

My favorite quote is “shared document doesn’t mean shared understanding” Iterative process is crucial to better design for sure but more importantly the iteration becomes a nugget for stakeholder to chew on and react toward. It helps data viz designers to get closer to what the ultimate needs are and what we need to prioritize. Quick visual iterations can answer more questions than the hundred requirement documents do.

Always iteration is helpful to develop a beautiful outlook. The nutcracker job is done with the useful delivery of projects and feedback. The visualizations help our creative thinking to present the roadmap for understanding things.

Une fois les données analysées et le but à atteindre bien circonscrit, on peut alors commencer à alimenter les graphiques. Il est souhaitable de commencer par petites touches, et d'en vérifier tant la cohérence que le confort de lecture, puis d'étendre progressivement le champ d'analyse des données. A la moindre alerte de confort de lecture, ne pas hésiter à revenir au point 0. Le résultat final en dépend !

Incorporate feedback from peers, stakeholders, and users throughout the design process. Constructive criticism helps identify potential inaccuracies and areas for improvement, ensuring the final visualization is both innovative and reliable.

Always feedback is greatly appreciated as it is the best final source from where the improvement starts. Implementation of feedback gives the best quality output that the clients demand.

This is the most important step to build trust in dashboards! Being able to tell someone exactly how accurate something is (with a %!) builds trust! It also allows you to be notified to fix an accuracy issue before the wider team realizes it

Letting the attachment to a certain design decision is the biggest thing in feedback loop. Preventing yourself from spending too much on iteration as well as preventing others from being attached to some idea are super important and it allows the feedback cycle healthier and smoother. (Plus quicker)

Même si souvent les restitutions graphiques sont demandées par une personne ou un petit groupe de personnes, il est important que les visuels puissent être compris de tous, pas uniquement des initiés. A la limite un adolescent normal devrait pouvoir en comprendre le contenu...

Strive to simplify complex data while maintaining its integrity. Use creative design techniques to present data clearly without oversimplifying or distorting the underlying information. Effective visualizations should enhance understanding, not confuse or mislead.

Simplifying means making complex data simple. It is more complicated than putting a bunch of questionable charts together where people need help understanding what's presented. Maybe a 30-slice exploding pie chart looks cool, but good luck grabbing insights from it. Think of your users. Use pre-attentive attributes, accessibility, and the appropriate charts to help your stakeholders understand and use the data visualization. Far too often, these considerations are overlooked.

Ici on en revient au premier point abordé : si les données et buts recherchés sont clairs, cela vous permettra de filtrer efficacement sur l'essentiel, c'est à dire ce qui s'avèrera significatif lors de la lecture. Tout le reste ne fera que complexifier inutilement la lecture et rendra votre graphique illisible.

Ensure that your visualizations adhere to ethical standards, avoiding misrepresentation or bias. Present data transparently and accurately, and be mindful of how design choices might influence interpretation or perception.

Regularly review and update your visualizations to reflect new data or insights. Stay informed about the latest trends and techniques in data visualization, and integrate them thoughtfully to enhance both creativity and accuracy.