How do you choose the right chart type for your data visualization needs?

When choosing a chart type for data visualization, you can consider what you want to communicate with your data, the story you want to tell, and how many variables and data points you want to display: Compare values: Use a pie chart for relative comparison or a bar chart for precise comparison Compare volumes: Use an area chart or a bubble chart Show trends and patterns: Use a line chart, bar chart, or scatter plot Visualize continuous data over time: Use a line chart Show change over time: Use a line, bar, or column chart Display parts of a whole: Use a pyramid or pie chart Visualize a lot of data: Use a scatter plot or treemap Show the relationship between two or more variables: Use a scatter plot

Understanding the type of data I work with has helped me so far in knowing the visual type to opt for. Is your data numerical i.e. Quantitative with various category? you will see that pie-chart won't give the ultimate insights. However, a bar chart would help. Likewise a line chart would be the best fit for trend time data to see the changes that occur over time.

According to my experience, answering some basic questions about the data structure could give insightful input for appropriate decisions on data visualization: - How many dimensions are you about to visualize? - What are the key identifier columns? In other words, how is data grouped for calculation? - What is the data type for each column? (categorical/numeric) - How many distinct values does each categorical variable have? - Is the dataset time-series or cross-sectional? - If it is a time-series, does it have any specific consistent frequency of recording? e.g. use line charts for time-series with inconsistent frequency of recording could mislead patterns. - Are the values in numeric columns absolute values or percentage values?

As per my experience knowing your data thoroughly is the first step in creating a good dashboard which gives meaningful insights as well as looks appealing to the audience for whom it is being created. E.g it’s not effective to show volumetric data in a tabular format. One should basic area charts for a good interpretation. Similarly there are many instances where one can improve the way of depicting the data by choosing the right chart based on the data.

Choosing the right visualization type is not that hard as you may thing. First you need to find out what you want to show? Do you want to show a trend over time? Or maybe you want to show a comparison between different categories. After you know that it is important to find out what the user really wants to know. Is he more interested in the total numbers? Does he only need the deltas? Does he need the deviation in percent? Is he only interested in the past or also in a forecast? What drives his decisions? Theses things should be recognizable immediately. And your dashboard or collection of viz should be build in a way that it "tells him the story" he needed to find out.

Charts are tools designed to simplify and visualize our ideas for the audience. If a chart fails to capture the intended message or, worse, confuses the audience, then it defeats its purpose. When plotting data on a chart, it's crucial to review whether it effectively communicates the desired information. From my experience, charts are particularly useful for making comparisons and showing trends.

Define the Objective: Clarify what you want to communicate with your data. Common objectives include: Comparison: Comparing values across different categories (e.g., bar charts, column charts). Distribution: Showing the distribution of data points (e.g., histograms, box plots). Trends: Displaying changes over time (e.g., line charts, area charts). Relationships: Demonstrating relationships between variables (e.g., scatter plots, bubble charts). Composition: Breaking down data into components (e.g., pie charts, stacked bar charts). By carefully considering these factors, you can select the most effective chart type to communicate your data clearly and compellingly.

Best use case can be when an educational institution for example wants to understand the distribution of students' test scores to identify the spread and any potential high performers. To show the distribution and central tendency of test scores. The best Visualization to use would be histogram and box plot which has an encompassing visuals for various summaries: A histogram can show the frequency distribution of test scores, illustrating how scores are spread across different intervals. Whilst a a box plot can summarize the distribution, showing the median, quartiles, and any outliers in the test scores. I recently just used this whilst analyzing assessment scores of Field data Agents at Melon

Yes, in my experience the stakeholders engagement plays a very crucial role. To understand the right KPIs and your visualization shall be able to help business/ Ops team to take real time decision. Aligning with the business goals. If you could also develop the business understanding then you can provide ls additional north star metrics which business team might not have thought about. Several graphs and charts do help in better visualization like bar charts, Scatter plot, histogram,etc.

depends on the kind of data you have and the message you want to convey. Purpose: What do you want to achieve with the visualization? Audience: Who will be viewing the chart, and what is their level of expertise? Data Type: Is your data categorical, continuous, or a mix?

In my opinion, understanding the needs and knowing the audience for your visualizations is crucial. People see the world differently and have varying needs at any moment. We can create the fanciest dashboards, but sometimes a simple Excel table is more effective for someone used to it. Our message must be tailored to the audience's age, data analysis skills, and characteristics. For instance, business stakeholders might prefer straightforward bar charts, while data scientists appreciate complex visualizations like heatmaps. In high-level executive meetings, clarity and brevity are essential. In detailed analytical reports, more complex charts may be appropriate.

La audiencia y el objetivo son la clave. Definir como comunicar, que historia contar con nuestros hallazgos, depende sin duda de la audiencia. Y no solo por cuestiones técnicas, sino también por el trato que deben recibir. Por ejemplo, si sabemos que vamos a presentar nuestro tablero a colaboradores que conocemos y tenemos un buen ambiente de trabajo, podríamos introducir el "humor" dentro de nuestro storytelling.

It's absolutely valuable to understand whom you're catering your report or dashboard to in order for the target audience to comprehend the insights conveyed through the visuals. Let's say you're building a dashboard for the c-suite or leaders in the organisation, it's always better to keep your visuals straight forward and showing up aesthetically to keep the attention span longer. Your visuals can always be improved based on the technical acumen of the end user or the team. While, the visuals catered to a BR or a customer facing team would need to be more insightful to take informed and holistic decision serving a more personalised information using the data available, where the design thinking principles can be of much help.

Once you are clear about: Who?( Who is the audience) You can focus on What?( what is the information i shall provide) then how ( the chart to be used) Understanding your audience's needs and the end users purpose is the guiding light for any visualisation.

A dataset offers multiple options for visualization through various chart types and each type could convey very different messages. Therefore, it's crucial to first identify your audience (whether they're technical or business-oriented) and understand their specific needs (such as trends, period over period changes, etc.) before selecting a visualization. Rather than solely relying on personal preference, choosing a data visualization tailored to your audience ensures effective communication of insights.

Um gráfico eficaz transmite a mensagem de forma rápida e clara. Gráficos complicados podem confundir e levar à má interpretação dos dados. A simplicidade também deve ser aplicada ao design, usando cores e textos de maneira parcimoniosa e intencional para realçar informações importantes sem sobrecarregar o leitor. Em resumo, gráficos simples e bem projetados são fundamentais para comunicações eficazes.

In my experience, dashboarding should be done in iterations. One should start from simple visuals that summarise and label the right business KPIs initially and should cover drill down views in further iterations. Though the tool suggests a lot of complex visuals available at the click of a button but the developer should avoid one click gratification and rather focus on how simple it is to interpret the visual and how clearly it addresses the business requirement.

Simplicity in charts is crucial for effective communication of data. A well-designed chart should convey its message quickly and clearly, without requiring extensive interpretation. Overly complex visuals can distract the viewer and lead to misunderstandings or misinterpretation of the data. By choosing the simplest chart type that serves the purpose, and using design elements like color and text sparingly and purposefully, one can enhance the viewer's understanding.

I strongly believe highlighting the most important data points that convey your main message. It is best to avoid including unnecessary details. For bar and pie charts, kindly limit the number of categories to prevent visual clutter. Combine less important categories into an "Other" category if needed. Using a consistent color scheme throughout visualization has become my forte. This helps maintain a cohesive look and feel. Use color to draw attention to key data points or trends. For example, use a distinct color shade to highlight a significant increase or decrease.

Sometimes less is more! 💡So I think knowing your audience is the key to success when building a data visualisation 🤓 If your stakeholders are not familiar with the data or the visualisation tool, just choose the simplest chart that show them what they need to see or analyse. Same applies to color pallets. Be aware everyone feels comfortable with your design to avoid confusion.

Choosing the right chart type for your data visualization comes down to being open-minded and trying different options until you find what works best. Don't feel stuck with your initial choice; software tools make it easy to switch between chart types, so feel free to experiment. Keep an eye out for any new insights or clearer understanding that might come from a different way of visualizing your data. Being flexible and open to trying out various approaches can help you refine your visualizations and make them more effective in the end.

Create multiple versions of charts and gather feedback to see which best communicates the intended message. Be open to revising the choice of chart based on new insights or audience feedback. Categorical: Data sorted into groups or categories (e.g., gender, departments). Numerical: Data measured in numbers (e.g., sales figures, temperatures). Time Series: Data points collected or recorded at specific time intervals. Geographical: Data related to locations or spatial regions. Utilize visualization tools like Tableau, Power BI, or Python libraries (Matplotlib, Seaborn) that offer a range of chart types and customization options.

Sometimes the first chart you choose isn't the most effective. Be flexible and willing to try different types to find the best fit for your data. Software tools often allow quick changes between chart types, making it easy to experiment. Look for unexpected insights or clarity that might emerge from a different visualization approach. The flexibility to iterate can lead to a more refined and effective final product.

I was building a dashboard recently, knowing fully well that the project funders are very interested in gender disaggregation. For similar data points, it meant I had to be creative. I used distinct colors to represent different genders (e.g., blue for males and pink for females). This made it easy to visually distinguish between genders at a glance. For bar charts and histograms, I placed bars for males and females side by side for each category. This allowed for direct comparison between genders within each data point. Where applicable, I used stacked bar charts to show the composition of each category by gender. Each bar was divided into segments representing males and females, showing both the total and the gender breakdown.

En este punto opinaré sobre Power BI, herramienta que manejo. En la mayoría de los casos, podemos usar mas de un gráfico para explicar el resultado, sea que tenemos un análisis temporal, o de mas de una dimensión para incluir, Power BI en sus gráficos nativos incluye varias opciones para aplicar. En tal caso podremos usar gráficos no nativos y gratuitos para dar ese plus de ser necesario.

Continuous learning in data visualization is key for staying current with evolving techniques and technologies. There are some actionable insights to help, such as staying updated with industry trends, engaging with online courses, mastering software and tools, practicing and experimenting, involving oneself in the community, reading foundational books, critically evaluating work, learning from case studies, contributing to open-source projects, and maintaining curiosity. In summary, continuous learning in data visualization is a blend of staying updated, hands-on practice, community engagement, and critical evaluation. Embrace every opportunity to learn, share, and grow in this dynamic field.

Siempre hay nuevas herramientas y técnicas por aprender. Aprender a usar herramientas como Tableau o Power BI puede abrir nuevas posibilidades para tus visualizaciones, es importante participar en distintos cursos, workshops y en las distintas comunidades de datos; que hay muchas. Hay muchas oportunidades para aprender, compartir y crecer. Mantenernos actualizados con las últimas tendencias y herramientas nos ayudará a mejorar continuamente nuestras habilidades de visualización de datos.

This is a great reminder about the ongoing importance of data visualization skills. By continuously learning and experimenting with different datasets and charts, we can develop a strong intuition for choosing the right visuals to communicate our message effectively. This will ultimately lead to better decision-making across the board. Also, most of data analytics certifications will always focus a chapter about choosing the right visualization for the right type of data.

Stay abreast of advancements in data visualization tools and techniques. Engage with communities such as those on LinkedIn, attend webinars, and read up on the latest research to incorporate innovative visualization methods. For example, the use of storytelling techniques in data visualization is gaining traction, where the visualization not only presents data but also guides the viewer through a narrative, making complex data more relatable and actionable.

- Follow Trends: Stay informed about the latest data visualization techniques and tools. For example, advancements in interactive visualizations can provide more engaging and insightful presentations of data. - Educational Resources: Use books, online courses, and webinars to deepen your understanding of data visualization principles. Resources like Edward Tufte’s books on visualizing information are highly recommended.

Think about the decisions your customer needs to make with the data and reduce the cognitive load required to achieve this. - pies are for eating not charting... (go read some Stephen Few content) - consider input from ux best practices in addition to your knowledge of the data - if the visualisation has no purpose - don't choose one at all. - don't show how much you know about charts/data - show them the answer they need. - if it's on a dashboard or report with other charts make sure you consider the impact of the surrounding visuals relative to your choice. - gen ai - may not have been trained well enough to suggest the correct chart for your customer, critically assess it's suggestion if you prompt for your choice.

Well most of the times, I have observed people use pie and column charts the most and even that incorrectly. People are not trained or made aware why each basic chart is used for, and I usually conduct training sessions on 'understanding basic charts' followed by advance charts. 1. Column / called as bar chart by freshers - to show count and comparison between multiple groups (team wise, state wise, city wise, category wise, etc.) 2. Pie chart - to show ratio / contribution of a main volume or dataset (% of x over y or others) 3. Line / run chart - to show data point over a period of time (spread over hours, dates, weeks, etc) Once these are mastered, then move to advance like scatter, histogram, stack, Pareto, trend, etc.

Necesitas analizar grandes volúmenes de datos? Quieres compartir insights de manera visual con otros? Buscas una forma intuitiva y rápida de encontrar tendencias y patrones en tus datos? Deseas realizar análisis profundos, como análisis de tendencias, pronósticos y modelado estadístico? Tableau permite maximizar el uso de los datos facilitando la toma de decisiones informadas y estratégicas. Además, su capacidad para transformar datos en visualizaciones comprensibles lo hace una herramienta esencial en la era del Big Data.

Visualization is a means of communicating a story to the audience. It isn't drawing the fanciest graphs possible. While eye catching colors and patterns help, they should be treated as a means to the end instead of the end itself.