Here's how you can present time-based data in a clear and understandable way.

Choosing the right chart type is essential when visualizing time-based data. Line graphs excel in illustrating trends over time, while bar charts are effective for comparing discrete intervals. For datasets with multiple variables, consider multi-line or stacked bar charts. The key is aligning the chart type with the narrative you aim to convey; a mismatch can obscure rather than illuminate insights in data visualization.

First things first, timeline must be horizontal. If there are only a few data points across your timeline, clustered or stacked column charts are your best bet. Beyond that, you may opt for line charts or shaded line charts. Do not overcrowd your chart by squeezing in too much data. Allow the user to slice the data to focus on specific periods/dimensions.

Choose the right charts like line charts, bar charts, or area charts to show trends over time. Simplify data by focusing on key data points and avoid cluttering the chart with too much information. Ensure axes, labels, and legends are clear and descriptive. Use colors, markers, or annotations to emphasize important trends or changes. Include titles, subtitles, and brief descriptions to explain the data and its significance. Use appropriate scales and intervals, and avoid distortions that could mislead the viewer.

Effectively communicating trends requires a strategic approach to time-based data. Here's how to ensure clarity and impact: Chart Selection: Align your chart with the narrative. Line charts excel at showcasing trends, while bar charts depict changes at specific intervals. For intricate patterns, consider heatmaps. Spotlight Key Moments: Emphasize critical events or milestones through chart annotations. This helps viewers grasp the significance of changes within the timeline. Contextualize Your Data: Establish a clear timeframe and scale on your axes. Employ descriptive labels and, if necessary, a concise narrative to illuminate the "why" behind the trends.

To present time-based data clearly, use line charts to show trends over continuous periods and bar charts for discrete time intervals. Ensure your axes are labeled clearly, with a consistent time scale, and avoid clutter by focusing on key data points. Highlight important periods or events with annotations. Use color effectively to differentiate between multiple data series without overwhelming the viewer. Implement interactive features like tooltips and zoom functions in digital presentations to allow users to explore the data in more detail. Additionally, consider using moving averages or trend lines to smooth out fluctuations and emphasize overall patterns. Regularly review and refine your visualizations based on feedback.

A la hora de representar datos en el tiempo el gráfico de líneas es el rey. Para mejorar la comprensión de la información, siempre podemos a?adir elementos adicionales que remarquen tendencias, valores extremos, proyecciones, ...., pero siempre que dicha información aporte valor y no distraiga al usuario de negocio con un exceso de elementos visuales. Así mismo, especialmente si estamos mostrando series largas, hay que tener mucho cuidado con las etiquetas de datos, procurando mostrar únicamente aquellas que puedan ser más significativas. Como siempre, a?adir contexto va a permitir una mejor interpretación de los gráficos, y una buena forma puede ser utilizar subtítulos que muestren información relevante para el usuario

In some cases, it might be helpful to forecast a time series into the future - if so, make the switch from historical figures versus the future forecast clear on the line graph by changing the solid line to dotted, and be sure to show a confidence interval that widens further into the future.

Use proper upper and lower range. If necessary, pinpoint in a second visual the highlights. This will help to make easier the comprehension of the history you are trying to explain. Just be carefull for do not manipulate the result, just use a smart way to present.

Trend Lines: Add trend lines to highlight overall direction. Annotations: Use callouts to explain significant events or data points. Smoothing: Apply smoothing techniques to reduce noise and emphasize the trend

If the intention of your visualization is to highlight changes over time then include trend lines or averages for a period so that users can quickly see the difference from one period to another if the changes the user is looking for is periodic. In a time series line chart if users are looking for unusual activity highlight outliers and anomalies. This draws the users attention to statistically relevant results not just guessing which periods may or may not have had a significant change.

Gráficos de apresentam dados através do tempo, como um gráfico de linha podem facilmente ficar poluídos com muitas informa??es. Tente sempre deixar o mais claro possível o que está sendo analisado naquele gráfico, escolha corretamente os intervalos de data, se estiver apresentando dados de uma ano inteiro, talvez um gráfico dia a dia possa tirar a clareza do dado, por exemplo. Outra dica é escolher onde colocar os rótulos de dados, talvez os valores plotados nos pontos de dados sejam mais legíveis do que em um eixo no canto do gráfico.

Für eine Bessere Lesbarkeit kann auf die Beschriftung der Wert-Achse h?ufig verzichtet werden. Stattdessen ist es oft besser die Datenpunkte bzw. Balken direkt mit ihrem Wert zu beschriften oder den Wert bei einem Mouse-Over erscheinen zu lassen. Horizontale Hilfslinien verwirren h?ufig mehr als das sie tats?chlich helfen.

Es super importante la correcta visualización de los datos y es no menor la correcta definición de los ejes, ?Quién no ha visto estos gráficos de barras con ejes distorsionados que nos hacen interpretar mal los resultados?

Time Intervals: Use consistent intervals (days, months, years) to make comparison easy. Labels: Keep axis labels clear and concise. Gridlines: Use gridlines sparingly to avoid clutter.

Gráficos precisam ser simples e capazes de identificar a varia??o de valores, escolha bem a escala dos eixos para a informa??o ficar clara e de fácil entendimento. Simplifique números, use poucas casas decimais e evite espa?os em branco no registro isso vai trazer um resultado muito satisfatório na apresenta??o dos resultados.

The first impression of a report dashboard is closely linked to the choice of colors??. -It's best to limit the color palette to 3-4 colors and use them consistently. -Websites like Colour Hunt and Canva are excellent resources for selecting a palette. -Use red to indicate negative trends?? and green for positive trends ??.

O uso de contraste é importante para diferencias características diferentes. Cuidado com o contexto do grafico em uma apresenta??o para que a paleta de cores n?o fique completamente descasada com o estilo da apresenta??o.

Nobody denies the importance of color coding in visualizations, but selecting the appropriate colors and limiting the number of colors is crucial for making charts more comprehensible and less overwhelming.

El uso del color de forma adecuada es una herramienta sumamente importante en cualquier cuadro de mando. En general los tonos claros son más fáciles de "manejar" que los modos oscuros, y por eso estos últimos hemos de utilizarlos con sumo cuidado para no crear visuales estridentes. En las series temporales hemos de cuidar la asociación de colores y conceptos, de forma que utilicemos, por ejemplo, verdes para valores positivos y rojos para valores negativos, y una vez elegido un color para un concepto mantenerlo en todo el informe, y de esa forma de un vistazo sabremos identificar fácilmente que se está representando.

Implement hover-over details to show additional data without cluttering the chart. Use filters to allow users to customize the view and focus on specific time periods. Interactive elements like zooming and panning can help explore data in depth.

Not just sliders and filters; creating visualizations that allow drilling down to the lowest level of your dataset can be highly effective. Additionally, incorporating hyperlinks within your visualizations can lead users to other apps and provide further information.

Interactivity is a game-changer in data visualization, it improves the clarity of time-based data. Sliders and time controls are used to adjust the time range dynamically, sliders can zoom in specific periods and reveal finer details. Filters or hover-overs enable users to engage the data at their own pace and focus on the most relevant aspects. Interactivity empowers users to explore data actively and discover meaningful patterns.

Debemos tener en cuenta que elementos interactivos como "Drill Downs" o ejes deslízantes pueden ser un arma de doble filo, y depende del lugar del informe para incluirlos o no. Si estamos en una pantalla de resumen de la información más relevante, toda la información debería verse de un vistazo, y un exceso de datos implicaría que parte de la información quedase escondida. Así mismo, un drill down puede esconder el hecho relevante que queremos mostrar, por lo que este tipo de elementos interactivos es mejor dejarlos para páginas de análisis en detalle dise?ando una experiencia de navegación en el informe intuitiva y fácil de utilizar. Igualmente, elementos como bookmarks tienen mucha utilidad, siempre y cuando quede clara su presencia

Hover Effects: Provide additional details on hover. Filters: Allow users to filter data to see specific time periods. Zoom & Pan: Enable zooming and panning for detailed analysis.

Establish a clear timeline, using consistent intervals to help the audience follow trends and patterns. Visual elements like line graphs or bar charts portray changes over time, ensuring each point is marked and labeled. Give data context with annotations to highlight significant events or trends. Use color and design elements in moderation to maintain focus on data progression.

?? Every dataset has a story. Highlight the key points and use storytelling techniques to make your data come alive. Explain the “why” behind the trends. - ?? Provide Context and Insights: Context is king. Explain any external factors that might have influenced the data. Share insights that can help the audience understand the implications and relevance of your findings.

Include a brief narrative or caption to explain the significance of the data. Use storytelling techniques to make the data more relatable. Highlight key insights and takeaways to guide the audience.

Context: Provide context for the data trends. Stories: Create a narrative that guides the viewer through the data. Explanations: Explain why trends are happening, not just what is happening.

Creating a data visualisation is like being a storyteller. So everyone is a storyteller, but it takes something to be a good storyteller. How can we do that in data visualisation ? we should use appropriate colours informative labels , use appropriate charts with newness , maybe scatter charts, maybe butterfly charts, maybe donut charts, maybe gauges, anything that helps to tell the story in a quick and compelling manner

Context: Always provide context for the data. Explain where it comes from and why it's relevant. Consistency: Maintain consistent formatting, scales, and design across all charts to make comparisons easier. Accessibility: Ensure that charts are accessible to all users, including those with color blindness or visual impairments. Use patterns or textures along with colors.

Remember that when viewing line graphs, the main thing people perceive is the angle of the lines. As popularised by Tufte after a 1988 paper by Cleveland, McGill & McGill, it is good practice to adjust the aspect ratio or scales of your chart so the lines ‘bank to 45 degrees’, i.e. have most of the slopes nicely mid way between horizontal and vertical. This tends to make it most easy to correctly perceive the trends in the data.

There are two main ways to present time-based data: - Textual formats: This includes tables, spreadsheets, and narratives. These are good for conveying specific data points or for in-depth analysis. For example, a table might show website traffic by hour for the past week. - Visualizations: This includes charts and graphs. These are great for identifying trends and patterns over time. Line graphs are a classic example, but there are many other options depending on your data. To present this data well, label your charts clearly, use consistent time scales, pick the right visualization (like line graphs for trends), highlight important parts, and avoid cluttering your presentation.

The best advice I can share is get advice from the best. Buy, read and enjoy Edward Tufte’s magnificent books. Study NYT’s outstanding visuals. Don’t treat LinkedIn snippets like this as sufficient advice in themselves.

Verschiedene Diagrammen stehen selten für sich allein. Sie sind Bestandteil von Standardreports oder Dashboards. Essenziell ist das alle Diagramme (eines Typs) über das Dashboard oder den Report gleich gestaltet sind. D.h. Gleiche Farben für Ist-Werte in allen Diagrammen, gleiche Farben für Abweichungen in allen Diagrammen, usw. Solche Gestaltungsrichtlinien helfen den Anwendern schnell den gewünschten Insight aus den Diagrammen zu ziehen. Vor jeder Auswahl eines Diagrammtyps steht aber die inhaltliche Betrachtung. Welche Fragestellung soll das Diagramm eigentlich beantworten? Dazu ist es essenziell die Endanwender zu befragen.