19.2 Creating visualizations and dashboards in Tableau
4 min read•july 30, 2024
Tableau's visualization tools empower users to create impactful charts and dashboards. From basic bar graphs to advanced bullet charts, Tableau offers a wide range of options to effectively present data. Built-in features like and streamline the process of crafting compelling visuals.
Interactive dashboards take to the next level. By incorporating , , and actions, users can dynamically explore data relationships. ensure optimal viewing across platforms, while analytics functions enable deeper insights through , forecasting, and statistical analysis.
Tableau Visualization Basics
Leveraging Built-in Chart Types
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- Tableau provides a wide range of built-in chart types (bar charts, line charts, scatter plots, pie charts, maps) which can be used to create basic and advanced visualizations
- Dimensions are categorical variables used to slice and dice data while measures are numeric values that can be aggregated
- Tableau's Show Me feature suggests appropriate chart types based on the selected dimensions and measures enabling users to create effective visualizations quickly
- Tableau allows users to create calculated fields using a combination of dimensions, measures, and functions to derive new insights from the data
Advanced Chart Types and Techniques
- Advanced chart types in Tableau include:
- Bullet graphs used to compare actual values against target values or ranges
- Box-and-whisker plots used to display the distribution of a dataset based on five summary statistics (minimum, first quartile, median, third quartile, maximum)
- Gantt charts used to visualize project schedules and timelines showing the start and end dates of tasks
- used to display the distribution of a continuous variable by dividing the data into bins
- Tableau supports the creation of dual-axis charts allowing users to combine two different chart types or scales in a single visualization to compare multiple measures or dimensions
Interactive Dashboard Design
Effective Dashboard Design Principles
- Dashboards in Tableau are a collection of related visualizations allowing users to gain a comprehensive view of their data and interact with it to derive insights
- Effective design involves considering the audience, purpose, and key metrics to be displayed ensuring that the most important information is easily accessible and understandable
- Tableau's dashboard layout containers (horizontal, vertical, floating) help organize and resize visualizations within the dashboard ensuring optimal use of space and responsiveness across devices
- Best practices for dashboard design include:
- Maintaining a consistent and throughout the dashboard
- Using clear and concise titles, labels, and annotations to guide users through the data story
- Leveraging effectively to avoid clutter and improve readability
- Providing context through comparisons, benchmarks, or trends to help users interpret the data accurately
Interactive Features and Device-Specific Layouts
- enable interactivity between visualizations (filtering, highlighting, linking) allowing users to explore data dynamically and uncover hidden patterns or relationships
- Tableau's device-specific dashboards feature allows designers to create separate layouts optimized for desktop, tablet, and mobile devices ensuring a seamless user experience across platforms
Enhancing User Interactivity
Filters and Parameters
- Filters in Tableau allow users to narrow down the data displayed in visualizations based on specific criteria enabling focused analysis and exploration
- Tableau supports various types of filters including dimension filters (based on categorical variables), measure filters (based on numeric values), and date filters (based on time periods)
- Filters can be applied at different levels (worksheet-level, dashboard-level, data source-level) providing flexibility in controlling the scope of the filter
- Parameters are dynamic values that can be used to modify calculations, filter conditions, or reference lines in visualizations allowing users to explore different scenarios or perform what-if analyses
- Parameters can be used to create interactive visualizations (selecting a specific measure to display, adjusting the threshold for a calculated field)
Dashboard Actions and Interactivity
- Actions in Tableau enable interactivity between visualizations within a dashboard or across different dashboards including:
- Filter actions filtering data in one visualization based on the selection made in another visualization
- Highlight actions highlighting data points in one visualization based on the selection made in another visualization
- URL actions opening a web page or external application based on the selection made in a visualization
- Dashboard actions can be triggered by hovering, selecting, or menu options providing users with various ways to interact with the data and uncover insights
Data Exploration with Tableau Analytics
Built-in Analytics Functions
- Tableau offers a range of built-in analytics functions enabling users to perform advanced calculations and statistical analyses directly within the platform
- allow users to perform computations across rows or columns of a visualization (running totals, percent of total, rank, moving averages) providing additional context and insights
- Level of Detail (LOD) expressions enable users to compute aggregations at a different level of granularity than the visualization itself (calculating the average sales per customer while displaying data at the product level)
- and help users identify patterns and project future values based on historical data using statistical methods (linear regression, exponential smoothing)
Advanced Analytics Capabilities
- Tableau's clustering feature allows users to group similar data points together based on selected dimensions and measures helping to identify patterns and segments within the data
- The function enables users to analyze the distribution of a dataset identifying outliers and comparing different groups or categories
- Tableau's built-in R and Python integration allows users to leverage the power of these statistical programming languages directly within the platform enabling advanced analytics and custom visualizations
Key Terms to Review (30)
Bar chart: A bar chart is a visual representation of categorical data using rectangular bars to show the quantity or frequency of each category. It allows for easy comparison between different categories, making it a fundamental tool for summarizing and analyzing data in various contexts.
Box-and-whisker plot: A box-and-whisker plot is a standardized way of displaying the distribution of a dataset based on five summary statistics: minimum, first quartile (Q1), median (Q2), third quartile (Q3), and maximum. This type of plot provides a visual summary that makes it easy to identify the central tendency, variability, and potential outliers in the data. It's particularly useful for comparing distributions across different groups.
Bullet graph: A bullet graph is a variation of a bar graph designed to convey a lot of information in a compact space. It typically displays a single measure along with contextual information such as target markers and qualitative ranges, making it easier to compare performance against a goal or benchmark. This type of graph is particularly useful in dashboards for visualizing key performance indicators (KPIs) at a glance.
Calculated Fields: Calculated fields are user-defined fields in Tableau that allow you to create new data from existing data using formulas and expressions. These fields enable users to perform operations like mathematical calculations, string manipulations, or logical comparisons, enhancing the overall analysis and visualization capabilities. By leveraging calculated fields, you can dynamically modify and refine your data, leading to more insightful dashboards and reports.
Clustering: Clustering is the process of grouping a set of objects in such a way that objects in the same group (or cluster) are more similar to each other than to those in other groups. This technique helps to reveal patterns and relationships in data, making it easier to visualize complex datasets through different visualization methods.
Cognitive Load: Cognitive load refers to the amount of mental effort being used in the working memory. When designing visualizations, it's essential to consider cognitive load because too much information can overwhelm viewers, making it difficult for them to process data effectively. Striking a balance between providing enough information and avoiding clutter is crucial for enhancing understanding and retention.
Color scheme: A color scheme refers to a planned combination of colors that are used in a visualization to convey information effectively and create a visual appeal. It involves the selection of colors that work well together and helps enhance readability, interpretability, and overall user experience in visualizations and dashboards. An effective color scheme not only aids in distinguishing different data points but also helps evoke emotions and convey meaning.
Dashboard: A dashboard is a visual display of key information and metrics that provide an overview of performance and progress in real-time. Dashboards aggregate data from multiple sources, enabling users to monitor trends, identify patterns, and make informed decisions quickly. They often utilize various visualization techniques to present complex data in a simplified manner, enhancing clarity and engagement.
Dashboard actions: Dashboard actions are interactive elements in data visualization that allow users to engage with the data in a dynamic way, such as filtering, highlighting, or navigating between different views. These actions enhance user experience by providing the ability to explore data more deeply and make insights more actionable. By using dashboard actions effectively, creators can improve user interaction and drive better data-driven decision-making.
Data accuracy: Data accuracy refers to the degree to which data is correct, precise, and reliable. It ensures that the information used for analysis, reporting, and decision-making is free from errors and accurately reflects the real-world conditions it aims to represent. High data accuracy is crucial because any inaccuracies can lead to misleading conclusions and poor decision-making in various fields, particularly when cleaning, preparing data, and visualizing it for dashboards.
Data exploration: Data exploration refers to the process of analyzing and visualizing data sets to discover patterns, trends, and insights before applying more formal analysis techniques. This initial stage is crucial as it helps to identify important characteristics of the data, such as anomalies or correlations, which can influence subsequent analyses and visualizations. It often incorporates interactive elements and filtering techniques to engage users and facilitate deeper understanding.
Data storytelling: Data storytelling is the practice of using data and visualizations to convey a narrative that helps audiences understand complex information and derive insights. It combines data analysis with narrative techniques to engage viewers, making the information more relatable and easier to remember. By leveraging visual elements, it creates a cohesive and compelling story that can influence decision-making and drive action.
Device-specific layouts: Device-specific layouts refer to the design approach in data visualization that tailors visual elements and layouts to suit particular devices, such as desktops, tablets, and smartphones. This customization enhances the user experience by ensuring that visualizations and dashboards are optimized for the screen size and capabilities of the device being used, making the information more accessible and engaging for users.
Dual-axis chart: A dual-axis chart is a visualization technique that allows users to display two different data sets on the same chart, using two separate y-axes. This approach is useful when comparing different metrics that have different scales or units, enabling viewers to identify relationships or trends between the two sets of data more easily. By aligning the data sets side by side, dual-axis charts can provide a clearer context and deeper insights into the underlying patterns in the data.
Effective labeling: Effective labeling refers to the practice of using clear, concise, and informative text to enhance the understanding of visual data representations. This practice helps users quickly identify key elements within charts, graphs, and dashboards, ensuring that insights can be grasped at a glance. Good labeling not only includes titles and axis labels but also utilizes annotations and tooltips to provide context and clarity for the data presented.
Filters: Filters are tools used in data visualization that allow users to limit the data being displayed based on specific criteria. By applying filters, users can focus on relevant subsets of data, enhancing clarity and insight in visualizations and dashboards. They are essential for exploring large datasets, providing a way to isolate particular aspects of the data to derive meaningful conclusions.
Font style: Font style refers to the design and appearance of text characters in a visualization, influencing how information is presented and perceived by viewers. In visualizations and dashboards, font style encompasses various attributes such as typeface, weight, size, and style (e.g., bold, italic) that collectively affect readability and the overall aesthetic. Choosing the right font style is crucial, as it can enhance communication, create emphasis, and establish a visual hierarchy within the data being displayed.
Forecasting functions: Forecasting functions are analytical tools used to predict future data trends based on historical data. These functions utilize mathematical models and statistical techniques to generate estimates and projections, allowing users to make informed decisions based on potential future outcomes. In the context of data visualization, forecasting functions enhance the interpretability of data by providing insights into future trends and helping stakeholders visualize the implications of their choices.
Gantt chart: A Gantt chart is a type of bar chart that represents a project schedule, showing the start and finish dates of various elements of a project. It visually illustrates the tasks involved, their durations, and how they overlap, making it easier to understand project timelines and manage resources effectively. This tool has evolved significantly since its inception and is widely used in modern data visualization tools to enhance project management capabilities.
Histogram: A histogram is a graphical representation of the distribution of numerical data, using bars to show the frequency of data points within specified ranges or intervals. It helps in understanding the underlying frequency distribution, making it easier to identify patterns such as skewness, modality, and outliers in a dataset.
Level of Detail Expressions: Level of Detail Expressions (LOD Expressions) are powerful features in Tableau that allow users to control the granularity of their data visualizations. They enable the creation of complex calculations at different levels of detail, which means you can calculate values across various dimensions without changing the view. This flexibility is crucial for creating insightful visualizations and dashboards, as it allows for more precise analysis based on specific data contexts.
Line chart: A line chart is a type of data visualization that displays information as a series of points connected by straight lines. It is particularly useful for showing trends over time, as it allows viewers to easily observe changes in data values across different intervals. Line charts can represent multiple datasets simultaneously, making them ideal for comparing different categories or variables within the same time frame.
Parameters: Parameters are dynamic values that allow users to control and filter data visualizations in interactive dashboards. They enable viewers to customize their experience by selecting specific values or options, which then modify the underlying data and visual outputs accordingly. This feature enhances interactivity and engagement, making visualizations more tailored to individual needs.
Presentation design: Presentation design refers to the process of creating visually engaging and effective presentations that communicate information clearly and effectively. This includes the use of layout, color schemes, typography, and graphics to enhance understanding and retention of the content being presented. Good presentation design ensures that the audience can easily follow along and grasp the key points, making it essential in data visualization, particularly in tools like Tableau.
Scatter plot: A scatter plot is a type of data visualization that uses dots to represent the values of two different variables on a Cartesian plane. This graphical representation helps to identify potential relationships, trends, or patterns between the variables, making it a crucial tool in data analysis.
Show me: 'Show me' refers to the visual representation of data, emphasizing the importance of clear and effective visuals in conveying complex information. It underlines the need for visuals to tell a story, making it easier for users to understand and engage with the data, enhancing decision-making processes and insights derived from analytics. This concept is critical in crafting visualizations and dashboards that effectively communicate key findings and trends to an audience, as well as establishing a strong connection between data sources and visual outputs.
Table calculations: Table calculations are computations applied to the values in a visualization, allowing users to derive insights and perform analytics directly on their data within tools like Tableau. These calculations are executed after the data has been aggregated, providing dynamic results based on the underlying dimensions and measures. They empower users to create sophisticated metrics, such as running totals, percentages, and rankings, enhancing both visualizations and dashboards by adding layers of information and interactivity.
Target audience: A target audience refers to a specific group of people that a particular message, product, or piece of content is aimed at. Understanding the target audience is crucial for effective communication and engagement, as it shapes the design choices, messaging, and overall approach to presenting data visually. By identifying the characteristics and preferences of the target audience, creators can tailor their visualizations and dashboards to meet the needs and expectations of those who will consume the information.
Trend lines: Trend lines are graphical representations used to illustrate the general direction or pattern of data points in a scatter plot or time series. They help to reveal relationships between variables by showing the trend in the data, whether it is increasing, decreasing, or stable over time. By fitting a line to the data points, trend lines facilitate a better understanding of underlying patterns and can aid in predictions.
White space: White space refers to the empty areas in a design or layout that are not occupied by text, images, or other visual elements. It plays a crucial role in creating balance and harmony, making it easier for viewers to focus on key information while also enhancing overall readability and aesthetic appeal.