Visual Language & Chart Types

Data Visualization

What Is a Chart?

A chart is a visual representation of data that lets you observe patterns, trends and relationships between variables. By presenting information graphically, interpretation and decision-making become easier. Charts are a powerful tool for communicating information clearly and effectively, since they let you visualize information in a structured, understandable way.

Today, in a world where 90% of data was created in the last two years, and that amount is expected to keep growing exponentially, data visualization is an indispensable tool in the professional world. Being able to read and interpret charts lets you make informed, data-based decisions. However, it's not enough to just look at a chart; it's crucial to understand what it's telling you and how to use that information effectively.

To do that, you need to understand the different types of charts available and when to choose each one, since each serves a specific purpose or function. By selecting and analyzing the right chart, you can extract valuable information from the data.

This is, without a doubt, the most important aspect, though other factors also play a role, like placement and size on the dashboard, hierarchy, titles, labels and, of course, colors. These elements are crucial for efficient reading and for directing the viewer's attention toward the most relevant data.

Choosing the right chart is essential for every metric or KPI you want to analyze. Each type of chart lets you visualize data in a specific way, so it's essential to choose the right chart for each situation.

Today, there are numerous data visualization tools that make it quick and simple to create charts. The most popular are Tableau, Power BI, Google Data Studio, D3.js, Qlik Sense, among others. These tools let you import data from different sources, create dashboards with interactive charts, and customize the visualization to fit user needs. All of these come with a default set of charts that can be customized and adapted to each project's needs. They also let you import other charts externally, like those from D3.js, which are very popular for their versatility and customization. Let's look at the different types of charts and when it's appropriate to use each one.

Chart Groups

As we saw, there are different groups of charts designed to represent a wide variety of data types, whether to show comparisons, distributions, correlations, classifications or rankings, changes over time, magnitudes and flows. That said, the same chart can belong to more than one group, since its function can vary depending on the context in which it's used. At the same time, the same types of data can be represented by different types of charts, depending on the information you want to highlight, or the audience the visualization is aimed at. For this article, we're basing our classification on one created by the Financial Times Visual Vocabulary, which divides charts into 9 main groups. There are certainly more, but these are the most common and widely used in the professional world.

Used to highlight numerical variations from a fixed reference point. Usually, the reference point is zero, but it can also be a target or a long-term average. This type of chart is useful for showing sentiment (positive, neutral or negative). There are different types of deviation charts, each with a specific function:

• Diverging Bar: A standard bar chart that can handle both negative and positive magnitude values. For example, in financial analysis, a diverging bar chart can be used to compare a company's income and expenses across different quarters, visually highlighting periods of surplus and deficit. This approach lets you quickly identify the months with the greatest financial impact; it could also represent the impact of a marketing campaign on sales, or temperature variation across different cities.
• Diverging Stacked Bar: Used to present survey results or opinion data, between neutral, agree or disagree. It's useful for showing the distribution of opinions in a survey, employee perception of a topic, or customer satisfaction with a product or service.
• Spine Chart: Splits a single value into two contrasting components (e.g., male/female). This type of visualization can be useful for showing gender distribution at a company, the proportion of students by grade in a school, or the number of visitors by country on a website.
• Surplus/Deficit Filled Line: Illustrates individual or group responses on a continuous line divided into zones. It can look like an area chart, but in this case, the areas are split into two parts, making it easy to visualize positive and negative values. It's useful for showing the evolution of a company's income and expenses, temperature variation across different seasons, or the distribution of opinions in a survey.

These charts are used when you want to show the relationship between two or more variables, to identify patterns, trends and correlations in the data. As with each chart group, there are different types of correlation charts, and choosing the right one depends on the data you want to represent and the goal of the visualization.

• Scatterplot: The most commonly used resource for showing the relationship between two continuous variables, each with its own axis. It can be used to visualize the relationship between age and income among employees, compare the height and weight of athletes, or analyze the correlation between temperature and ice cream sales, for example.
• Column + Line Timeline: Used to show the relationship between two variables, one of quantity (columns) and one of ratio (timeline). It's useful for comparing changes in data over time, highlighting differences in quantities and rates. It's commonly used to visualize monthly sales (columns) and growth percentage (line), a factory's annual output (columns) and defect rate (line), or the number of visitors to a website (columns) and conversion rate (line).
• Connected Scatterplot: Shows movement in a scatterplot when the points are connected in sequence. Unlike a standard scatterplot, this chart connects data points in order, letting you visualize how the two variables relate over time or in a specific sequence. It can be used to visualize the relationship between sales growth and ad spend month by month, show how temperature and humidity change during the day at different hours, or track production progress against cost over a year, among others.
• Bubble: Adds a third variable to the scatterplot, representing data with bubbles of different sizes. This chart is useful for showing the relationship between three variables, using bubble size to represent the magnitude of the third variable. For example, you can visualize the relationship between stock price, transaction volume and a company's market cap, or compare the age, weight and height of a group of people.
• XY Heat Map: Uses shading or color to show the intensity of the relationship between two variables on a grid. Applied to visualize the relationship between two continuous variables in a scatterplot, using colors to highlight point density. It's useful for identifying patterns and trends in large datasets, like the relationship between temperature and humidity across different regions, or the correlation between time on site and conversion rate on a website.

These charts are very easy to implement and very useful for showing rankings, since they let you easily visualize each item's position in an ordered list. For example, they can be used to show the countries with the largest population, the most-visited cities, or the best-selling products.

• Ordered Bars: Horizontal bars make it easier to compare values between individual categories when the values are ordered. It's a widely used chart for showing rankings, since it lets you easily visualize each item's position in an ordered list. For example, it can be used to show the countries with the largest population, the most-visited cities, or the best-selling products.
• Ordered Columns: Just like the bar chart, the column chart is also widely used to compare individual categories when values are ordered, and it's particularly effective for showing change over time. For example, it can be used to show product sales across different months, the variation in unemployment rate across different years, or the evolution of a music genre's popularity across decades, to name a few examples.
• Ordered Proportional Symbol: Shows quantities through the relative size of symbols. Recommended when there's variation between values and seeing minor differences between them isn't as important. This chart is useful for visualizing rankings and comparing significant magnitudes, using symbol size to represent the magnitude of values. For example, it can be used to show city population on a map, represent sales volume by region, or visualize the number of disease cases across different geographic areas.
• Dot Strip Plot: Points on an ordered strip are an efficient method for showing ranges across several categories. While similar to a bar chart, this chart is more effective for showing all the data in a table, since it highlights individual values. It's useful for visualizing rankings and comparing significant magnitudes, using points on an ordered strip to represent values. For example, it can be used to show country rankings by population, represent the number of graduates by university, or visualize income variation by economic sector.
• Slope Chart: Shows the change between two points using slanted lines to connect the values. It's useful for visualizing changes in data between 2 or 3 key points without losing important information. This chart lets you visually compare differences between starting and ending values, highlighting significant trends and changes. Some common uses include comparing a company's income across two different years, evaluating the change in a school's graduation rate between two periods, or seeing the variation in customer satisfaction before and after a campaign, among others.
• Lollipop Chart: Similar to a bar chart, it uses a line with a dot at the end to represent individual values, combining elements of bar charts and dot charts to highlight comparisons; it's useful for showing rankings and comparing significant magnitudes, using points on an ordered strip to represent values. For example, it can be used to see a ranking of a city's most expensive neighborhoods, compare visitor numbers across different museums, or visualize the best-selling car model in a given year.
• Bump Chart: Effective for showing changes in different rankings across multiple dates. This chart is a bit more complex to read than the previous ones, since it shows the evolution of several rankings over time, overlaid at once. A good use of this resource would be to use one color for a global group of items and another eye-catching color to highlight the one you want to focus on. It's effective for visualizing income variation among companies in different sectors, the evolution of music genre popularity on playlists, or team standings in a sports league, among others.

This type of chart highlights a series of values within a dataset and shows how often they occur, showing how variables are distributed over time. This helps identify outliers and trends.

• Histogram: Similar to a column chart, but the histogram's columns represent data ranges called "bins" and sit nearly touching to highlight the shape of the data's distribution. This lets you identify groups of data that repeat most frequently. It's the most common way to show statistical distributions. A typical use is measuring age distribution in a population.
• Dot Plot: A simple way to visualize changes in the minimum and maximum range of data across multiple categories. This chart is useful for identifying variations and comparing categories clearly. An example use would be showing the distribution of student grades across different subjects.
• Dot Strip Plot: This chart is efficient for showing individual values in a distribution, since the points are laid out on a continuous line. However, it can become hard to interpret when too many points share the same value. It's ideal for visualizing response frequency in surveys with discrete options.
• Barcode Plot: Similar to the dot strip plot, this chart shows all the data in a table and is particularly useful for highlighting individual values. It works well in situations where you need to represent the presence or absence of data, like DNA sequence analysis or the frequency of events on a timeline.
• Boxplot: This chart is valuable for visualizing several distributions, since it shows the median (the central value), the range (the difference between the minimum and maximum value) and other key elements like quartiles and outliers. It's commonly used in statistical analysis to compare the spread and symmetry of data, for example, when comparing salaries across different sectors.
• Violin Plot: Similar to the boxplot, but with the added benefit of showing data density. This makes it more effective for visualizing complex distributions that can't be summarized with a simple average. It's used to represent the distribution of exam scores with different levels of detail, letting you see the variability of the data.
• Population Pyramid: This chart shows population distribution by sex and age, using inverted histograms placed side by side to represent both genders. It's a standard tool in demographics for analyzing a country or region's population structure, making it easier to study aging or population growth trends.
• Cumulative Curve: Represents the cumulative frequency of a variable as a specific measure increases along the x-axis. It's useful for visualizing how data is distributed across a range and understanding what percentage falls below a given value. An example application is showing the accumulation of sales over a period to identify how quickly certain targets are reached.
• Frequency Polygons: These charts are useful for showing multiple data distributions and are similar to line charts, but they better highlight the shape of the distributions. It's recommended to use them with a maximum of 3 or 4 datasets to keep them readable. An example use is comparing temperature distribution across different cities over a year.
• Beeswarm Chart: This scatterplot arranges points horizontally to avoid overlap, creating a visual layout reminiscent of a swarm of bees. It's especially useful for showing the distribution of individual data points and lets you observe both density and variation without points hiding each other. A common example is visualizing individual scores of participants in a competition, highlighting differences and the concentration of results. It's also used to show the spread of survey responses or to analyze the distribution of physical properties of samples in scientific studies.

This group is used to emphasize trends changing over time. It can cover short movements or extended series spanning years. Choosing the right period is key to providing context.

• Line Chart: The standard way to show a changing time series, where the x-axis represents time and the y-axis represents the variable's values. If the data is irregular, markers can be used to represent individual data points and highlight variations. Some example uses include tracking a product's monthly sales, recording daily temperature over a month, tracking stock price over a year, or monitoring the number of website visitors per day.
• Column Chart: Uses vertical bars to represent data and is especially useful for showing change over time. Each column represents a point in time (like months, quarters or years), making it easy to visualize trends and comparisons over a given period. It's ideal for highlighting increases, decreases and patterns in a time series.
• Column and Line Timeline: An effective way to show the relationship over time between a quantity (represented by columns) and a rate or percentage (represented by a line). This chart makes it easy to compare changes in both metrics over the same period. It can be used for monthly sales (columns) and growth percentage (line), a factory's annual output (columns) and defect rate (line), or the number of website visitors (columns) and conversion percentage (line).
• Slope: Ideal for showing changes in data between 2 or 3 key points without losing important information. This chart lets you visually compare differences between starting and ending values, highlighting significant trends and changes. Some common uses include comparing a company's income across two different years, evaluating the change in a school's graduation rate between two periods, or seeing the variation in customer satisfaction before and after a campaign, among others.
• Area Chart: Visualizes trends by filling the area between the x-axis and the data line, showing changes in a series' total. However, it's important to use it carefully, since it can make it harder to see changes in individual components.
• Candlestick Chart: Generally focuses on daily activity, showing the opening, closing, high and low points for each day. It's commonly used in financial analysis to visualize the behavior of stocks and other assets over short time periods.
• Fan Chart: Used to show uncertainty in future projections, with areas that expand as predictions move forward in time, indicating a range of possible outcomes. It's used to visualize sales forecasts, population growth estimates, revenue projections, among others.
• Connected Scatterplot: An effective way to show change in data across two variables when there's a clear progression pattern. This chart connects data points in order, letting you visualize how the two variables relate over time or in a specific sequence. It can be used to visualize the relationship between sales growth and ad spend month by month, show how temperature and humidity change during the day at different hours, or track production progress against cost over a year, to name a few examples.
• Calendar Heatmap: An excellent way to show temporal patterns (daily, weekly, monthly), though it sacrifices precision in the amount of data. This chart uses colors to represent the intensity of values on a calendar, making it easier to identify trends and patterns in specific periods. For example, visualizing daily sales frequency over a year, showing user activity in an app for each day of the month, or highlighting the highest-traffic days on a website, among others.
• Priestley Timeline: Ideal for showing data where date and duration are key elements. This chart lets you visualize events on a timeline, highlighting their start, end and duration, making it easier to understand how they unfold over time and their relationship to other events. Useful for showing the start and duration of projects over a year, representing important historical periods in an educational context, or visualizing the run time of marketing campaigns and their overlap, among others.
• Circle Timeline: Useful for showing discrete values of different sizes across multiple categories. It lets you visualize events or data organized in a circular format, highlighting differences and patterns between categories. Representing earthquakes by continent and magnitude, showing major sporting events by region and attendance, or visualizing product launches by year and their impact on sales are some examples of use.
• Vertical Timeline: Presents time on the Y axis and is ideal for showing detailed time series. It works especially well for scrolling on mobile devices, making it easier to visualize chronological events or data. Some applications include showing the development of a story of events with important dates, visualizing a project's progress with key milestones, or representing a timeline of social media posts.
• Seismogram: An alternative to the circle timeline for showing data series with large variations. Commonly used in earthquake analysis, this chart lets you visualize drastic changes and fluctuations over a time period, clearly highlighting peaks and valleys. The most common applications are representing an earthquake's intensity over time, showing variations in ambient noise levels during a day, or visualizing spikes in server or network activity.
• Streamgraph: A type of area chart used when it's more important to observe changes in proportions over time than individual values. This chart shows how different categories contribute to the total in a fluid way, creating a visual effect that highlights trends and the evolution of proportions. Some applications could be visualizing income distribution by category over a year, visualizing the popularity of music genres across decades, showing how different products' revenue contributes to total sales over time, or seeing several companies' market share in a sector over the years.

Useful for visualizing size comparisons. These comparisons can be relative or absolute and generally show variables that can be counted, like people, units or dollars.

• Column Chart: The standard way to compare the size of different items, ideal for representing magnitudes. It should always start at 0 on the axis to ensure an accurate representation of differences. Examples: comparing different companies' annual revenue, visualizing product sales across various categories, or showing the number of students at different schools.
• Bar Chart: Similar to the column chart, but oriented horizontally. It's ideal for comparing data when it isn't a time series and category labels have long names, since it allows for better readability. Examples: comparing scores across different teams, showing the number of products sold by region, or visualizing survey distributions with lengthy categories.
• Stacked Column Chart: Similar to the standard column chart, but it lets you represent multiple data series stacked on top of each other in each column, showing how they contribute to the total. It's useful for comparing parts of a whole across several categories. However, it can be hard to read if there are many series. Examples: showing the composition of product sales by region, visualizing the breakdown of spending across different departments, or representing total revenue broken down by source.
• Stacked Bar Chart: Similar to the standard bar chart, but it lets you represent multiple data series stacked on top of each other in each bar, showing how they contribute to the total. It's useful for comparing parts of a whole across several categories and is ideal when you have long category labels. However, it can be complicated to read if there are many series. Examples: showing population composition by age and gender across different regions, visualizing sales distribution by product type across various stores, or representing cost breakdowns across different projects.
• Marimekko Chart: An effective way to show both the size and proportion of data at the same time. This chart combines elements of stacked bar and area charts, letting you visualize the contribution of different categories and subcategories in a single chart. It's ideal for representing market share and resource distribution, but can become confusing with very complex data. Examples: showing market share by region and product category, visualizing spending distribution by department and project, or representing revenue breakdowns by customer and product.
• Proportional Symbol Chart: Used when there are large variations between values and/or when fine differences between data points aren't that important. This chart represents data using symbols whose size is proportional to the value they represent, making it easier to visually compare significant magnitudes. Examples: showing city population on a map, representing sales volume by region, or visualizing the number of disease cases across different geographic areas.
• Pictogram Chart: An excellent solution in certain cases, since it uses repeated images or symbols to represent quantities visually. It's important to use it only with whole numbers, since dividing a symbol to represent decimals can be confusing. Examples: showing the number of employees at a company, representing the quantity of products sold, or visualizing voter turnout in elections.
• Lollipop Chart: This chart highlights data values more than standard bar or column charts, by using a line with a dot at the end to represent each value. Although it's preferable for the axis to start at zero, it isn't mandatory. It's useful for making clear comparisons and emphasizing individual data points. Examples: comparing student grades across different subjects, visualizing employee performance in evaluations, or showing product sales by quarter.
• Radar Chart: A space-efficient way to show the values of multiple variables in a circular format. It's important to organize the variables logically so the reader can easily interpret the data. This type of chart is useful for comparing the performance or characteristics of different items across several dimensions. Examples: evaluating different candidates' skills in an interview, comparing the features of similar products, or visualizing sports teams' performance across different metrics.
• Parallel Coordinates Chart: An alternative to radar charts that lets you visualize multiple variables in a linear format. The arrangement of the variables is crucial for correct interpretation, and it's useful to highlight values to make comparison easier. This chart is ideal for analyzing complex relationships between several data dimensions. Examples: comparing the performance of different vehicle models across various features (like fuel consumption, speed, price), analyzing financial data for several companies across different indicators, or visualizing student performance across several subjects.
• Bullet Chart: Ideal for showing a measurement in the context of a target or performance range. This chart lets you quickly see whether a value reaches, exceeds or falls short of an established goal, and is commonly used in performance reports or KPI analysis. Examples: evaluating sales progress against a target, comparing a project's performance to its goal, or showing a factory's production level relative to its expected standard.
• Grouped Symbol Chart: An alternative to bar or column charts, useful when you need to count data or highlight individual items. This type of chart represents data using repeated symbols, grouped in a way that makes comparison and visual understanding easier. It's ideal for showing quantities clearly and comparatively. Examples: visualizing the number of products sold across different stores, comparing the number of employees across different departments, or showing the number of events by category over a period.

This type of chart shows how an entity is divided into the elements that make it up, like budgets or election results.

• Stacked Column/Bar Chart: A simple way to show part-to-whole relationships, though it can be hard to read when there are more than a few components. Examples: representing income distribution by source in a given year, showing several brands' market share in an industry, or visualizing different products' contribution to total sales.
• Marimekko Chart: A good way to show both the size and proportion of data at the same time, as long as it isn't too complex. This chart combines elements of bar and area charts to represent categories and subcategories. Examples: showing market share by region and product category, visualizing cost distribution across projects, or representing revenue breakdowns by customer.
• Pie Chart: A common way to show part-to-whole data, though it can be hard to precisely compare the size of the segments. Examples: visualizing budget distribution across different areas, showing sales share by product, or representing the proportion of responses in a survey.
• Donut Chart: Similar to the pie chart, but with an empty center that lets you include more information about the data (for example, a total). Examples: representing revenue distribution by product category, showing market share while highlighting the total in the center, or visualizing a budget's composition with a central legend.
• Treemap: Used to represent hierarchical part-to-whole relationships, but can be hard to read when there are many small segments. Examples: visualizing a company's structure by department, showing sales distribution by product category and subcategory, or representing an investment portfolio's composition.
• Voronoi Diagram: Converts points into areas where any point within an area is closer to that area's central point than to any other. This type of chart is particularly useful in service coverage analysis, like the distribution of cell towers across a region. It lets you see which areas are better covered and which have coverage gaps. It's also applicable in market influence studies, where you can visualize the zones of greatest influence for a store or point of sale; another application could be representing the distribution of gas stations across a city.
• Arc Chart: A semicircle commonly used to visualize a parliament's composition by number of seats. Examples: representing seat distribution by political party in a parliament, visualizing vote share on a council, or showing the composition of a steering committee.
• Gridplot: Good for showing percentage information, works best with whole numbers and in a format of multiple small representations. Examples: visualizing goal completion rate in projects, showing response distribution in a survey, or representing the percentage of sales achieved by region.
• Venn Diagram: Generally used for schematic representations of sets and their intersections. Examples: showing the intersection of shared features between products, representing overlapping skills within a work team, or visualizing groups of customers with common interests.
• Waterfall Chart: Useful for showing part-to-whole relationships where some components can be negative. This chart helps visualize how each individual contribution affects a cumulative total value. Examples: showing the impact of income and expenses on net financial results, representing changes in inventory stock over a period, or analyzing how different areas contribute to a company's net profit.

This type of chart is used when precise locations or geographic patterns in the data matter more to the reader than anything else.

• Choropleth Map: A standard approach for representing data on a map, showing rates or proportions through color rather than totals, using an appropriate geographic base.
• Bubble Map / Proportional Symbol: Shows data using proportionally sized bubbles on a map, ideal for representing totals or magnitudes. Small differences can be visually hard to distinguish.
• Flow Map: A Flow Map shows movement across a geographic area, like wind direction, animal migration routes, human displacement, or trade between regions. It's useful for visualizing clear movement patterns.
• Contour Map: A Contour Map shows areas of equal value on a map, like elevation levels, temperature or atmospheric pressure. It can also use color schemes to represent deviations, like positive and negative values, and is useful in climate maps and geological studies to show constant heights or depths.
• Equalised Cartogram: An Equalised Cartogram turns each unit on the map into a regular, uniformly sized shape, which is useful for representing voting districts or equitable data distribution. For example, on an electoral map, each state or district can be shown as a square or circle of the same size to reflect its equality in number of votes or seats, regardless of its actual geographic size.
• Scaled Cartogram: Distorts a map by adjusting the size of each area according to a specific value, like population or income.
• Dot Density Map: Used to show the location of individual events or places. It's ideal for representing, for example, population distribution in a city or crime concentration in an area. It's important to note relevant patterns the reader should notice, like areas of high or low density.
• Heat Map: Shows data on a grid using a color intensity scale, similar to a choropleth map, but not limited to political or administrative units. It's common in visualizing densities, like pedestrian traffic in a city or activity on a website.

Shows the movement of variables or elements within a system or process, which is useful for visualizing sequences, paths or changes in a flow.

• Sankey Diagram: Shows changes in flows from one condition to at least one other, ideal for tracking the outcome of a complex process. Designed to visualize the sequence of data in a flow process, typically in budgets, and can include both positive and negative components. It's a complex but powerful diagram that can illustrate bidirectional flows (and the "net winner") in a matrix. It's used to show the strength and interconnection of different types of relationships.
• Waterfall Chart: The Waterfall Chart is ideal for showing how an initial value is affected by a series of increases and decreases over a given period until it reaches a final value.
• Chord Diagram: A complex but powerful diagram that illustrates connections between categories through bidirectional flows in a matrix, highlighting the "net winner."
• Network Chart: Lets you see complex relationships and the interconnection between various nodes in a system.

Choosing the right chart for each metric or KPI you want to analyze is essential for the visualization to be effective and serve its purpose. That's why it's important to keep each chart type's functionality in mind and choose the one that best fits the data you want to represent.

It's also important to consider the audience; for non-technical audiences, it's advisable to use simpler, easy-to-interpret charts, like bar, line or pie charts. These charts allow for quick understanding of the data without needing exhaustive analysis. More complex charts, like network charts or Voronoi diagrams, on the other hand, should be reserved for audiences with data analysis experience and specific contexts where they're essential.

Making the right choice is a key element for effective visualization. That said, as we've seen, there are other factors that also affect how well these charts are read, like their placement, scale, hierarchies, colors, titles and legends, and the use of filters, which let the user interact with the visualization and explore the data in more detail.

The following PDF is a complete guide to the chart types and functionality we covered in this section.

Key Elements

Once you've selected the right chart for your data, there are a few key elements to look at in order to interpret it effectively. First, the title and axis labels provide an initial orientation to the chart's context. Without this information, it's easy to misinterpret what's being shown, especially if the unit of measure or the scale being used isn't known.

Scale is another aspect that requires attention. In some charts, a manipulated scale can exaggerate or minimize changes in the data. A bar chart where the vertical axis starts at a high number could make the differences between categories look smaller than they actually are. It's important, then, to verify that the scale properly reflects the data.

Colors and the legend also play a central role in interpretation. In line or area charts, colors let you distinguish between variables, while the legend helps identify what each color represents. A well-designed chart makes reading easier by presenting information in a visually organized way.

Some charts can show reference points, like average lines or target values. These elements are useful for putting data in context, indicating whether values are above or below a standard or expectation.

An interesting resource is automatically marking the highest and lowest values with eye-catching colors, so the user can quickly identify them on a chart. If filters or interactions between charts are used, this can be very useful for quickly identifying values within the range of interest.

There are numerous design resources for creating efficient reports that are easy to read and interact with, but this article focuses on chart types and their functionality.

Common Mistakes and Tips

Charts are often misinterpreted, whether due to design errors or a lack of knowledge about how to read them. This can be due to poor practices when designing a chart, or a lack of knowledge about how to interpret them.

A common example of a poor choice is using a pie chart to compare multiple categories with small differences in their proportions. This can lead to incorrect interpretation, since the segments become hard to distinguish. A bar chart would be more appropriate instead, since it makes it easier to precisely compare values. Let's look at other common mistakes and tips for avoiding them:

• Incorrect Scale: Modifying the scale can make small differences look more significant. It's essential for the scale to accurately represent the data, avoiding distortions. For example, a bar chart with a vertical axis that doesn't start at 0 can exaggerate differences between categories.
• Too Many Categories: As we mentioned, using too many categories in pie or stacked bar charts can make the visualization confusing and ineffective. Reducing the categories or using a different chart type helps keep things clear.
• Comparing Inappropriate Chart Types: Sometimes it's necessary to simultaneously analyze several related charts on a dashboard, and comparing different chart types (like a line and a pie chart) without the right context can lead to mistaken conclusions. It's important to choose compatible charts for clear comparisons.
• Using Inappropriate Colors: Colors should be chosen carefully to ensure the traceability of categories and clear understanding of the data they contain. Avoid combinations that make reading harder or that aren't accessible to people with visual impairments.
• Lack of Context: A chart without a title, legend, labels or clear axes can be hard to interpret. Providing context and additional explanations helps make the visualization more understandable.
• Too Many Visual Elements: Using too many decorative or unnecessary elements can distract the viewer and make it harder to interpret the data. Keeping the visualization simple and clear is essential for effective communication.

Interpretation Tips

• Define the Goal: Before creating or interpreting a chart, it's useful to define what story it's trying to tell. This helps you choose the right chart and focus the visualization on the relevant data. Questions like, what do I want to communicate?, what's the key message?, what action is this visualization expected to drive? are essential for guiding interpretation.
• Look for Patterns and Anomalies: Identifying patterns and detecting outliers can provide valuable insights. An upward trend in a line chart, for example, can indicate growth, while an outlier in a box plot could signal an opportunity or a problem.
• Keep the Chart Simple and Clear: Using only necessary elements avoids overloading the visualization. Every color, shape and line should have a clear purpose and align with the chart's message. They shouldn't contain elements that don't contribute to communicating what you want to show.
• Validate Conclusions: It's not advisable to rely solely on a single chart; analyzing the data behind it and comparing it with other metrics leads to a more robust interpretation. For example, if a chart shows an increase in sales, it's important to verify whether that increase is reflected in inventory data or profits.
• Interact With the Visualization: If possible, use filters or interactions to explore the data in detail. This makes it easier to identify trends, comparisons and relationships between variables. Proper use of filters is a key factor for effective interpretation. It's important to keep in mind that interactions between charts can always be enabled or disabled to make data interpretation easier, or to avoid combinations that could lead to a misreading.
• Continuous Improvement: Data visualization is an ongoing process of learning and improvement. Reviewing and updating charts based on results and feedback received helps refine the interpretation and communication of data.
• Get Feedback and Collaborate: Sharing charts with colleagues or subject-matter experts can enrich interpretation and bring different perspectives. Collaboration and feedback are essential for improving the quality of data-driven analysis and decisions.

By applying these recommendations and avoiding common mistakes, you achieve an accurate interpretation of data, enrich the analysis, and improve decisions based on visual information. In the professional world, the ability to visualize and correctly interpret data can make the difference between a successful strategy and a less effective one.