Charts can really help you to see the patterns in data. A sea of numbers takes form and trends become visible.

Charts provide a tool for analysis and communication. Rendering data in chart form enables you to find answers to questions and recognize changes in patterns and trends. Having selected meaningful data, these findings can be presented to an audience in a chart. If the chart is carefully constructed, it can be easily read and interpreted.

Anaplan has a range of chart types to choose from, but how do you decide which chart type is best for your requirements? There’s no doubt, choosing the right chart to effectively display those patterns and trends involves careful thought — and some trial and error.

When choosing a chart type, there are a number of things to consider:

  • The data you're using.
  • The different types of analysis.
  • Is a table an appropriate way of presenting your data?
  • Is a chart a better option?
  • Experimenting with different chart types to find the best for your requirements.
  • Which data to present.
  • Charts and dashboards.

The Quick Reference helps you choose a chart type, based on the questions you want to answer. You can also see the details of the structure and uses of individual chart types.

Data falls into one of two categories:

  • Quantitative, meaning it can be measured in some way.
  • Categorical, identifying what's being measured.

The relationships between these two types of data highlight the messages requiring the attention of your audience. A change in the pattern or size of a relationship is an indicator of a potential opportunity or problem to be addressed.

The independent variable, the variable being manipulated, is usually represented on the X-axis. The dependent variable, the variable being measured, is usually represented on the Y-axis. In combination charts, there may be two Y-axes: one on the right and one on the left.

When analyzing data, the analysis usually consists of a comparison, transition, relationship, or examination of the composition of data.

AIllustratesFor example
ComparisonThe highs and lows of the dataset, often across a time period.How are sales, compared to the same time last year? How is area A’s performance in comparison to the rest of the organization?


Changes in data over a period of time, how the current situation was reached.The ups and downs in the sales that contributed to total revenue or changes in the value of raw materials and how they affected retail price.
CompositionThe constituent parts of a data value.The values contributing to the retail price of an item or sales figures for each of the areas comprising a region.
RelationshipThe correlation between two or more variables.Predicting the sales for next year, based on the sales of the last two years; or whether a marketing program has had a positive impact on sales.

These high-level analyses, often composed of large data sets, are usually best represented as charts. Such volumes of data would be difficult to analyze as text in tables.

The first thing to consider is, would data be best represented in a table? Because they are composed of text and numbers, tables are very familiar and people instinctively know how to read them:

  • Tables show data,
  • It’s easy to find individual values in a table,
  • It’s easy to compare values in a table,
  • They provide a precise level of detail,
  • They can contain more than one unit of measure, and
  • Values in tables can be at a detailed and/or summary level.

Note: If the audience needs a tool for looking up values, a table is the best option. If they want to see patterns and trends, a chart is a better option.

To establish whether a chart would display your data more effectively, think about how charts:

  • Show interpretation.
  • Paint a picture, displaying the shape of the data.
  • Don’t require many words, the shapes and colors tell most of the story.
  • Can compare entire sets of data.
  • Reveal trends, outliers, changes, differences, similarities, and exceptions.

Thinking about what you want to achieve and the message you want to convey makes choosing a chart much easier. You should:

  • Focus on the questions you want to answer.
  • Decide the categories and measures before creating the chart.
  • Consider how you want to visualize the items of interest. Are you looking for outliers in the data, variations within categories, causes of issues or opportunities?

Your aim is to show data that's new, meaningful to the audience, and which moves them to respond in some way. Trying your data in different chart types will quickly highlight the format that best conveys your message. Your first chart choice may not be the best choice; you might find that:

  • Changing a pie chart to a column chart makes it much easier to compare values that are close in size. We find it easier to compare the length of bar than the size of portions of a pie.
  • Rather than presenting two values to be compared. For example budget and actual, you can give the audience a percentage difference or present a line chart showing actual as a deviation from budget. Audiences appreciate shortcuts to information.
  • A line chart makes a trend more obvious than a stacked area chart because stacked area charts don't provide an easily-read baseline for multiple categories.
  • Changing a column chart to a line chart can imply a sense of continuity and make the flow of time clearer. The line carries the eye across the chart, whereas chart columns cause the eye to jump around.

Ensure that you know:

  • Exactly what the audience want or need to know. Conveying a message plainly to an audience makes it clear what their response should be.
  • Which of the messages you recognize in the data you want to illustrate. It’s unlikely you can put all the messages in a single chart.

To ensure they are easy to interpret, keep charts simple with minimum necessary information. If the message has been identified and communicated clearly, the audience will be interested. The mechanism's simplicity will not matter.

Don’t overload the chart with information the audience already knows, or that's superfluous to the message. Less cognitive effort is required to decode simple charts. Don't annoy your users by making charts complicated.

If the message is complex, create a series of simple charts rather than a complicated one. Create a separate chart for each category, using the same chart type for them all, and keep axes measurements the same. Arrange charts alongside each other so your audience can quickly read across them and understand the message.

To help convey the message:

  • Oganize the data. Group it, prioritize it, sequence, or rank it.
  • When ranking categories, think about natural category order. Does it make sense to rank them in terms of geography, importance, value, etc.?
  • Emphasize interesting data points. Eliminate anything that doesn’t support the message.
  • Sort data in ascending or descending order before creating a chart. This makes comparison easier.

Following conventions helps your audience quickly comprehend the message:

  • Time is generally displayed on the horizontal (X) axis. It should run from left to right and all time periods should be displayed, even if they contain no data.
  • Provide axes labels, units of measure and, if there is the potential for misunderstanding, a legend to describe any categories or codes.
  • Print your chart in greyscale to ensure differentiation in color and support for color-blindness, as well as saving on printer ink.
  • Don’t alter axes numerically: always start at zero. Don't physically stretch or expand an axis: the resulting chart will be distorted and the data could be misleading.

Dashboards can be used to display simultaneous views of a dataset, perhaps in different chart types, to convey messages that are meaningful to the audience. Dashboards also offer filtering and/or conditional formatting to help highlight any emerging patterns, issues, or opportunities.

Dashboards can be tailored to present the same dataset, in different formats, to a range of audiences, based on their requirements. You can choose summary level data using Hide, to create one kind of chart for a particular audience, but then use Show to create a more detailed chart for a different audience.

This quick reference provides an overview of which chart type is suitable for answering different questions about your data. Click the ♦ symbol to jump to more information about the particular chart type.


Ranking DeviationTime DistributionCorrelationPart to whole Geospatial
Question typeLarger/Smaller Greater than
Less than
Equal to
Plus/Minus X
Different to
Relative to
Increases with
Changes with
Varies with
Relates to
Is caused by
Is affected by
Ratio is …
Constituent parts
% rate of total
%age rate of total
Account for X %

Stacked columnYes

Stack % columnYes

Stacked barYes

Stacked % barYes





Each chart type has characteristics that make them the best option for conveying the message contained in particular types of data. This table describes those characteristics in detail.

The information here covers the format and the use of each chart type. Select the chart name for instructions on how to create that type of chart.

Used for

Showing data changes over a period of time or for illustrating comparisons between items. For example, comparing numerical values taken on different dates or under different conditions, such as monthly sales of a product.

Grouped column charts are a way of showing information about different sub-groups of the main categories, such as monthly sales for several different products.

Stacked column charts compare the contribution of values to a total across categories. They display the values of the sub-groups, in a single column. The overall size of the column shows the total size of the category with the sub-groups, represented by different colors, indicating their relative contribution to the whole.

Stacked percentage column charts compare distributions within categories, and, at the same time, display the differences between categories.

Answers the question

How does A differ from B?

What is the ratio of X to Y?

Which product is selling most units?

Who is selling most units?

What is the composition of our website traffic?


Data is displayed in vertical bars with one axis representing the categories, and the other the unit of measurement.

The quantitative measure should always begin at zero because reading the chart relies on comparing the length of columns. If the base value is not zero, the column length is distorted and comparing values is more difficult.

Column charts can be created in standard, grouped, stacked, or stacked percentage formats.

A stacked percentage chart is exactly the same as a stacked column chart but the figures are represented as percentages totaling 100%. This enables you to examine the proportions between values in each grouping, as well as each grouping's total.

Time is usually displayed along the X-axis and categories on the Y-axis, making a column chart the best option when categories are to be displayed over time.


Easy to read.

Easy to compare single values, if the number of categories isn’t large.

Categories can be reordered to emphasize results.

Negative values are clearly represented.

Becomes more difficult to read if there's a large number of categories.

Have less space for category axis labels to display. If you have longer category labels, consider using a bar chart.

Used for

Summarizing and displaying categories of data to easily compare values between different categories.

Also useful for comparing multiple series of data, providing snapshots of data at particular points in time. Popular for showing categorical information. The horizontal display provides more room for category labels.

Stacked and Stacked Percentage bar charts are useful for comparing component parts to the whole.

Use bar charts where the interest is in individual values. If the interest is directed more towards trends, use a line chart.

Categories are usually displayed along the Y-axis, making bar charts popular where time is not a factor.

They’re also very useful for ranking items. Remember to sort data before creating the chart.

Answers the question

Is A different to B?

Which salesperson sold most product?

What percentage of X does Y represent? Perhaps using a Stacked Bar Chart.

What is the average growth over the last X years?

FormatBar charts display data in horizontal bars. They are exactly the same as a column chart, but with the axes rotated. The Y-axis represents categories and the X-axis, measurement.

Easy to read labels as they follow the natural reading direction.

Categories can be sorted to emphasize results.

Negative values are clearly represented.

More difficult to read where there are a large number of categories.

Long labels on the Y-axis will push a chart towards the right of the dashboard.

Used for

Displaying several dependent variables against one independent variable such as the sales results of different regions within a particular timeframe.

Line charts enable users to easily see spikes and troughs in a continuous data set. They are particularly good for emphasizing trends over time as the rate of change is often clearer in a line chart. If your interest is in individual values, rather than trends, use bar or column charts.

Answers the question

What are the fluctuations in X?

How does A differ from Y?

Is X related to A?

Have sales increased over the last financial year?

FormatA line chart is a series of data points, connected by straight lines, on two axes. Dimensions, such as time, are displayed on the horizontal X-axis and the dependent data are displayed on the vertical Y-axis. One data set is always dependent on the other set.

Relationships are easily identified.

Can become difficult to read if too many categories are included.

Used forShowing the proportions within a single category.
Answers the question

What is Y’s share of the whole?

What is the ratio of X to Z?

What is the composition of website traffic?

FormatPie charts consist of a circle, representing a whole, divided into segments that represent the component parts of the whole. The axis for a pie chart follows the circumference of the pie.

Can provide a clear representation of proportions.

Can only represent a single category.

Can be difficult to identify quantity because of the shape of the segment.

The axis follows the circumference of the circle. As it’s usually hidden, reading values isn't possible.

The more segments there are in the pie, the harder it is to interpret.

Used for

Contribution analysis. It shows the contributory components to a larger total. Also good for tracking performance and the contributors to success over time.

The inclusion of subtotals provides a layer of information that a stacked column chart can't show.

Answers the question

How did we grow our profit?

Why do we have so much stock on hand?

What does our income statement look like?

How has our profit changed over the last five years?


A waterfall chart contains an opening value and a series of increments and decrements, represented by floating colored bars, that contribute to a closing value. Each floating bar begins where the last bar ends.

Generally, green represents positive values, red for negative, and blue is used for totals.

The data is always displayed in the same order as the source data and can cross the zero axis.

Subtotals, relative to the preceding value, can be displayed in a chart as checkpoints. This makes waterfall charts really useful for illustrating profit and loss.


Provides a clearer view of the components of a total than a bar chart.

Legibility degrades as the number of components increase.

Used forIllustrating events over time. The progress of a project, advertising campaign, acquisition process.
Answers the question

When did we …?

At what point did …?

How do we illustrate the stages in the website design project?

FormatGiven a date range, a timeline chart shows events in chronological order, in whatever unit of time the data was recorded: week, month, year, quarter.
Advantages/DisadvantagesLimited use. Timeline charts have little analytical use as they simply illustrate events across time.
Used forPositioning data in a geographic context to quickly identify performance by location.
Answers the question

How does this area differ from that area?

How many X do we have in Z, or in W?

What is our worst performing area?

FormatDisplays a map of the relevant area. Data values are displayed in pop-ups when rolling over areas on the map.

Easy to understand the distribution of the organization’s presence across a region.

Can represent a large number of data items compared to other chart types.

Used for

llustrating relationships and correlations between one or more measures in a single visualization by combining the features of two different chart types.

Three data sets are required. The first one is based on a continuous set of data, and the other two are grouped by category, each having a different quantitative scale on the Y-axis. A combination chart illustrates any correlation between the three data sets.

Combination charts can also be used to create a Pareto Analysis.

Answers the question

What's the relationship between monthly projected sales and actual sales?

Compare total sales against deals won over a series of quarters.

Compare sales over a time period for different products.


A combination, or multi-axis, chart displays data using two Y-axes and one shared X-axis.

The two charts share a common category axis and may have more than one quantitative axis (Y-axis), each representing a different scale of values.


Combination charts are good for showing correlations between different types of data.

There's a broad range of chart types and combinations to choose from (for example, column, line, dot, area) to ensure the most appropriate form for the message.

Single-axis dot or area charts can be created.

Variables with very different scales can be compared.

They can be complex and difficult to read. It’s easy to overload a combination chart with data.

Points of intersection are meaningless because the scales of measurement for the Y-axes are different. Users are not always quick to comprehend this.

Stacked area charts are not good tools for comparing multiple categories. The lack of a consistent baseline for all but the bottom area makes interpretation difficult.

Used for

Visualizing the progressive reduction of data as it passes through the phases of a process. Data in each of these phases is represented as portions of the whole.

Funnel charts are most often used for illustrating the size of some quantitative measure of a sales pipeline. They can also be used to analyze the success of a process (for example, a marketing campaign, the stages in a recruitment process, order fulfillment process, registration process, or for tracking purchase patterns on websites) to identify where customers drop out of the process. They are sometimes used to identify potential problems in an organization’s operations.

Answers the question

How many sales occurred in each stage of the process?

How much Y was used in each stage of the Q?

FormatRather than being an axis chart, funnel charts display the relative lengths of the component values.

A simple visualization of the reduction process.

The data may not create a funnel shape. It relies on a progressive reduction of values in the data. If one value is larger than the preceding value, the shape will be distorted. In this situation, a bar chart might be a better option.