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Data-Driven Charts 101

How do you show data-driven information optimally? How do you select a chart type? The answer is identifying your message and then understanding your options.

  • Identifying your message. More often than not, the lead-in or take-away text associated with the chart will tell you which chart you need to use.

If the associated text states that you will be presenting the top 10 competitors in an industry, then ranking and volume measurements are important.

If the take-away text explains why a spike in sales occurred at a particular point in time, then volumes and trends need to be conveyed.

If the lead-in sentence prepares the audience to see data that shows a cluster of sales, then a scatter chart is needed.

Get into the habit of reading the surrounding text. This text will generally guide you into the proper chart-type selection.

  • Understanding your options. There are very few unique chart types. There are many, many ways to use these chart types; and this is often where confusion exists. Understanding the very few unique chart types paves the way for correctly selecting, formatting, and populating the chart with data.

Before we begin looking at the specific types of charts, it's important to understand some of the basics. Most charts are built on two axes, which mean that data is measured against two sets of values: the X axis is the horizontal baseline and the Y axis is the vertical baseline (regardless of whether the axis visible or not).


Broadly, data-driven charts either provide comparisons or compositions. As with most things, form is dictated by function.

Comparisons

Comparisons are, by far, the most widely used messages delivered by data-driven charts. There are seven basic types of comparisons and each will be discussed in turn.

Range


Ranges are used to show a starting point and ending point for data (and sometimes a midpoint or average) that is not grounded on a baseline, so they "float" in the chart. Each category theoretically begins and ends at a different value, thus providing a unique visual representation of data and message.

Relationship

There are a few different chart types that show relationships. Relationships are established when data is plotted for two or more categories.

  • Example A (grouped bar chart) compares two sets of data: category (Y axis), volume (X axis); and classifies items within the category (differently colored bars/legend)
  • Example B (grouped column chart) compares two sets of data: category (X axis), volume (Y axis); and classifies items within the category (differently colored bars/legend)
  • Example C compares three sets of data: timing (X axis), volume for columns (left-hand Y axis), and volume for line (right-hand Y axis); and classifies items within the column category (differently colored columns/legend)
  • Examples D, F, and G (bubble charts) compare three sets of data: value (X axis), value (Y axis), and volume (size of bubble), and Example F uses color to further classify the bubbles.
  • Examples E (scatter chart) compares two sets: value (X axis), value (Y axis); and color is used to classify the sprites.

Volume

Showing comparative volumes is common to almost all chart types; so many chart types can perform this task. But some are more correct than others in certain situations. The following explains the volume comparisons:

  • Example A compares two sets of data: production cost volumes (Y axis) against quantity volumes produced (X axis) – a cost curve. Cost curves are like a column and line chart combined: both the height and width have values
  • Example B and C compares two sets of data: category (X axis) and volume (Y axis). Example C (grouped column chart) classifies items within the column category (differently colored columns/legend)
  • Examples D compares three sets of data: value (x axis), value (y axis), and volume (size of bubble)
  • Example E compares three sets of data: timing (X axis), volume for columns (left-hand Y axis), volume for area (right-hand Y axis); and classifies items within the column category (differently colored columns/legend)
  • Example F and G compares two sets of data: category (Y axis) and volume (X axis). Example G classifies items within the bar category (differently colored columns/legend)
  • Example H (stacked column chart) compares three sets of data: category (X axis), volume total (Y axis), and the segments within the columns (Y axis). Of course, this type of chart can be converted to a bar configuration where the Y axis is the category and the X axis is the volume
  • Examples I and J (area charts) compare three sets of data: time (X axis) and volume (Y axis). The categories are identified by the differently colored sets of data. Example J plots volume as the combined category volumes for each point in time – that is, 100%.

Ranking

Rankings are typically always shown with bar charts (above). The data should be entered in a specific way: largest category first descending to smallest last – thus the ranking. Rankings for charts that have subcategories (grouped-bar chart example above right) should also be plotted in a specific manner. The top subcategory is the ranking category (the lightest blue in the case above).

Trend


Trends are always a comparison of value or volume over time. Trends indicate trajectory and are many times the basis for determining an overall status. Trends are also used to develop projections: past performance as an indicator for future performance.

  • Examples A and B compare two sets of data: time (X axis) and volume (Y axis). The categories are identified by the differently colored sets of data. Example B plots volume as the combined category volumes for each point in time – that is, 100%
  • Example C compares two sets of data: category (X axis) and volume (Y axis). Grouped and stacked column charts can also be used to show trends, although no example is given above
  • Example D a dual-axis line chart used for trends. This type of line chart compares two sets of volumes (two Y axes) over time (X axis). Many times this type of chart is used to show one line trending up and one line trending down and their intersection point
  • Example E compares three sets of data: timing (X axis), volume for columns (left-hand Y axis), volume for line (right-hand Y axis)
  • Example F is the traditional chart used for trends: the line chart. This chart plots the volume (Y axis) for multiple categories (lines) over time (X axis)
  • Example G compares three sets of data: timing (X axis), volume for columns (left-hand Y axis), volume for area (right-hand Y axis)
  • Example H compares three sets of data: timing (X axis), volume for columns (left-hand Y axis), volume for line (right-hand Y axis); and classifies items within the column category (differently colored columns/legend)
  • Example I compares three sets of data: timing (X axis), volume for columns – each segment is a data set (left-hand Y axis), volume for line (right-hand Y axis); and classifies segments within the column category (differently colored columns segments/legend)
  • Example J compares categories (colored plotted the lines) against a cycle of time (week, quarter, etc.)

Distribution


Scatter charts and bubble charts are used to reveal clusters or concentrations. Both scatter and bubble charts are plotted on two values (X and Y axes). The bubble chart adds a third set of data that plots volume (size of bubble). When the X and Y axes scales are interpreted qualitatively, the concentrations can be interpreted and action can be taken.

Cycle


Radar charts compare volume data (Y axis) for categories (the colored lines) over a set and repetitive cycle (X axis). These charts work best when fewer categories are plotted; too many makes the chart unreadable. The volume axis is the vertical line starting at the center of the chart and ending at the top of the chart: scale values and gridlines indicate volume markers. The X axis is indicated by the labels for each part of the cycle.

Composition

Charts that convey composition of a whole are not as plentiful, but just as useful. There is some redundancy between comparison and composition. But, as you've noticed, there is redundancy between the different types of comparison charts, as well. There are basically two types of composition charts.

Segments of a whole (100%)


Segments of a whole can be associated with percentages (100%) or can merely represent unit volumes. Certain chart types should be used if the data is associated with percentages.

  • Pie charts, Example A, are always percentage based, even if the values in the slices are not percentages. Regardless, the units description for a pie chart should read "100% = [total value of all pie slices added together]"
  • The stacked column chart in Example B is really just another way to plot five pie charts on an X axis. Each of the columns is scaled to 100%, even though the total number of Y axis value units for each column is different. The totals for each column should be labeled "100% = [total value of column segments added together]"
  • The stacked bar chart in Example C is also really just another way to plot five pie charts on an X axis. Each of the bars is scaled to 100%, even though the total number of Y axis value units for each bar is different. The totals for each bar should be labeled "100% = [total value of bar segments added together]"
  • The donut chart in Example E is rarely used, but it is useful in certain situations. Each of the segments for each donut represents a pie chart. The data is laid out so that the composition of each pie is more directly comparable to each other.
  • Example F is an area chart that compares three sets of data: time (X axis) and volume (Y axis). The categories are identified by the differently colored sets of data. Example B plots volume as the combined category volumes for each point in time – that is, 100%

2. Segments of a whole

Segments of a whole do not have to be associated with 100%, of course.

  • The stacked bar chart in Example A shows the each of the segmented bars scaled to its total value. The Y axis plots the categories and the X axis plots the volume. The totals for each bar should be labeled with the total for all the segments.
  • The stacked column chart in Example B shows the each of the segmented columns scaled to its total value. The Y axis plots the categories and the X axis plots the volume. The totals for each column should be labeled with the total for all the segments.
  • Example C compares three sets of data: timing (X axis), volume for columns – each segment is a data set (left-hand Y axis), volume for line (right-hand Y axis); and classifies segments within the column category (differently colored columns segments/legend). Each stacked column represents a whole; the segments are the components of the whole.
  • Example D compares two sets of data: time (X axis) and volume (Y axis). The categories are identified by the differently colored sets/layers of data.
  • Examples E and F are waterfall charts. They are merely a plus and minus system that sums. Thus, the columns (Example E) or bars (Example F) are parts of the whole (the end column/bar that is plotted as the total). Both of these waterfall charts show a subtotal, which is not part of the whole.

* * *

Data-driven charts are a powerful tool when conveying messages supported by data. Don't settle for a text and data table when a data-driven chart can display the information much more clearly and intuitively. But choose carefully the charts you use to convey messages with data. The series descriptions for the Data-Driven Charts Category on the PowerFrameworks site have a lot of additional information and for each chart type. Be sure to review these descriptions and the series example if you are in doubt about which chart you need to use.

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