1062 lines
46 KiB
Markdown
1062 lines
46 KiB
Markdown
# EXPRESS – Choose proper visualization
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EXPRESS covers all aspects of choosing the proper visualization in reports and
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presentations.
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_Proper visualization_ means that reports and presentations contain charts and
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tables, which convey the desired message along with the underlying facts as
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quickly as possible.
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This chapter covers utilizing the correct types of charts and tables, replacing
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inappropriate visualizations and representations, adding comparisons, and
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explaining causes.
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1. [Use appropriate object types](#ex-1-use-appropriate-object-types)
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2. [Replace inappropriate chart types](#ex-2-replace-inappropriate-chart-types)
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3. [Replace inappropriate representations](#ex-3-replace-inappropriate-representations)
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4. [Add comparisons](#ex-4-add-comparisons)
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5. [Explain causes](#ex-5-explain-causes)
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## EX 1 Use appropriate object types
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Choosing the appropriate _object type_ is of prime importance for the
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comprehension of reports and presentations.
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We use tables when looking up data. Tables have a high information density. They
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are clear, they are honest, they do not want to highlight, and they typically do
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not want to visually convey a certain message. So they do not compete with
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charts.
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Charts on the opposite are always biased. It is the selection of data, the
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selection of the chart type, and the usage of highlighting which makes the
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difference. We evaluate charts by asking whether they transfer the intended
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message effectively and in a proper way. So charts cannot be replaced by tables.
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The following section is about choosing the appropriate types of charts and
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tables. It presents in detail different types, layouts, and examples as well as
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their proper application.
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## EX 1.1 Use appropriate chart types
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A _chart_ is a graphical object, in which visualization elements such as
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columns, bars, and lines represent data.
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This section discusses the types, layout, and examples of _single charts_.
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_Overlay charts_ _and multiple charts_ are discussed in the CO 4 “[Add
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elements](06-condense.md#co-4-add-elements)” and CO 5 “[Add
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objects](06-condense.md#co-5-add-objects)”.
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The most important groups of business charts are those showing development over
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time (charts with horizontal category axes), those showing structural
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relationships (charts with vertical category axes), and those showing x‑y
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charts, scatter plots, and bubble charts (charts with two value axes), see
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Figure EX 1.1.
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Other chart types are of lesser interest in business communication and will be
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treated in a later version of the standards.
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Looking at charts with horizontal and vertical category axes, the chart
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selection matrix displayed in the figure aids in selecting the right chart type
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for time series and structure analyses.
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In the following sections, the correct usage of _charts with horizontal category
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axes_, _charts with vertical category axes_, and *charts with two value axes* is
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discussed in greater detail.
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**Charts with horizontal category axes**
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Charts with horizontal category axes (short: _horizontal charts_) typically
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display time series. Use the horizontal category axis as a time axis.
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Vertically, the visualization elements represent the data per time period or
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point of time (there is no need to show a vertical value axis as the
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visualization elements carry their own values). Time category axes run from left
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to right and show characteristics of period types (e.g. months or years) or
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points of time (dates).
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In general, the data series of a _horizontal chart_ is represented by columns
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(single, stacked, grouped), vertical pins, horizontal waterfalls, or lines.
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_Vertical pins_ can be considered very thin columns. Because of their
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importance, they are dealt with in a separate section.
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Here follows the grouping of _horizontal chart types_:
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**Single column charts**
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In general, _single column charts_ (short: single columns) are used to display
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the temporal evolvement of one data series.
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Single columns consist of:
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- **Horizontal category axis:** The _horizontal category axis_ represents with
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its labels the respective time periods or points of time. The part on
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“Semantic rules” suggests to use the category width (see width A in the
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figure) for identifying the period type (see UN 3.3 “[Unify time
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periods](09-unify.md#un-33-unify-time-periods-and-points-of-time)”).
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- **Columns**: One _column_ per time period or point of time extends from the
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category axis in accordance with the respective value. Columns are displayed
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in the foreground of the category axis, so that the length of the column is
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not hidden. The part on “Semantic rules” suggests that the ratio of column
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width to category width (see ratio B/A in the figure) represents information
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about the measure type (see UN 3.1 “[Unify
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measures](09-unify.md#un-31-unify-measures)”).
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- **Legends**: As there is only one data series, the legend (name of the data
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series) is part of the chart title.
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- **Data labels**: _Data labels_ name the values of the data series
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corresponding to the length of the respective columns. Position the labels
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of positive values above their respective columns, the labels of negative
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values below.
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**Stacked column charts**
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_Stacked column charts_ (short: stacked columns) represent more than one data
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series (e.g. multiple products, countries, or divisions), see the figure on the
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left.
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Stacked columns consist of:
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- **Horizontal category axis:** See single column charts.
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- **Columns**: The columns (see single column charts) are divided into
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segments (Excel names them “data points”) representing the data series
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(stacked columns).
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- **Legends**: Legends (names of the data series) are positioned either on the
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far left side with right-aligned text or on the far right side with
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left-aligned text. The column segments define their vertical position,
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centered vertically with the data labels of the respective column segment.
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If a segment at the far left or far right is missing or has a very small
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size, its legends might need assisting lines.
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- **Data labels**: _Data labels_ name the values of the data series
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corresponding to the length of the respective column segments. If the sum of
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the column segments of a category is positive (column pointing upward), the
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label of the sum is positioned above the respective column, if negative
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(column pointing downward), it is positioned below.
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It must be pointed out that stacked columns should only be used if all chart
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values are either positive or negative.
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This chart type might also not be a good choice if the values of each data
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series vary too much. The maximum number of data series (segments of a stacked
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column) to be shown depends on the range of how much the values of each data
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series vary: More than 5 data series will only work well in the case of little
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variations.
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Position the data series of central importance or interest directly on the axis
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in order to best see its development over time.
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**Grouped column charts**
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_Grouped column charts_ (short: grouped columns) show, in general, time series
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for a primary scenario (e.g. AC or FC) in comparison with a reference scenario
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(e.g. PY or PL). Two columns per category (_grouped columns_) represent these
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two scenarios.
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The columns of the primary scenario and the reference scenario overlap, the
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reference scenario placed behind the primary scenario – either to the left or
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right of the primary scenario (see bottom chart of the figure as well as the
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paragraph on ”Scenario comparisons” in UN 4.1 “[Unify scenario
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analyses](09-unify.md#un-41-unify-scenario-analyses)”). A third scenario could
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be displayed using a _reference scenario triangle_. All other elements of a
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grouped column chart are identical to single column charts.
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Instead of using grouped columns, the primary scenario can be represented with a
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single column with the reference scenario represented by reference scenario
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triangles (see top chart of the figure).
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**Horizontal pin charts**
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_Horizontal pin charts_ (short: horizontal pins) are used for the visualization
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of relative variances in a time series analysis.
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Horizontal pins consist of:
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- **Horizontal category axis:** see _single column chart_.
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- **Pins**: One _pin_ per time period or point of time extends from the
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category axis to the respective length. The pin has the size of a very thin
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column. Color the pin green or red corresponding with positive or negative
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relative variances respectively. The fill of the pinhead represents the
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primary scenario (see the paragraph on “Scenario comparisons” in UN 4.1
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“[Unify scenario analyses](09-unify.md#un-41-unify-scenario-analyses)”).
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Display the pin in the foreground, so that the length of the pin (see length
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X in the figure) is not hidden.
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- **Legends**: As there is only one data series, the legend (name of the data
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series) is part of the chart title.
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- **Data labels**: _Data labels_ name the values of the data series consistent
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with the length of the respective pins. Position the labels of positive
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values above the respective pins, labels of negative values below.
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**Horizontal waterfall charts**
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_Horizontal waterfall charts_ (short: _horizontal waterfalls_ or _column
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waterfalls_) analyze root causes, over time, for the change or variance between
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two or more statuses. Accordingly, horizontal waterfalls consist of two or more
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_base columns and totals columns_. In between a base column and a totals column
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there are _contribution columns_ demonstrating what led to the difference
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between these two columns. The _contribution columns_ start at the end value,
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i.e. the height, of the preceding column, and show the positive or negative
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contribution as well as the accumulated contribution of all columns up to the
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respective point of time.
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There are two types of horizontal waterfalls:
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**Growth waterfalls**: In _growth waterfalls_, base columns and totals columns
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represent a stock measure (e.g. headcount, accounts receivable) at different
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points in time (e.g. end of Q4 2012, 2013 and 2014). The contribution columns in
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between represent the changes (increases and decreases) over time of this stock
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measure.
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(There is no vertical equivalent to the horizontal _growth waterfall_.)
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**Horizontal variance waterfalls**: In _horizontal variance waterfalls_, base
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columns and totals columns represent a flow measure (e.g. sales) at different
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periods in time (e.g. 2015 and 2016) and/or regarding different scenarios (e.g.
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PL and AC). The contribution columns in between represent the periodical
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variances between the different time periods and/or scenarios.
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The elements of a horizontal waterfall chart are the same as the elements of
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single column charts. In addition, _assisting lines_ connect the end of a column
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to the beginning of the succeeding column.
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**Line charts**
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In general, _line charts_ are used for the display of the temporal evolvement of
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data series with many data points.
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Many data points lead to small category widths. The advantage of line charts
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over column charts is the simplified display of data (better _data-ink-ratio_).
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In addition, they can better represent positive and negative values of more than
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one data series than columns. On the other hand, lines tend to imply a
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continuous timeline – practically non-existent in business communication.
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Therefore lines should not be used for the presentation of data series with only
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a few values.
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Line charts cannot be “stacked” in order to show structure like in stacked
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column charts. In the place of line charts for “stacked data”, _area charts_
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offer a good solution (there is no layout concept for area charts in this
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version of the guide yet).
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Line charts with more than three intersecting lines tend to be confusing.
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Instead, several smaller charts with one line each could be placed next to one
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another (small multiples), particularly when the general trends of the lines are
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to be analyzed – not the direct comparison of two data series (e.g. in comparing
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seasonal developments of several years), see also EX 2.4 “[Replace spaghetti
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charts](04-express.md#ex-24-replace-spaghetti-charts)”.
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Line charts consist of:
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- **Horizontal category axis:** See _single column chart_. The semantic rules
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in part 3 suggest to use the category width (see width A in the
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first figure) for identifying the period type (see UN 3.3 “[Unify time
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periods](09-unify.md#un-33-unify-time-periods-and-points-of-time)”).
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- **Lines**: One or more _lines_ with _line markers_ represent the values of
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the respective data series. Use line thickness, line color, and line markers
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for meaning, see part “Semantic rules”.
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- **Legends**: _Legends_ label the data series. If the line chart shows only
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one data series, include the legend in the chart title. If the line chart
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shows two or more data series, the legend should be positioned to the right
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of the far right data point (left-aligned text, see first figure) or the
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left of the far left data point (right-aligned text, see second figure).
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Alternatively position legends close to the lines at any other place in the
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chart.
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- **Data labels**: _Data labels_ name the values of the respective data
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points. If possible, label maximum values (peaks) above the line markers and
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minimum values (valleys) below the line markers. In many practical
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applications it is not necessary to clutter the line chart by labeling every
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data point, see second figure on the left and SI 5.3 “[Avoid unnecessary
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labels](05-simplify.md#si-53-avoid-unnecessary-labels)”.
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**Other horizontal charts**
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Other chart types with horizontal category axes are *boxplot charts* (range
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charts) and _area charts_. There is no specific notation concept for these chart
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types yet however it can be easily derived from the notation concept of column
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and line charts.
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**Charts with vertical category axes**
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Charts with vertical category axes (_vertical charts_) typically show structural
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data. In general, present structural data of one period or one point of time in
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the form of _bars_.
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Use the vertical category as a structure axis. Horizontally, the visualization
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elements represent the data per structure element (there is no need for a
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horizontal value axis as the visualization elements carry their own values).
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Structure axes run from top to bottom and show characteristics of structures
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(e.g. products or countries). The sequence of these elements depends on the
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intended analysis; see the UNIFY section about “Structure analyses”.
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In general, the data series of a vertical chart is represented by (horizontal)
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_bars_ (single, stacked, grouped), by _horizontal pins_, or by _waterfall bars_.
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Do not use lines in vertical charts as they could be interpreted as trends or
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developments, which do not exist in structure analyses.
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_Horizontal pins_ can be considered very thin bars, but because of their
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importance are dealt with in a separate section. A chart with horizontal pins is
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called a _vertical pin chart_.
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Here follows the grouping of _vertical chart types_:
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**Single bar charts**
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In general, _single bar charts_ (short: single bars) are used for the structural
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analysis of one data series (e.g., products, countries, or divisions) for one
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period or one point in time.
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Single bar charts consist of:
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- **Vertical category axis:** The _vertical category axis_ with its labels
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represents the respective structure elements such as countries, products,
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etc. The category width (see width A in figure) should be the same for
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corresponding analyses.
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- **Bars**: One _bar_ per structure element extends from the category axis to
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the length representing the respective value. Display the bars in the
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foreground of the category axis, so that the length of the bar is not
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hidden. The part on “Semantic rules” suggests that the ratio of bar width to
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category width (see ratio B/A in figure) represents information about the
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measure type (see UN 3.1 “[Unify
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measures](09-unify.md#un-31-unify-measures)”).
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- **Legends**: As there is only one data series, the legend (name of the data
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series) is part of the chart title.
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- **Data labels**: _Data labels_ name the values of the data series consistent
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with the length of the respective bars. Position the labels of positive
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values at the right hand side of the respective bars, the labels of negative
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values at the left hand side.
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**Stacked bar charts**
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Stacked bar charts (short: stacked bars) represent more than one data series
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(e.g., products, countries, or divisions) for one period or one point in time.
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Stacked bar charts consist of:
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- Vertical category axis: See single bar charts.
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- **Bars**: The bars (see single bar charts) are divided into segments (Excel
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names them“data points”) representing the data series (stacked bars).
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- **Legends**: Legends (names of the data series) are positioned either above
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the top stacked bar or below the bottom stacked bar, with the bar segments
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defining their horizontal position: they are horizontally centered with the
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data labels of the respective bar segment. If a segment at the top or bottom
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is missing or has a very small size, its legend might need assisting lines.
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- **Data labels**: _Data labels_ name the values of the data series
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corresponding to the length of the respective bar segment. If the sum of the
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bar segments of a category is positive (bar pointing to the right), the
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label of the sum is positioned to the right hand side of the respective bar.
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If the sum is negative (bar pointing to the left), the label of the sum is
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positioned to the left hand side of the respective bar.
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It must be pointed out that stacked bars should only be used if all chart values
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are either positive or negative.
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This chart type might also not be a good choice if the values of each data
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series vary too much. The maximum number of data series (segments of a stacked
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bars) to be shown depends on the range of how much the values of each data
|
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series vary: More than 5 data series will only work well in the case of little
|
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variations.
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Position the data series of central interest directly at the axis in order to
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best see its structure.
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**Grouped bar charts**
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In general, _grouped bar charts_ (short: grouped bars) show structure analyses
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for a primary scenario (e.g. AC or FC) in comparison to a reference scenario
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(e.g. PY or PL). Two bars per category (_grouped bars)_ represent _these_ two
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scenarios.
|
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|
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The bars of the primary scenario and the reference scenario overlap, the
|
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reference scenario placed behind the primary scenario – either above or below
|
||
(see the bottom chart of the figure as well as the paragraph on “Scenario
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||
comparisons” in UN 4.1 “[Unify scenario
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analyses](09-unify.md#un-41-unify-scenario-analyses)”).
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A third scenario could be displayed using a _reference scenario triangle_. All
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other elements of a grouped bar chart are identical to a single bar chart.
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Alternatively, instead of grouped bars, the primary scenario can be represented
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with a single bar and the reference scenario by reference scenario triangles
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(see top chart of figure).
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**Vertical pin charts**
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_Vertical pin charts_ (short: vertical pins) are used for the visualization of
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relative variances in a structure analysis.
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Vertical pins consist of:
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- Vertical category axis: see _single bar chart_.
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- **Pins**: One _pin_ per structure element extends from the category axis to
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||
the respective length. The pin has the size of a very thin bar. It is
|
||
colored green or red when representing positive or negative relative
|
||
variances respectively. The fill of the pinhead represents the primary
|
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scenario (see the paragraph on “Scenario comparisons” in UN 4.1 “[Unify
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scenario analyses](09-unify.md#un-41-unify-scenario-analyses)”). Display
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pins in the foreground, so that the length of the pin (see length X in the
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figure) is not hidden.
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- **Legends**: As there is only one data series, the legend (name of the data
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series) is part of the chart title.
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||
|
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**Data labels**: _Data labels_ name the values of the data series corresponding
|
||
to the length of the respective pins. Position the labels of positive values at
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the right hand side of the respective pins, the labels of negative values at the
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left hand side.
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**Vertical waterfall charts**
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||
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_Vertical waterfalls charts_ (in short: _vertical waterfalls_ or _bar
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waterfalls_) analyze structural root causes for the difference between two or
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more statuses. Accordingly, vertical waterfalls consist of two or more _base
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||
bars_ and _totals bars_. In between a base bar and a totals bar there are
|
||
_contribution bars_ representing the contribution to the difference between
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||
these two bars. Starting from the top base bar, _contribution bars_ always start
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at the end of the preceding bar, showing positive or negative individual
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contributions of the respective structure element as well as the accumulated
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contribution resulting in the next totals bar.
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There are two types of vertical waterfalls:
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**Calculation waterfalls**: In _calculation waterfalls_, base bars and totals
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bars represent base and result measures (e.g. sales and EBIT) whereas the
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contribution bars in between represent the additions and subtractions of other
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measures (e.g. financial income and direct cost) in a calculation scheme. More
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complex calculation schemes (e.g. profit and loss calculation) can have
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intermediate subtotals bars.
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(There is no horizontal correspondence to the vertical _calculation waterfall_.)
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**Vertical variance waterfalls**: In _vertical variance waterfalls_, base bars
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and totals bars represent values at different periods or points in time (e.g.
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January 1, 2013 and January 1, 2014) and/or different scenarios (e.g. PY and
|
||
AC). The contribution bars in between represent the variances in structure
|
||
between the different times and/or scenarios.
|
||
|
||
The elements of vertical waterfalls are the same as the elements of single bar
|
||
charts. In addition, _assisting lines_ connect the end of a bar with the
|
||
beginning of the succeeding bar.
|
||
|
||
**Remainder bar**
|
||
|
||
|
||
|
||
If a large number of elements need to be presented, then only the most important
|
||
elements can be displayed in one chart or on one page. In order to make the
|
||
analyses exhaustive, sort the elements by descending size, accumulating the
|
||
smallest elements, which cannot be depicted, in a _remainder bar_ (“rest of...”).
|
||
Separate the remainder bar from the other bars by a wider gap (see gap C in the
|
||
figure on the left).
|
||
|
||
Note: This remainder bar has to be excluded from some Structure analyses such as
|
||
averaging, ranking, and selecting.
|
||
|
||
**Other vertical charts**
|
||
|
||
Other chart types with vertical category axes are _vertical boxplot charts_
|
||
(range charts). There is no specific notation concept for this chart type yet
|
||
however it can be derived from the notation of the standard bar charts.
|
||
|
||
In general, do not use lines and areas in vertical charts as they might
|
||
underline a continuum of data non-existent in business communication.
|
||
|
||
**Charts with two values axes**
|
||
|
||
|
||
|
||
_Charts with two value axes_ show two-dimensional positioning of visualization
|
||
elements, which can provide new and interesting insights. *Scattergrams* arrange
|
||
points in a two-dimensional coordinate system.
|
||
|
||
|
||
|
||
*Bubble charts* (portfolio charts) have bubbles instead of points and use the
|
||
bubble area to show a third dimension. A fourth dimension could be presented via
|
||
pie segments within the bubbles (bubble pie charts).
|
||
|
||
Besides _scattergrams_ and _bubble charts_ there are other chart types with two
|
||
value axes, e.g. charts with horizontal axes representing a continuous timeline
|
||
(instead of fixed time categories) and charts with columns or bars of variable
|
||
width.
|
||
|
||
There are no specific notation rules for charts with two value axes yet. An
|
||
appropriate notation concept for these chart types can be derived from the
|
||
notation of column charts, bar charts and line charts with their data
|
||
visualization elements, legends, data labels, and axes.
|
||
|
||
## EX 1.2 Use appropriate table types
|
||
|
||
A *table* is a communication object in which data is arranged in two dimensions,
|
||
i.e. (vertical) _columns and_ (horizontal) _rows_. The _row header_ (row name)
|
||
describes the content of a row, the _column header_ (column name) the content of
|
||
a column. The data are positioned at the intersections of rows and columns
|
||
called _table cells_.
|
||
|
||
“One-dimensional tables” (tables with one or more columns but without row
|
||
headers) are called _lists_ and are not covered here.
|
||
|
||
_Table types_ are defined by a set of _columns_ and a set of _rows_ in order to
|
||
fulfill specific analytic and/or reporting purposes.
|
||
|
||
**Column types**
|
||
|
||
Column types are columns with similar content falling under similar column
|
||
headers. Typical column types are _time columns_ (with monthly or yearly data),
|
||
_scenario columns_ (with actual or plan data) and _variance columns_ (ΔPL or
|
||
ΔPY).
|
||
|
||
The following layout principles apply to all column types:
|
||
|
||
- **Width**: Columns belonging to a certain column type should have an
|
||
identical width. This column width should not depend on the text length of
|
||
the respective column header.
|
||
|
||
- **Orientation**: Right-align columns with numerical data. Left-align columns
|
||
with non-numerical data (e.g. texts or product names). _Column headers_ have
|
||
the same orientation as the rest of the column. Headers for combined columns
|
||
can be centered or even left-aligned to increase legibility.
|
||
|
||
- **Vertical lines and gaps**: Vertical lines separating different columns
|
||
should be very light or even omitted. Use white vertical lines or white
|
||
vertical gaps to mark the columns. In the following figures, different
|
||
widths of these white lines resp. gaps are being used to separate and group
|
||
columns. Separate columns of the same type by a narrow gap (see gap B1 in
|
||
the figure in section “Scenario columns” et seq.). Use a wider gap to
|
||
separate a group of similar columns from the next group (see gap B2 in the
|
||
figure in section “Row header columns” et seq.).
|
||
|
||
Additional layout principals depend on the _column types_ described below.
|
||
|
||
**Row header columns**
|
||
|
||
|
||
|
||
Row header columns contain the header texts of the rows. Often, these columns
|
||
are positioned at the very left of a table. In most cases, row header columns
|
||
are much wider than other column types.
|
||
|
||
Keep the texts of the row headers short by using abbreviations or footnotes in
|
||
order to omit too wide tables.
|
||
|
||
Use a wider gap (see width B2 in the figure) to separate the _row header column_
|
||
from columns with numbers.
|
||
|
||
**Scenario columns**
|
||
|
||
|
||
|
||
_Scenario columns_ show data for scenarios (e.g. previous year, plan, actual).
|
||
Use the same width for all scenario columns (depending on the number of digits).
|
||
|
||
For the sequence of scenario columns see UN 4.1 “[Unify scenario
|
||
analyses](09-unify.md#un-41-unify-scenario-analyses)”.
|
||
|
||
**Variance columns**
|
||
|
||
|
||
|
||
_Variance columns_ show data of absolute variances (e.g. ΔPL, ΔPY) or relative
|
||
variances (e.g. ΔPL%, ΔPY%).
|
||
|
||
**Time columns**
|
||
|
||
|
||
|
||
_Time columns_ show _time periods_ (for flow measures) or _points of time_ (for
|
||
stock measures).
|
||
|
||
Use a temporal order – from left to right – for the sequence of the time columns
|
||
(e.g. Jan, Feb, Mar, or 2013, 2014, 2015).
|
||
|
||
**Measure columns**
|
||
|
||
|
||
|
||
_Measure columns_ show measures such as sales, headcount, or equity.
|
||
|
||
Displaying long measure names in column headers can be challenging. As the
|
||
column width should not depend on the length of the measure name, use the
|
||
abbreviations defined in the glossary instead.
|
||
|
||
Use a wider gap after intermediate results to expose the calculation scheme (see
|
||
width B2 in the figure on the left).
|
||
|
||
**Structure columns**
|
||
|
||
|
||
|
||
_Structure columns_ show the elements of a structure dimension (e.g. countries
|
||
or products).
|
||
|
||
**“Thereof” columns**
|
||
|
||
|
||
|
||
If details of an aggregated column are shown in one or more column not totaling
|
||
to the aggregated column, these columns are called “thereof” columns.
|
||
|
||
The design of the _thereof columns_ should differ from other columns. E.g. use a
|
||
smaller font (see X in the figure) to expose a _thereof column_ and do not
|
||
separate it from the mother column (see columns _AL3_ and _AL3.1_ in the figure)
|
||
in order to show that it is part of it. A _thereof column_ is positioned at the
|
||
right hand side of the mother column.
|
||
|
||
**Remainder columns**
|
||
|
||
|
||
|
||
If the set to be presented in the columns has too many elements, accumulate the
|
||
less important or smaller elements in a _remainder column_ (e.g. 10 columns for
|
||
the top 10 countries and a remainder column titled“Rest of world” or “RoW”).
|
||
|
||
In the figure, the _remainder column_ “Other cost” has the same vertical gaps B1
|
||
as the other measure columns.
|
||
|
||
**“Percent of” columns**
|
||
|
||
|
||
|
||
Use “_Percent of_” columns to present important data of another column as shares
|
||
of a given total. A typical example for a “percent of” column is data of a
|
||
profit and loss statement as a percentage of sales.
|
||
|
||
“Percent of” columns have a smaller font size (see X) than the other columns.
|
||
|
||
**Totals columns**
|
||
|
||
|
||
|
||
Position columns displaying _totals of a group of columns_ (e.g. quarters
|
||
totaling in years or products totaling in product groups) at the right hand side
|
||
of the columns belonging to this group. The design of the _totals columns_
|
||
should differ from other columns, e.g. highlighted by bold fonts or by light
|
||
gray background.
|
||
|
||
The column types described before refer to _single_ columns. The following
|
||
paragraphs present _combined_ columns i.e. _hierarchical_ and _nested_ columns.
|
||
|
||
**Hierarchical columns**
|
||
|
||
|
||
|
||
Hierarchies in dimensions may call for columns showing multiple levels. If
|
||
possible, the sibling elements belonging to the same parent element of a
|
||
dimension should be homogenous, mutually exclusive, and collectively exhaustive.
|
||
|
||
Separate parents by appropriate means, e.g. wider gaps. Display the parent
|
||
columns at the right hand side of their child columns (like _totals columns_).
|
||
|
||
In the figure, a wider gap B2 separates the two years (with four quarters each)
|
||
from each other.
|
||
|
||
**Nested columns**
|
||
|
||
|
||
|
||
In _nested columns_, two column types are combined in such a way that the
|
||
columns of one type repeat iteratively within every column of the other type.
|
||
Separate the resulting groups of columns by appropriate means, e.g. wider gaps.
|
||
|
||
In the figure, wider gaps B2 separate the four years (with AC and PL data each)
|
||
from each other.
|
||
|
||
**Row types**
|
||
|
||
_Row types_ are rows with similar content falling under similar row headers.
|
||
Typical row types are _measure rows_ (e.g. sales, cost, profit) or _structure
|
||
rows_ (e.g. Italy, France, UK).
|
||
|
||
The following layout principles apply to all row types:
|
||
|
||
- **Height**: Rows belonging to a row type should have an identical height
|
||
(see height A in the figure in section “measure rows” et seq.).
|
||
|
||
- **Horizontal lines**: Separating rows by light horizontal lines will
|
||
increase the legibility.
|
||
|
||
Additional layout principals depend on the row types described below.
|
||
|
||
_Time periods and points of time_, _scenarios_, and _variances_ should be
|
||
displayed in columns rather than in rows.
|
||
|
||
**Column header rows**
|
||
|
||
|
||
|
||
Column header rows contain the header texts of the columns. In most cases, these
|
||
rows are positioned at the very top of a table. In order to group columns two
|
||
and more column header rows might be necessary. If necessary, abbreviate column
|
||
header texts in order to fit in the preferred column width. Alternatively keep
|
||
column headers short by using footnotes.
|
||
|
||
In the figure the _column header row_ uses two lines in order to fit the column
|
||
header texts in the narrow columns.
|
||
|
||
**Measure rows**
|
||
|
||
|
||
|
||
_Measure rows_ show measures such as sales, headcount, or equity.
|
||
|
||
Separate rows showing final or intermediate results of a calculation scheme
|
||
(_results rows_ _or totals rows_) by solid lines. Display results rows in bold
|
||
font or highlight them with light gray background. An additional gap B below a
|
||
results row will increase legibility.
|
||
|
||
**Structure rows**
|
||
|
||
|
||
|
||
Structure rows show elements of a structure dimension (e.g. countries or
|
||
products).
|
||
|
||
**“Thereof” rows**
|
||
|
||
|
||
|
||
If details of an aggregated row are shown in one or more rows not totaling to
|
||
the aggregated row, these rows are called “thereof” rows. Place the aggregated
|
||
_above_ the “thereof” rows (in contrast to the _totals row_ positioned _below_
|
||
the rows of its group).
|
||
|
||
The design of the _thereof rows_ should differ from other rows. E.g. in the
|
||
figure, the _thereof row_ is of smaller height, written in a smaller font (see
|
||
X), not separated by a horizontal line, and has a right-aligned row header.
|
||
|
||
**Remainder rows**
|
||
|
||
|
||
|
||
If the structure dimension to be presented in the rows outline has too many
|
||
elements, accumulate the less important or smaller elements in a _remainder row_
|
||
(e.g. 7 rows for the top 7 countries and a remainder titled “Rest of world”).
|
||
|
||
Exclude remainder rows from some of the Structure analyses such as averaging,
|
||
ranking, and selecting.
|
||
|
||
In the figure, the _remainder row_ has the same row height A as the other
|
||
structure rows of this table example.
|
||
|
||
**“Percent of” rows**
|
||
|
||
|
||
|
||
Use “_Percent of_” rows to present important data of another row as shares of a
|
||
given total. A typical example for a “_percent of_” row is gross profit as a
|
||
percentage of sales.
|
||
|
||
“Percent of” rows have a smaller font size (see X) than the other rows.
|
||
|
||
**Totals rows**
|
||
|
||
|
||
|
||
Place rows displaying _totals of a group of rows_ (e.g. countries totaling in
|
||
regions or products totaling in product groups) below the rows of this group and
|
||
separated them by solid lines.
|
||
|
||
The design of the _totals rows_ should differ from other rows, e.g. highlighted
|
||
by bold fonts or by light gray background.
|
||
|
||
The row types described before refer to _single_ rows. The following paragraphs
|
||
present _combined_ rows i.e. _hierarchical_ and _nested_ rows.
|
||
|
||
**Hierarchical rows**
|
||
|
||
|
||
|
||
Hierarchies in dimensions may call for rows showing multiple levels. If
|
||
possible, the sibling elements belonging to the same parent element of a
|
||
dimension should be homogenous, mutually exclusive, and collectively exhaustive.
|
||
|
||
Separate parents by appropriate means, e.g. wider gaps (see additional gap B in
|
||
the figure). Display the parent rows _below_ their child rows (like _totals
|
||
rows_).
|
||
|
||
**Nested rows**
|
||
|
||
|
||
|
||
In _nested rows_, two types of rows are combined in such a way that the rows of
|
||
one type repeat iteratively within every row of the other row type.
|
||
|
||
Separate the resulting groups of rows by appropriate means, e.g. wider gaps (see
|
||
additional gap B in the figure).
|
||
|
||
**Table types**
|
||
|
||
|
||
|
||
Table types are distinguished by their analytic purpose in time series tables,
|
||
variance tables and cross tables. Tables serving more than one analytic purpose
|
||
are called combined tables.
|
||
|
||
**Time series tables**
|
||
|
||
|
||
|
||
_Time series tables_ are used for time series analyses, combining time columns
|
||
with measure rows or structure rows.
|
||
|
||
A typical example for a _time series table_ is a sales analysis by countries
|
||
(rows) and years (columns).
|
||
|
||
**Variance tables**
|
||
|
||
|
||
|
||
_Variance tables_ are used for scenario analyses, combining scenario columns and
|
||
variance columns with measure rows or structure rows.
|
||
|
||
A typical example for a _variance table_ is a sales analysis by countries (rows)
|
||
showing different scenarios and different variances (columns).
|
||
|
||
**Cross tables**
|
||
|
||
|
||
|
||
_Cross tables_ are used for Structure analyses, combining structure columns with
|
||
structure rows.
|
||
|
||
A typical example of a _cross table_ is a sales table with countries in rows and
|
||
products in columns.
|
||
|
||
**Combined tables**
|
||
|
||
|
||
|
||
_Combined tables_ are used for multiple analyses. A combined table uses more
|
||
than one _column type_ and/or more than one _row type_ presented either side by
|
||
side or nested.
|
||
|
||
The first figure shows a hierarchical structure of countries on three levels in
|
||
the rows. The columns are nested: scenarios and variances are the same for both
|
||
time periods _November_ and _January_November_.
|
||
|
||
|
||
|
||
The second figure shows the measures of a calculation scheme in the rows. The
|
||
columns are nested: The four quarters and the annual total are the same for both
|
||
years.
|
||
|
||
|
||
|
||
The third figure shows the same rows as the second one (measures of a
|
||
calculation scheme). The nested columns now show PY and AC data as well as
|
||
absolute and relative variances for two markets.
|
||
|
||
## EX 2 Replace inappropriate chart types
|
||
|
||
Inappropriate charts make it hard to perceive the message. Knowing the correct
|
||
usage of chart types helps in replacing inappropriate visualizations, such as
|
||
pie charts, speedometer visualizations, radar charts, and spaghetti charts, with
|
||
those chart types better suited.
|
||
|
||
## EX 2.1 Replace pie and ring charts
|
||
|
||
|
||
|
||
_Pie_ and _ring charts_ are [circular
|
||
charts](http://en.wikipedia.org/wiki/Circle) dividing some total into
|
||
[sectors](http://en.wikipedia.org/wiki/Circular_sector) of relative proportion,
|
||
but there are better ways to illustrate the numericalproportions of segments,
|
||
e.g. bar charts or charts with stacked columns, see Figure EX 2.1.
|
||
|
||
_Pie charts_ allow for one-dimensional analyses only, and therefore seldom
|
||
convey revealing insights. However, some useful applications for pie charts
|
||
exist, for example when market sizes and/or market shares for one period need to
|
||
be allocated to certain regions on a map (see CH 3.3 “[Avoid misleading colored
|
||
areas in maps](07-check.md#ch-33-avoid-misleading-colored-areas-in-maps)”). As
|
||
opposed to column or bar charts, pie charts can be positioned on a specific
|
||
point on a map.
|
||
|
||
## EX 2.2 Replace gauges, speedometers
|
||
|
||
|
||
|
||
Often found as part of a so-called dashboard, _speedometers_ are probably one of
|
||
the most useless visualizations out there. They take up way too much space and
|
||
have often confusing color coding and scaling. In general, bar charts showing
|
||
the respective structures or columns charts showing the respective development
|
||
over time are better choices, see Figure EX 2.2.
|
||
|
||
## EX 2.3 Replace radar and funnel charts
|
||
|
||
|
||
|
||
So-called _radar charts_ (also called _net charts_ or _spider charts_) are
|
||
frequently used for evaluating purposes. Having no advantage over bar charts and
|
||
having, actually, many weaknesses, use them only for two-dimensional analyses
|
||
(e.g. comparing young-old with rich-poor). Willard C. Brinton wrote almost 100
|
||
years ago: “This type of chart should be banished to the scrap heap. Charts on
|
||
rectangular ruling are easier to draw and easier to understand.”
|
||
|
||
Of course, if the circular arrangement has meaning (such as the compass
|
||
direction), this kind of chart can be very valuable, but these types of analysis
|
||
are not typical in business reporting.
|
||
|
||
_Funnel charts_ are misleading when the size of the area displayed does not
|
||
correspond to the respective numerical values – an issue applying also to other
|
||
artificial chart forms (e.g. spheres) in which the length, area, or volume do
|
||
not correspond to the numerical values.
|
||
|
||
## EX 2.4 Replace spaghetti charts
|
||
|
||
|
||
|
||
A chart with more than three or four intersecting lines (“spaghetti chart”) can
|
||
be more confusing than several smaller charts with one line each placed next to
|
||
one another (small multiples), particularly when evaluating the shape or the
|
||
trend of the lines, see Figure EX 2.4.
|
||
|
||
However, when needing to compare exactly the height of data points of several
|
||
lines, spaghetti charts cannot be avoided.
|
||
|
||
## EX 2.5 Replace traffic lights
|
||
|
||
|
||
|
||
“Traffic lights” with green, red, and yellow areas are a popular form of
|
||
visualization but contain little information per area used. However, they can be
|
||
used for analyses showing “yes or no” decisions or situations similar to real
|
||
traffic lights. In all other cases replace them with more suitable means of
|
||
(analog) representation such as bar charts, see Figure EX 2.5.
|
||
|
||
## EX 3 Replace inappropriate representations
|
||
|
||
From a perceptual perspective, avoid all visual representations requiring time
|
||
consuming analyses or additional explanations, particularly the popular use of
|
||
merely conceptual representations and all forms of texts, including bullet
|
||
lists.
|
||
|
||
## EX 3.1 Prefer quantitative representations
|
||
|
||
|
||
|
||
Due to the time constraints usually involved with presentations, conceptual
|
||
graphs prove less suitable than charts, photos, maps, etc. For a one-hour
|
||
presentation, do not use more than three or four conceptual representations. Do
|
||
this only if they are absolutely essential for comprehension. The audience will
|
||
understand charts and pictures (photos, drawings, etc.) better and faster,
|
||
see Figure EX 3.1.
|
||
|
||
## EX 3.2 Avoid text slides in presentations
|
||
|
||
|
||
|
||
Avoid all forms of text slides in presentations. Texts should either be recited
|
||
or written in a handout. A few exceptions to this rule are specific texts being
|
||
discussed such as definitions, quotes, etc. In general, all forms of lists
|
||
(bullet points) should appear only in the written handout, not projected on the
|
||
screen. True, if someone sees and hears something simultaneously, he remembers
|
||
it better than when he just hears it, but bear in mind texts are not considered
|
||
something merely to be seen – they must be read and understood, see Figure EX
|
||
3.2.
|
||
|
||
## EX 4 Add comparisons
|
||
|
||
Visual perception is strongly based on setting one perceived object in relation
|
||
to another. Adding meaningful comparisons helps the eye evaluate faster, the
|
||
main purpose of charts.
|
||
|
||
## EX 4.1 Add scenarios
|
||
|
||
|
||
|
||
Scenarios such as “plan” and “previous year” are the most common references for
|
||
comparison purposes. Add them whenever available. Use a standardized scenario
|
||
notation for faster comprehension, see Figure EX 4.1.
|
||
|
||
## EX 4.2 Add variances
|
||
|
||
|
||
|
||
Having added scenarios for comparison purposes, the visualization of variances
|
||
makes it easier to evaluate the situation. Use a standardized notation of
|
||
variances for faster comprehension, see Figure EX 4.2.
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||
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## EX 5 Explain causes
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||
|
||
Present data more understandable by showing interrelations, i.e. causes and
|
||
dependencies. Seeing the entire context, especially extreme values and deviant
|
||
values, helps to explain causes. Details increase not only the level of
|
||
credibility but also comprehension. Use charts to prove, explain, and render
|
||
something plausible, not to serve merely as decoration. This section focuses on
|
||
the explanation of causes by using tree structures, clusters, and correlations.
|
||
A more structured approach to increasing information density is discussed in the
|
||
chapter “CONDENSE – Increase information density”.
|
||
|
||
## EX 5.1 Show tree structures
|
||
|
||
|
||
|
||
The presentation of the assumptions or basic data upon which a business analysis
|
||
is based, results not only in better understanding, but also makes it more
|
||
convincing. A good choice is the display of calculated measures in a tree
|
||
structure, see Figure EX 5.1 (see also CO 5.2 “[Show related charts on one
|
||
page](06-condense.md#co-52-show-related-charts-on-one-page)”).
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||
|
||
## EX 5.2 Show clusters
|
||
|
||
|
||
|
||
With the help of clusters in two-dimensional and three-dimensional forms, large
|
||
amounts of data very often can provide interesting and new insights, see Figure
|
||
EX 5.2.
|
||
|
||
## EX 5.3 Show correlations
|
||
|
||
|
||
|
||
When comparing several data series, correlations are often sought in order to
|
||
better understand the interrelations. Suitable rankings and comparisons can
|
||
facilitate the understanding of patterns, see Figure EX 5.3.
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||
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[← Organize content](02-structure.md) | [Avoid Clutter →](05-simplify.md)
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