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Dashboards tell us about critical business or operational metrics.
Key features of a dashboard include:
“Dashboards are not an appropriate venue for artistic impression.” -- Stephen Few
There are three major types of dashboards we will discuss:
These different dashboards fit the differing needs of an organization.
Operational dashboards provide “day-to-day” data that assist line employees in making decisions. They tend to be specific to the nature of the user.
Operational dashboards drive action by informing that a process may be “out of control” and giving operators a hint as to where the change might have occurred.
Strategic dashboards focus on KPIs which are tracked periodically. Good strategic dashboards let people get a quick view of important business measures.
Tactical dashboards tend to show business-level KPIs like strategic dashboards but let you slice and dice data.
Many dashboards look something like this:
We can intuitively see that this is not a good dashboard, but we might not be able to explain all the reasons why. The rest of this session will give us the tools we need to understand why this is not a good dashboard.
Key factors in knowing your audience:
Example: the following is great for a line worker or maybe a low-level manager, but board members don't have the time to care about your operational metrics.
Who is your intended audience?
For each of these, we would display different measures in different ways.
Continue scrolling for bonus material related to knowing your audience and your data.
How will users make use of your dashboard? What actions do you want them to take as a result?
Your job as a dashboard creator is to provide relevant metrics in an easy-to-understand way, requiring the user to get relevant information as easily as possible in order to make good business decisions. Critical considerations:
Critical considerations (cotd.):
Are you showing the right measures in the right way?
For a sales team dashboard, you want to include sales-centric measures like leads in different steps of the pipeline, likelihood of a contact making a purchase, and number of sales by person against a quota. You don't want to include measures like server uptime, net margin, or number of high-severity issues.
Tailor your dashboards to show what people need to see in the right way: use visuals which make sense given the context. For example, a gauge might be an acceptable visual for team-wide sales versus quota, but it would be unacceptable for number of leads in a pipeline step.
What cultural differences might matter?
The meanings of colors can differ between cultures. For example, in European and American culture, red typically has one of two major meanings: love and danger. In finance, we use red to indicate negative values (going back to danger).
By contrast, in China, red has historically indicated prosperity--people give gifts of money at weddings and during major holidays in small red envelopes.
If you are tailoring your dashboard for people in a particular culture, it helps to have some understanding of the roles particlar colors play in that culture.
Knowing your data matters as much as knowing your audience. Critical questions include:
In this section, we will look at six important visual principles:
People can only process a limited amount of information at a time. Our working memory can hold approximately 3-7 items at one time. Think of this working memory like registers and our long-term memory like RAM: we move information out of long-term memory into working memory and (sometimes) vice versa.
Because of this, we want to look for ways to reduce mental load. Techniques include:
Remove unnecessary items: extraneous text, logos, etc. These simply clutter the screen.
After removing the clutter, notice how much easier this is to read.
Next, remove legends whenever possible. Legends require people to look up and down over and over to make sense of your visual. If you have a detailed legend, you are probably using the wrong visual.
Reduce your color usage. Keep color usage limited and consistent. Make sure colors actually add value.
In this case, it's clear that the colors were not in fact helping anything.
By reducing color usage, I can easily highlight a particular value with a color, a pre-attentive attribute.
Color is also an associative property, so people tend to link similar things with the same color together. If you break that pattern, you can confuse people.
Humans have trouble with precise 3D measurements. In rare cases, a 3D image is better than a 2D equivalent, but that is never the case for bar or column charts.
3D "gloss" is also not helpful for getting your point across.
In European languages, we read left-to-right, top-to-bottom.
In Middle Eastern languages (Hebrew and Arabic), we read right-to-left, top-to-bottom.
In ancient Asian languages (especially Chinese), we read top-to-bottom, right-to-left. In modern Asian languages, we read left-to-right, top-to-bottom (except when we don't).
These also bias where we tend to look.
The Gutenberg layout:
The Z layout:
The F layout:
These layouts tend to work for evenly distributed, homogeneous data, things like newspaper articles which are text-heavy in content.
For image-heavy dashboards, people follow a different principle: focal points. Our eyes look for the most dominant element on the page, and then follow a path from the dominant element to other focal points.
This means you should lay out your primary focal points in a way that makes it easy for a viewer to follow along.
The Rule of Thirds applies to images, but also to dashboards.
The strategic dashboard (sort of) follows the Rule of Thirds.
Glanceability is the idea that a human can, at a glance, gain important information from your dashboard. Things which help glanceability include:
Color Vision Deficiency: 8% of men & 0.5% of women.
|Protanomaly||Red||Red, orange, and yellow appear greener.||1% M|
|Protanopia||Red||Red appears black. Some yellows, oranges, and greens appear yellow.||1% M|
|Deuteranomaly||Green||Blue and violet look the same. Yellow and green look redder.||5% M|
|Deuteranopia||Green||Reds are brownish-yellow and greens are beige.||1% M|
|Tritanomaly||Blue||Blue appears greener and yellow & red look pink.||Rare M/F|
|Tritanopia||Blue||Blue appears green and yellow appears violet or grey.||Rare M/F|
A sunset in Orange County, California:
A sunset in Orange County, California with protanopia:
A sunset in Orange County, California with deuteranopia:
A sunset in Orange County, California with tritanopia:
Remember old SSIS?
SSIS with protanopia:
SSIS with deuteranopia:
SSIS with tritanopia:
There are a number of visuals available to us:
Great when users need to compare data directly.
Tables and matrices generally don't belong on strategic dashboards, but do belong in tactical dashboards and sometimes operational dashboards.
Great when there are few categories but potentially many periods.
Great when there are few periods with many categories. Also good when labels are lengthy.
Choose a bar chart if:
Choose a column chart if:
Whichever you choose, you should have your bar/column charts start at the origin (0). That prevents these charts from being misleading.
Named after Bill Cleveland, this is a minimalistic visual for comparing categories.
Dot plots are not native to Power BI, but there are a couple of custom visuals which give you dot plots. They can provide a lot of information in a very small amount of space, making them great accompaniments for tables or larger visuals.
Good for showing a normalized comparison of measures across several categories.
Great for time series data stretching over many periods with non-cyclical data, but showing few categories. You can also use a column chart here, especially with only 2-3 categories.
Risky chart, but works well with two inter-related but distinct variables. Often used with stock market levels and volumes.
For bar charts, we want to start at the origin. For line charts (and dot plots), we don't need to. Instead, Cleveland, McGill, & McGill proposed making the average line slope 45 degrees. This principle reduces the potential for confusion when viewing a line chart and is why it's fine to have the bottom of a line chart start above 0.
Power BI and Excel automatically do this for you.
Great for showing relationships between two variables over a relatively small number of categories.
Great small showing relationships between three variables over a relatively few number of categories.
Risky chart, but great for showing the relative share of value for a medium to large number of hierarchical, categorical values.
The following charts are ones which have their uses, but I'm not particularly fond of them.
We will look at potentially good use cases, but also look at why I don't much like these charts.
Best use: simple share of a static total.
Pie charts have several problems:
There are several alternatives to pie charts:
Best use: showing progress toward a target or as a percent full.
Gauges themselves are fine, but they need to show progress toward a goal or provide an intuitive status. This is an example of a bad gauge, one where we lack information necessary to understand whether we need to take action. We have a value of 34, but what does it mean? Is that good? Bad?
Best use: showing relative and absolute differences of data which changes over time but has relatively few periods.
Looking at this again, what information can we know for sure? Just the top line and the bottom category.
Instead, you can show information more clearly with a line chart. The downside is that you'll have to calculate the total yourself if you want it to show.
Instead, you could also use a ribbon chart, which at least shows rank changes. But it's also a very noisy chart.
After going through theory, let's take another look at that "normal" dashboard and see why exactly it is not great.
After a presentation, I go get a Victory Burrito. I want to track these victory burritos over time, with the goal of having a victory burrito in 50 separate cities. Therefore, I want a dashboard. My dashboard should tell me:
My cousin Joey offered to make me a dashboard. Here's Joey's attempt:
Keep these tips in mind when creating a dashboard:
To learn more, go here: http://CSmore.info/on/dashboards