Neat and potentially useful data vis but copy like this makes me barf: “Bullet charts have proven to be ideally suited for any business dashboard data visualization.”
Proven? How? By what standard or metric? And for any business dashboard data vis? What does that even mean?
There is an example on creating a bullet chart in Excel. It starts with bar chart, which simple and easy to do and read. It then go through many steps to abstract and obscure some the information, to get to a chart that the author find more appealing. I don’t. I think a bar chart as more space and is more zen, while his chart feels crowded. Unless you start with only 3 bars, in which case it looks fine and doesn’t obstruct any information.
An unusual chart to convey simple information, and requires explanation, is a poor way to visualize your data. Only make a unique visualization if necessary to convey unique data. So, as you said, here just stick with the bar chart!
If it's ideally suited for everything then either it's a significant gain across the board and should be used in every situation ever, or it's got no particular advantage in any specific domain.
> To create a comparison range, you’ll have to pick a colour and make use of four different shades of colour. To help you do this, you’ll have to select the colours one after the other.
> Bear in mind that a bullet graph can either be horizontal or vertical in shape. As clearly mentioned above, the target of the organization must be feasible, and the right tools to reach the target should be provided for employees.
How did that last sentence end up in that paragraph?
Immediately below that, the final Excel output is unreadable: it shows the left axis scale ending at 450 and right axis scale ending at 400, with both values corresponding to the same grid line.
Below that, it says you can draw these charts in JavaScript with a <chart> element, as if browsers natively supported it.
That makes more sense. It’s still horribly non-intuitive. In particular, I don’t like the “qualitative” background shading technique, because it makes it appear as if a “satisfactory” performance is all of bad, good and satisfactory at the same time. Why not simply mark those thresholds under the scale of the x-axis? The background shading is disproportionately distracting, considering what little information it encodes.
That said, I can see maybe it would help when you are stacking multiple of these on top of each other and each has its own qualitative ranges.
This actually makes a lot more sense now. I was trying to figure out how this wins in comparison to a standard bar graph with markers. It says information density in the OP, but it never gives an example of how it consolidates information without obscuring it.
i dont see that as a good data visualization. why you might ask? because it's not self explanatory. while the target bar might be clear, the poor, average and excellent value feature a different style, while containing similar information. also it's not possible to have 2 bars at the same height, which might not be a problem, but the example they are showing has exactly this problem.
That's not the fault of the chart type. It's just a bad execution in that blog post. I.e. the shorter bars should be very light, and feel more like a backgrund for the actual bar.
It doesnt make the graph any clearer in my opinion. the graph uses 3 different styles of value indicators. the background colored areas, the bar itself and the "target indicator".
discarding the "target indicator" and using intuitively understandable colors (red/yellow/green) for the background colors might help, but othervise i dont see this as a easily readable graph style. especially it cannot be read without prior explanation.
If the graph is being used regularely within a company or group it may well be used.
As a research scientist, I can not tell you how many scientists seem to have watched a few TED talks too many and desperately try to be interesting, never actually telling me the essence of what they've done instead of some analogy or a pretty picture.
That being said, I don't disagree with you. The point is to know your audience and your goals. Trying to be interesting is great for a lay audience, as are long derivations for a seminar of your peers. Just don't confuse the two.
I think the best way is 3-5 word bullet points, but mostly talking about tables/charts/figures. If you've truly done the research yourself, you can easily explain what is behind every chart.
Really depends on the content. If I'm being presented with a mathematical theorem, for instance, I'd like to read the exact statement at my leisure while the speaker reads it and then talks about it. Structuring derivations can also be tricky (omit too many technical details and risk losing the interesting part of the work). Charts are fine, but for an audience of scientists the methodology behind the result is often more interesting than the result.
But it manages to actually show less information. Sparklines are nice because you keep the time axis so we know if the current metrics is improving or decreasing.
Or, in other words, nothing like a sparkline. This visualization is useful for gauge-like displays. Sparklines suck at communicating scale or proportion or context if the context is anything other than recent historical values. I could see a sparkline — or better yet, a simple yet properly labeled line graph -- complementing a bullet chart to show historical data. If something can effectively complement another thing, then they probably are not basically the same thing.
A lot of places I have worked at would love this to show a KPI in a simple manner, and they didn't care about the time axis and this type of slide was common:
KPI #13
2018: 124
2019: 125 (A BIGGER NUMBER THAN 2018, YAY!)
Proven? How? By what standard or metric? And for any business dashboard data vis? What does that even mean?