From the past posts, you would have gathered that dashboards are tools to solve specific problems. They are also formed from individual charts and data elements.

Selecting information

That dashboards are so specific is great, because the problem that they are designed to solve will help choosing the information that we need and also prioritizing it – two essential tasks in dashboard creation. Again, we don’t want to shove every data point we have.

Another great tool to help us do those two tasks is user research. As a designer, we may think we chose the right metrics, but they have to make sense to real users and resonate with them. The bias that we may have is that we would favor data which is easy to obtain or that makes sense to us, compared to data which can be more elaborate, more sophisticated or more expensive to collect or compute, even if that makes more sense to the user.

Here’s an illustration of that.

When I was working at Facebook on this product, Audience Insights, we designed this page to help marketers understand how a group of users they could be interested in used Facebook. (The link / screenshot showcases fans of the Golden State Warriors). One of the main ways we classified users at Facebook, for internal purposes, is by counting how many days of the last four weeks they have been on Facebook. It’s a metric called L28 and one of the high-level things Facebook knows about everyone. So, we integrated it in the first version of this page. But, even though it’s not a concept unique to Facebook, it wasn’t that useful to our users, and it was taking space from a more relevant indicator.

Instead, we have included indicators which are more relevant to the task at hand (ie getting a sense of who those users are through the prism of their activity). For instance we can see that very few Warriors fans only use Facebook on their computer, compared to the general population of US Facebook users. They tend to skew more towards Android and mobile web (going to www.facebook.com from their phone, versus using an app.) They tend to be more active in terms of likes, comments and shares.

Information hierarchy

Once information is chosen and you get a sense of what is more important than the rest, it’s time to represent that visually.

Here are some of the choices you can make.

Show some metrics on top or bigger than others. 

That’s probably the first thing that comes to mind when thinking hierarchy and prioritization. And it needs to be done! Typically, you should get one to three variables that really represent the most important thing you want your users to read or remember. If you come up with more than 3, you should refine your question/task and possibly split it in two.

The rest of the variables will support these very high level metrics. Again, in a typical situation, you could come up with up to three levels of data (with more than three being a good indication to rethink your scope). Some metrics can support the high-level metrics (i.e. show them with a different angle, or explain them) and some metrics could in turn support them.

Present some metrics together. 

Stephen Few argues that dashboards should fit on one page or one screen because their virtue is to present information together. With the flexibility offered by the modern web, and the size constraints of mobile, this is a requirement that shouldn’t be absolute. But it’s relevant to remember that some variables add value when seen along other variables. With that in mind, you can have part of your dashboard as a fixed element (always visible on screen) while the rest can scroll away, for instance.

Push some metrics to secondary cards (such mouseovers, pop-ups or drill-down views)

Hierarchizing information is not just about promoting important information. It’s also about demoting information which, while is useful in its own right, doesn’t deserve to steal the show from the higher level metric. The great thing about interactive dashboards is that there are many mechanisms for that. Some information can be kept as “details on demand” and only shown when needed.

Figure out what form to give to the data

So you have data. It probably changes over time, too (and you have that history as well!). And a sense of how important it is.

You can represent it as a static number (and, further, to adjust the precision of that number) or as a time series (i.e. line graph, area graph, bar graph etc.), or both.

The key question to answer is whether the history and how the metric moved over time is relevant and important, versus the latest figures.  If you think that the history is always important or that it doesn’t hurt to have it for context anyway, consider that it’s yet another visual element to digest, another thing that can be misinterpreted, and that unless its importance is clearly demonstrated, you’d rather not include it. Yes – even as a tiny sparkline.

Here is another example from my work at Facebook of a page where proper hierarchy has been applied.

Page Insights, to use a parallel with a better known product, is like google analytics, only for Facebook Pages instead of web sites. Unsurprisingly, the metric we put to the top left is the Page Likes, which is the number of people who like a page. The whole point of the system is to let people understand what affects that number and how to grow it. Two other high-level metrics are shown on the same row in the two cards on the right: the Post Reach for the week (number of people who have seen content from this page this week, whether they like the Page or not) and Engagement (number of people who acted on the content – actions could be liking, commenting, sharing, clicking, etc.)

The number of new Page Likes of the past week, which is represented as a both a line chart and a number in the left card, is an example of a level two metric. It supports the top metric – total likes. The number of Page Likes of the past week, which is represented as a line chart only, is a level three metric. It’s here just as a comparison to the number of the current week – here, it helps us figuring out that last week has been a better week.

Connecting the dots

Ultimately, a dashboard is more than a collection of charts. It’s an ensemble: charts and data are meant to be consumed as a whole, with an order and a structure. Charts that belong together should be seen together. The information gained like so will be much more useful than from looking at them in sequence.

Linking, for instance, is the concept of highlighting an element in a given chart with repercussions on other charts, depending on the element highlighted. A common use case is to look at a data for one given time point, and see the value for that time point highlighted in related charts. Here is an example:

In this specific case, the fact that both charts share the same x-axis makes comparing the shape of both charts easier even without linking.

Each variable doesn’t have to be on its own chart. Your variables can have an implicit relation between one another. Bringing them together might make that relation explicit. Here are some interesting relationship between variables or properties of variables that can be made apparent through the right chart choice.

• One variable could be always greater than another one, because the second is a subset of the first. Here are some examples:

1. The number of visits on a website last week will always be greater or equal than the number of unique visitors that week, which will always be greater than the number of visitors last day.
2. The number of visitors will always be greater to the number of first-time visitors.
3. The cumulative number of orders over a period of time will always be greater than the number of daily orders over that same period.
4. The time that users spend with a website in an active window of their browser will always be greater than the time they spend actively interacting with the site.

What’s interesting here is that these relations are not just true because of experience, they are true by definition. It’s also metrics that are expressed in the same units, and, in most cases, with the same order of magnitude, so they can be displayed on the same chart. When applicable, showing them together can show how they, indeed, move together or not.

• One variable could be the sum of two other, less important variables.

In the example below we go even one step further and we show that one variable is the sum of two variables minus a fourth one.
Here, we look at the net likes of a Facebook Page, that is, the difference between the number of people who like a page on a given day and the day before.
Two factors can make more people like a page: paid likes (a user sees an ad, is interested, and from it, likes the page) or organic likes (a user visits a page, or somehow see content from that page, and likes it, without advertisement involved). Finally, people may also decide to stop liking the page (“unlikes”).
Here, net likes = organic likes + paid likes – unlikes. The reason why we have decomposed Likes between organic and paid is because we wanted to show that ads can amplify the effect of good content. So, visually, we chose to represent that as a layer on top of the rest. (important remark: your dashboard doesn’t have to be neutral. If it can show that your product, company, team etc. is delivering, and you have an occasion to demonstrate it, don’t hesitate a moment). By showing the unlikes as a negative number, as opposed to a positive variable, going up, possibly above the likes (which would be unpredictable) we can keep the visual legible and uncluttered. A user can do the visual combination of all these variables. This chart, by the way, shows the typical dynamic of a Page : new content will generate peaks of new users, but also will cause some users to stop liking the page.

• One variable could be always growing. Or always positive.

When that is the case this can be used to make choices to represent the chart. If a variable is always growing by nature (i.e. cumulative revenue) you may want to consider representing a growth rate rather than the raw numbers. A reason to consider that is that your axis scale will have to change over time (i.e. if you plot a product that sells for around $1m per day, having an axis that goes from 0 to $10m would be enough for a week, but not for a month let alone for a year, whereas with a growth rate you can represent a long period of time consistently). And if a variable is always positive (ie stock price), your y axis can start at 0, or even at an arbitrary positive value, as opposed to allocate space for negative values.

Conversely, if a variable doesn’t change over time, it doesn’t mean that it’s not interesting to plot. That absence of change could be a sign of health of the system (which is the kind of task that dashboards can be useful for). So the absence of change doesn’t mean that there’s an absence of message.

In the past few articles I’ve exposed what dashboards are not:

• an exercise in visual design,
• an exercise in data visualization technique.

Another way to put this is that “let’s do this just because we can” is a poor mantra when it comes to designing dashboards, or visualizations in the broader sense by the way.

Do it for the users

Now saying that dashboards should be products is a bit tautological. Products, in product design, refer to the result of a holistic process that solves problems of users – a process that includes research, conception, exploration, implementation and testing.

Most importantly, it’s about putting the needs of your users first. And your users first. Interestingly, treating your dashboard as a product means that the dashboard – your product – doesn’t come first.

Creating an awesome dashboard is a paradox. Googling for that phrase yields results such as: 20+ Awesome Dashboard Designs That Will Inspire You, 25 Innovative Dashboard Concepts and Designs, 24 beautifully-designed web dashboards that data geeks or 25 Visually Stunning App Dashboard Design Concepts. This is NOT dashboard product design (though it’s a good source of inspiration for visual design of individual charts).

Eventually, no one cares for your dashboard. When designing a dashboard, it’s nice to think that somebody out there will now spend one hour everyday looking at all this information nicely collected and beautifully arranged, but who would want to do that? Who would want to add to their already busy day an extra task, just to look at information the way you decided to organize it? This point of view is a delusion. We must not work accordingly.

Instead, let’s focus on the task at hand. What is something that your users would try to accomplish that could be supported by data and insights?

What is the task at hand?

If you start to think “show something at the weekly meeting” or “make a high-level dashboard” I invite you to go deeper. Show what? a dashboard for what? not for its own sake.

Trickier – how about: “to showcase the data that we have”? That is still not good enough. You shouldn’t start from your data to create your dashboard, and for several reasons. Doing so would limit yourself to the data that you have or which is readily available for you. But maybe that this data, in its raw form, is not going to be relevant or useful to your users. Conversely, you would be tempted to include all the data that you have, but each additional information that you bring to your dashboard would make it harder to digest and eventually detrimental to the process. Most importantly, if you don’t have an idea of what the user would want to accomplish with your data, you cannot prioritize and organize it, which is the whole point of dashboard design.

Finally – “to discover insights” is not a task either. Dashboards are a curated way to present data for a certain purpose. They are not unspecified, multi-purpose analytical exploration tools. In other words: dashboards will answer a specific, already formulated question. And they will answer in the best possible way, if they are designed as such. For exploration, ad-hoc analysis is more efficient, and is probably best left to analysts or data scientists than end users.

Here are some example of tasks:

• check that things are going ok – that there is no preventable disaster going on somewhere. For instance: website is up – visits follow a predictable pattern.
• Specifically, check that a process had completed in an expected way. For instance: all payments have been cleared.
• If something goes wrong, troubleshoot it – find the likely cause. For instance: sales were down for this shop… because we ran out of an important product. Order more to fix the problem, make sure to stock accordingly next time.
• Support a tactical decision. For instance: here are the sales of the new product, here are the costs. Should we keep on selling it or stop?
• Decide where to allocate resources. For instance: we launched three variations of a product, one is greatly outperforming the other two, let’s run an ad campaign to promote the winner.
• Try to better understand a complex system. For instance: user flow between pages can show where users are dropping out or where efficiency gains lie.

This list is by no means limitative. But it’s really useful to start from the problem at hand than just try to create a visual repository for data.

Next, we’ll see how to implement these in the last article: charts assemble!

Dashboards are extreme data visualizations

In the recent Information is Beautiful 2014 awards,  I found interesting that there is an infographics and a data visualization categories. My interpretation is that the entries in the infographics section are static and illustrated, while those in the data visualization are generated and data-driven. However, all the featured data visualization projects are about a one-off dataset. So aesthetical choices of the visualization depend on the characteristics of this particular dataset. By contrast, the dashboards I have worked with are about a live, real-time datastream. They have to look good (or at least – to function) whatever the shape and size of the data that they show. The google quote and news chart that we saw earlier must work for super volatile shares, for more stable ones, for indices, currencies, etc. So, if the distinction between infographics and data visualization makes sense to you, imagine that dashboards sit further in that continuum than data visualization. Not only are dashboards generated from data, like data visualizations, but they are also real-time and should function with datasets of many shapes and sizes.

But dashboards problems are not data visualization problems

Data visualization provides superior tools and techniques to present or analyze data. With libraries and languages dedicated to making visualizations, there is little that can’t be done. In many successful visualizations, the author will create an entirely new form, or at least control the form very finely to match their data and their angle. Even without inventing a new form, there are many which have been created for a specific use, and which are relatively easy to make on the web (as opposed to say, in Excel): treemaps, force-directed graphs and other node-link diagrams, chord diagrams, trees, bubble charts and the like. And even good old geographic maps.

In most cases, it is not a good idea to be too clever and have a more advanced form.

Up until mid November 2014, Google Analytics allowed users to view their data using motion charts.

This was really an example of having a hammer and considering all problems as nails. Fortunately, this function disappeared from the latest redesign.

Likewise, on twitter followers dashboard, the treemap might be a bit over the top:

and possibly confusing and not immediately legible to some users. On the other hand, it is economical in terms of space and would probably work in almost every case which are two things that dashboards should be good at. So while I wouldn’t have used it myself I can understand why this decision has been made.

Dashboards are not an exercise in visual design either

A dashboard such as this:

(for which I can’t find the source. I found it on pinterest and was able to trace it to this post but not prior) is well designed visually, it makes proper use of space, colors and type, its charts are simple.

But what good is it? what do I learn, what can I take away from it, what actions can I perform?

Most of the dashboards examples I find on sites like dribbble or beyance (see my Pinterest board) fall into that category: inspiring visual design, probably not real data, no flow, no obvious use.

Dashboards are problems of their own

What makes a dashboard, or any other information-based design successful, is neither the design execution nor the clever infovis technique. Dashboards, eventually, are meant to be useful and to solve a specific problem.

How so? We’ll see in the next article: dashboards as products. 

In 2008, when I was working at OECD, my job description was that of an editor. That implied I was mostly working on books. I was designing charts, but they were seen as components of books. And this was typical of the era.

So we would create charts like this one:

And it was awesome! (kind of). I mean, we respected all the rules. Look at that nicely labelled y-axis! and all the categories are on the x-axis! the bars are ordered in size, it’s easy to see which has the biggest or smallest value! And with those awesome gridlines, we can lookup values – at least get an order of magnitude.

What we really did though was apply styling to an excel chart (literally).

Print charts vs interactive charts

Origin of rules for print charts

Rules that govern traditional charts (which are many: ask Tufte, Few) make a certain number of assumptions which are interesting to question today.

One is that charts should be designed so that values can be easily looked up (even approximately) from the chart. This is why having labeled axes and gridlines is so useful. This is also why ordering bar charts in value order is nice. With that in mind, it also makes sense that charts like bar charts or area charts, which compare surfaces, be drawn on axes that start at 0.

The other assumption is that a chart will represent the entirety of a dataset that can be shown at a time. We have to come up with ways to make sure that every data point can be represented and remains legible. The chart author has to decide, once and for all, which is the dataset that will be represented, knowing that there will be “no backsies”.

In the same order of thought, the author must decide the form of his chart. If she wants to compare categories, she may go for a bar chart. If she wants to show an evolution over time, for a line chart. And if she wants the user to have exact values, she will choose a table.

And so, when everything else than a table is chosen, we typically don’t show values with all the data points, because adding data labels would burden the chart and make its overall shape harder to make out.

In this framework, it makes sense to think in term of data-ink (the cornerstone of Tuftean concepts): make sure that out of all the ink needed to print the chart (you can tell it’s a print concept already…), as much should go to encode the data as possible, versus anything else.

How about now

However, there is not a single of these reasons which is valid today in the world of web or mobile charts. Data-ink only made sense on paper.

Web charts have many mechanisms to let the user get extra information on a given data point. That can be information that updates on mouseover, callouts and tooltips… This might be less true of mobile in general where the distinction between hovering and clicking is less distinct. But it is definitely possible to obtain more than what is originally displayed. If I want to have an exact value, I shouldn’t have to simply deduce that from the shape of the chart. There can be mechanisms that can deliver that to me on demand.

An example: Google Finance Quote & News

The Google Finance Quote and News chart is a very representative example of a web-native chart. Around since 2006, they provide the price of a given security, along with news for context. While its visual design has probably been topped by other dashboards, what makes it a great example is that it’s publicly available, which is uncommon for business data.

While this chart has gridlines and labelled axes, that is not enough to lookup precise values. However, moving the mouse over the chart allows the user to read a precise value at a given point in time. A blue point appears and the precise value can be read in the top left corner.

One very common data filter in chart is controls that affect the time range: date pickers. By selecting a different time range, we make the chart represent a different slice of the dataset – we effectively filter the dataset so that only the relevant dates are shown. This is in contrast with the traditional printed charts, again, where all of the dataset is shown at once. For instance, we can click on “6m” and we’ll be treated with data from the last 6 months:

Comparing the selected security with others will make the chart show the data in a different mode. This is the same data (plus added series), in the same screen and the same context, but the chart is visually very different:

As to the other two characteristics of web charts I mentioned, data exports and drill downs, they are also featured (but less graphical to show, so I haven’t captured a screenshot for those). There is a link on a left-side column to get the equivalent data (so it is always possible to go beyond what is shown on screen). The little flags with letters in the 3 first screenshots are clickable, and represent relevant news. Clicking them will highlight that article in a right-side column. So it is always possible to get more information.

What does that change?

Everything.

Rules or best practices based on the assumption that data is hard to lookup or to compare are less important. The chart itself has to be legible though. So, for instance, it’s ok to have pie charts or donut charts, as long as the number of categories doesn’t go totally overboard.

Web charts, and dashboards even more so, should focus on only showing relevant data first, then showing it in the most useful and legible way. Again, a noted difference with the print philosophy where as much data as possible should be shown.

How this play out is what we’ll cover in the next articles of the series: Dashboards versus data visualizations.