Visualise the data

Catie Williams, InEight Construction Software, USA, looks at the best practices for visualising data to help communicate information in a clear and effective way.

Several years ago, employees at InEight Construction Software were asked to implement a new cutting-edge set of visualisation tools for a big ERP implementation. At that time, most work that had been undertaken just involved reproducing existing Excel-based types of reports. After all, it was what people were most comfortable with and what they knew. It was traditional and seen as the way things had always been done. Whilst trying to gain buy-in and adoption from InEight Construction Software’s stakeholders, the system was met with resistance and a lack of trust for the visual style reports that the company made available. Over time, as comfort and maturity with the new system increased, tools such as Tableau® and Microsoft Power BI® were on the rise, and a shift started to form in the acceptance of using data visualisations for managing work.

Today, with the right data visualisation tools that provide customisable, interactive, even sharable dashboards, comprehensive visualisations can be built to truly help clients and projects in ways that connect people and systems with new transparency and understanding. The speed at which decisions can be made when presenting information in a visual format is staggering in comparison to what it takes going line by line through pages of numbers. But creating effective data visualisations, that require little explanation and immediately draw attention and provide insights, can be challenging. What follows are several best practices in creating data visualisations.

Colour and layout count

When it comes to the topic of colour in data representations, especially within today’s best dashboards, it can be tempting to use lots of it because it looks appealing and can seem impressive. However, if everything has colour then nothing will really stand out. Thus it is wise to use colour sparingly for the best impact. The question that should be asked is what story is the data report or chart trying to tell, and will it communicate the right information that the viewer needs to see? It should not be used just for the sake of using it, but only when there is meaning behind it.

For instance, when showing positive and negative values, what is the information that should pop out of that? If the information that the user should be aware of first is a negative number (so they can go and fix the issues represented by that number) then that is what should be seen first. In that case, having a bar chart or a tree map that has fairly muted colours, and something that catches the eye for the negative numbers, may be a good idea. The number of colours used should be limited, though, so the viewer is not overwhelmed.

On the contrary, say only greys are used in a chart. There will likely be an almost knee-jerk reaction from the viewer that this chart does not have the information

they need. So again, colour is key. It is also important to keep in mind that approximately 8% of men (one in 12) and 0.5% of women (one in 200) have some form of red-green colour blindness.1 This is the reason that blue and orange are common colours in some tools to represent positive and negative in place of the more traditional green and red.

Another very important visual element that should be kept in mind is how the viewer will read the chart. In the US, for instance, people read from left to right. Making sure to place the most important information in the top left and top right is therefore key. It is the most valuable real estate on a dashboard, and if there are only a few seconds to make an impression, that is a great place to start. Metrics that provide actionable information or require action are good contenders for occupying that space. The lower left and right can have more descriptive/informative information, but not necessarily a metric that requires immediate action. A good rule of thumb is to try out dashboards/visualisations on a non-practitioner and carry out some usability testing to understand where they are looking first, what is catching their eye, and what is not grabbing their attention.

Label for clarity

Often overlooked are the texts included with the visualisation, such as labels or annotations. For example, a bar chart could be used, however, if it does not have meaningful and understandable labels such as chart title, axis labels, or a legend, a lot of the context behind it can be lost. Thus, the data labels are very important, especially with positive and negative values. Labels should be kept clear and not too dense. It is also a best practice to include an annotation that is brief but describes what is happening in the chart very clearly. For example, “Sales have trended down 10% over the last three months” is a fast, easy way to tell the viewer exactly what they should take from the visualisation. The text supports the visual that is telling that story, without being overwhelming or requiring a significant amount of analysis or effort to read.

Positive and negative values can often be difficult to clearly label, so a beneficial trick for adding more clarity is using patterns and/or shapes. If a KPI dashboard has a user requirement that the KPIs are reflected as positive values, they could be made blue and a ‘+’ sign could be potentially included. Or if a project owner requires the use of red and green colours, the use of either an up or down arrow clearly indicates which is considered good vs bad. If negative numbers are used, the zero should be clearly marked.

Symbols or patterns can also be used to reflect different categories in a visualisation. For example, if a scatterplot visualisation with different work categories is being used, and it is important to draw the difference between categories to the user, different shapes, such as a circle for one, then a triangle or an X for another, could represent the different categories. This allows the user to quickly realise that for Category A, it is always represented by a circle, and Category B is represented by an X. Whatever is decided, the choice of symbols and labels should be consistent.

Let communication choose the format

Data visualisation is all about communication. As a result, what information is going to be to communicated should be thought about before the format is chosen. If an amount is to be communicated, it is very difficult for viewers to tell proportions by simply looking at an area, such as with a pie chart. People are much better at discerning differences from length than they are from an area. As a result, when an amount or a comparison is being communicated, bar charts can be very effective.

Then there are bullet charts. These essentially look at where something is now vs where it was or what it is aiming for, plus what percent it is towards achieving its goal. If a comparison is being made between the current and the prior year, or prior year-to-date, different shades of grey backgrounds can show progress based on a quarter, a month or a period. Bullet charts usually require a quick explanation of what is being compared, but then people understand them quickly going forward.

Sometimes too much information can muddy the waters, so the message should be kept clear and clean. A cumulative line chart can help with that and is something that can be combined with bullet charts. And if data is moving in a downward trend, a cumulative line chart can show very quickly where a real problem is, meaning this can be rapidly communicated out to stakeholders.

The construction engineering industry always involves interacting with multiple hierarchies. Tree maps can be an effective way to display large amounts of hierarchical information, for example, ‘piping and insulation’ could be the top level of the hierarchy, and each level could go into a lower level such as ‘aboveground piping’ and ‘underground piping’, etc. This hierarchy is a method for tracking costs and productivity. One of the codes could be drilled into and achievements or poor performance could be analysed. Looking across all projects, it is easy to see where the most issues are and a team can then be pulled together to determine if standard operating procedures need to be modified for that specific type of work, so it can start trending in the right direction.

Another method of data visualisation that may not be as well-known as some others, is a chord chart, which can be used to see how many issues a project may have by category. These categories might be RFIs, change orders, safety incidents, quality issues, etc. A chord chart displays important information quickly, as it immediately shows which project has the most overall issues and which category of work is the largest. It shows which project requires immediate assistance and where focus is needed by issue category as an entire company. A chord chart is a great way to see relationships between two data points quickly.

With today’s more advanced data visualisation tools, there is also a heightened level of interactivity. So, if one aspect of a particular chord chart should be the focus, a mark can be clicked on and it will highlight the peaks and then put everything else into the background.

One final word about pie charts. With these traditionally multi-coloured charts, it can be challenging for viewers to figure out which piece of the pie is bigger than another. This visual is best suited for information that is binary, meaning true/false or yes/no. Comparing slices of a pie with two variables is relatively easy, and exact percentages or values typically do not matter, only which is bigger or smaller. What is difficult with a pie chart is when data that has an infinite amount of possibilities is used, such as category or status. This makes it harder to control how the visual looks and is interpreted — and the last thing a company wants when building visualisations is for the interpretation to be left to the user. Each visualisation should be purposeful, with the goal being when two different people view a chart, they both arrive at the same conclusion.

Figure 1. Orange-coloured metrics indicate areas of concern, contrasting other components on screen and drawing attention there first.

Figure 2. Place metrics at the top of reports to draw users to the most critical information.

Understand growth and adoption

This article has covered what kind of formats to use and when, but there is a lot more to successful data visualisation than simply what things look like. It is the communication level that matters, and that is the one thing that data visualisation can do extremely well, but only with the right tools. There must be a shared narrative or a shared understanding. It is that shared understanding of what the information is saying that can help create a collaborative understanding across a project, especially for those not in the data field.

Data visualisation is also about growing a business. Virtually every organisation where data visualisation has been adopted and promoted has grown organically. And once people start seeing that growth and what is truly possible, they start getting excited because now, they own it. Like any new concept or technology, when it comes to getting stakeholders to embrace data visualisation, it is really about trust and a feeling of ownership. It will not happen overnight, but if some of the best practices discussed here are incorporated into data visualisation, trust can built day by day, project by project, and well into the future.

References

1. National Eye Institute, ‘At a Glance: Color Blindness, National

Institutes of Health, NIH National Eye Institute’, https://www.nei. nih.gov/learn-about-eye-health/eye-conditions-and-diseases/colorblindness.