information communication
Authors
Yichuan Hu is a consultant in the Advisory Practice, Ernst & Young, Hamburg Gintaras Hinz is a consultant in the Advisory Practice, Ernst & Young, Hamburg
Business data visualization
adds nothing to the clear and accurate comprehension of the data, then it is not information visualization. Most of the IT-supported visualization tools or dashboards that are advertised in popular business intelligence (BI) software fall far short of the basic requirement to communicate efficiently and effectively. Nevertheless, the development of effective visualization tools is not the focus of software solution vendors.2 The
dashboards they provide are frequently decorated with shimmy designs, which add no value to the purpose of communication. As a result, corporate practitioners are frequently victims of the fallacy of data visualization with serious consequences.
What can go wrong?
Effective data visualization is more science than art, but, above all, it is about good communication. But this goal is undermined by weak visual design which only serves to diminish the usefulness of business data visualization.3
and store it increases, but our ability to make sense of and communicate it to other interested parties remains inert.
Information visualization can be dated back to the beginning of 19th century. It is about helping people understand data, with the eventual goal of making better decisions. The need for information to be illustrated visually has manifested itself recently in the form of a dashboard fever. Dashboards are reporting mechanisms that deliver business intelligence (BI) in a graphical form, which are one of the key characters of a modern business intelligence platform. Over the last few years, the BI concept has overtaken corporate executives who are eager to turn enormous amounts of data into knowledge. Whole industries have been created as a result of this trend, with even the traditional enterprise solution providers, like Oracle and SAP, offering new business data visualization capabilities.
being tend to attach less importance to anything that lies beyond their immediate field of vision.
Not enough detail or too much Insufficiency and excess are two sides of the same coin: each can seriously impair the impact and usefulness of data visualization. A lack of detail can means insufficient context for the visual information. Context enlightens the reader and can inspire action as it illustrates the numbers. The key objective of dashboard visualization is to provide information that gives the viewer a quick overview. That said, excessive detail is overkill in these circumstances. Choosing inappropriate or poorly designed display media
One of the most frequent mistakes in data presentation is the use of inappropriate display media. Data communication expert Stephen Few writes, “If a dashboard fails to tell you precisely what you need to know in an instant, you’ll never use it, even if it’s filled with cute gauges, meters, and traffic
25 a particular data set to best communicates
the intended message is a skill that does not come naturally to many people If you are asked to rank the average corporate image of three companies using a radar chart (see Figure 1), can you instantly give a clear answer? Although the radar design is “cutting edge”, a bar chart or just a simple plain table can communicate the message just as effectively.
In this example, a radar chart is not the best fit for this type of data and purpose. Instead, it unnecessarily complicates an otherwise simple message. As long as an overall rating is concerned, a plain simple table does a very good job to communicate clearly (see Table 1).
Moreover, the radar chart has been poorly designed. The legend takes up nearly one-third of the size of chart, forcing the reader’s eye to bounce back and forth. The poor choice of colors causes the eye to strain to distinguish the different elements. We will look at more common design problems later in this article.
Encoding quantitative data inaccurately Poorly designed graphical representations of quantitative data could potentially display inaccurate values or mislead the reader. The problem can be insidious, but the impact of visual distortion of key data could have a negative impact.
Table 1. Rating summary
Rating areas Company A Company B Company C
Brand reputation 4.0 4.5 2.5 Tech support 3.5 2.0 5.0 Community awareness 4.0 2.0 4.5 Employment practices 4.0 4.0 2.5 Ethical practices 3.0 5.0 2.0 Product quality 3.0 1.5 3.0 Customer focus 3.5 4.5 4.0 Future orientation 4.0 3.0 2.0 Average rating 29.0 26.5 25.5
Figure 1. A ranking puzzle
Source: Ernst & Young analysis
0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 Brand reputation Tech support Community awareness Employment practices Ethical practices Product quality Customer focus Future orientation Company A Company B Company C
Business data visualization
The bars in the graph on the next page (see Figure 2) represent planned revenue and actual revenue. At first glance, they suggest that the actual revenue more than doubled the planned figure in the first and fourth quarter. But careful examination of the chart scale reveals that the scale on the vertical axis does not start at zero.
Against such a confusing backdrop, effective communication often fails to even make the list of key criteria for effective graphics, according to Data Communication expert, Stephen Few. A recent survey conducted in Italy revealed that one-quarter of key performance indicator graphs are materially distorted; graphical alterations that are favorable to those who use them are relatively more frequent than those
unfavorable graphics; and financial graphs exhibit slope parameters that depart materially from the optimum.4
Clogging up the charts with too many colors and ineffective elements
A common error that occurs in the creation of business performance visualization is the over-design of graphics with meaningless elements and poor color combinations.
In most cases, the maxim “less is more” applies to data visualization. A cluttered
“Outstanding people have one thing in common: an absolute sense of mission.” Determining the mission of data visualization should also be the first step towards effective communication. Data visualization is not just about converting the numbers into graphs, careful consideration should be given to arranging the visualization in a way that best suits a particular communication objective or mission. You may want to present overall strategic fit to a board of executives. Or maybe your objective is to let the engineers be instantly aware that the robotic arm on the manufacturing assembly line runs out of bolts. Always know what you want to say and who your audience is?
Identify the data and determine the type of chart
Before you can communicate the data, it is useful to take some time to understanding what it means, how it is structured and what is important to your audience or target group. Frequently, business messages are encoded in four basic common relationships. Each of them has its own “best-fit” types of chart.6
chart forces the viewer to spend more time processing the visual content before he can understand the data itself. Colors should be chosen with care, as they can affect our memory, judgment and learning. When you look at a dashboard, your eyes should be immediately been drawn to the information that is most important. If the color choices are weak or the chart is cluttered with too much unnecessary information, the information that deserves prominent presentation may be easily overlooked. It is also important when using colors, to give a thought to the approximately 10% of Source: Ernst & Young analysis
0 100000 200000 300000 400000 500000 600000 Q1 2009 Q2 2009 Q3 2009 Q4 2009 Planned revenue Actual revenue
27 Nominal relationship
1.
Nominal scales consist of discrete items that belong to a common category, but really do not relate to one another in any particular way. Sales regions are typical nominal scales, e.g., Europe, Asia, America, North, South, etc.(see Figure 3) These are simply names or tags, without any particular sequence. But it is often necessary to present a certain ranking or a structure of nominal values. A bar chart or point chart will usually suffice.
Pie charts are commonly used to present parts of a whole. However, they do not display quantitative data very effectively, nor do they rank the information. The weakness of pie chart is that data is encoded in two-dimensional areas. Angles are used in pie chart to illustrate the proportional relationship. The problem is, our eyes do not process information in this
Figure 3. Nominal relationship
Source: Ernst & Young analysis
0 20 40 60 80 100 120 140 160 Europe America (North) Asian America (South) Australia Africa Sales regions (Mio. €)
Business data visualization
format easily. Nevertheless, if you want to show a very simple structure, for instance, the percentage distribution of two variables that add up to one hundred per cent, a pie
chart should demonstrate this information effectively. But for complex rankings or structures, it is better to encode your information with bar graphs.
messages are emphasized if you use line charts or dot charts rather than a bar graph. It is the overall shape of distribution is that you want to emphasize.
are the most intuitive ways of encoding Time-series information.
A word of caution however, when displaying Time-series data using line charts on a dashboard, it is the shape of the data, rather than the emphasis on individual values, that is the picture that needs to be painted. A line chart will provide a quick overview. If you want to highlight the individual values or enable a close comparison of values that are located next to one another, a bar chart would be better.
Figure 4. Time-series relationship
Sales 2009 (Mio. €) 0 200 400 600 800 1000 1200 1400 1600 1800
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
and persistent is
the flood of paper
29 As well as displaying the general direction, a scatter plot shows the degree to which two paired sets of data are correlated. In practice, people frequently use a straight trend line, the so-called “line of best fit” in a scatter chart to make the direction and strength of the correlation stand out. However, when this type of chart is used, the needs of the viewer must be addressed, as most will not have the specialist knowledge required to interpret them. It is better to use the simplest straight line of best fit unless you and your target group have the necessary statistical knowledge to understand the other complex forms. The four data relationships listed here are not exhaustive. The general principle, however, remains unchanged. You need to be able to understand the structure of the data, whatever it may be, before you can talk about visualization. The following chart selection matrix summarizes the discussion above and is designed to support you in the selection of encoding media (see Figure 7).
Liberating structures
Figure 5. Frequency relationship
Source: Ernst & Young analysis
0 2 4 6 8 10 12 1 3 5 7 9 11 13 15
Figure 6. Correlation relationship
Source: Ernst & Young analysis
0 2 4 6 8 10 12 33500 34000 34500 35000 35500 36000 36500 M on th s Costs
Business data visualization
Correlation relationship
4.
Correlation illustrates significant relationships between pairs of quantitative values, each measuring a different element of an entity (see Figure 6). Correlation makes selective use of historical values to assist in forecasting. Most data analysis software packages available on the market today can generate correlation scatter charts automatically. When used correctly, correlation is a powerful tool in the provision of valuable insights.
Source: Ernst & Young analysis Key words • Fall • Rise • Change • Fluctuation • … 0 200 400 600 800
Jan Feb Mar Apr MayJun Jul Aug Sep Oct Nov Dec
Key words • Go down with … • Go up with … • Varied with … • … Correlation relationship Key words • Dispersion • Frequency • Structure • … 0 2 4 6 8 10 12 1 3 5 7 9 11 13 15 0 2 33500 34000 34500 35000 35500 36000 36500
Costs Determine the message
Sophisticated software is not
a substitute for well-designed
data visualization
purpose. In a business with different departments and units, it is important to establish a company-wide visualization standard. A diversity of chart types may dilute the message you are trying to communicate. In order to build up a big picture of the business, the visualization across the company should be uniform and easy to interpret.
Keep to basic chart formats and avoid
•
using complex graphics unless they truly add value. Everything can be reduced to the essential elements and rearranged into a logical sequence to achieve a particular goal, using particular elements and color combinations. If you have something to say, say it clearly and accurately. When you create a graph, you have a choice to make — to communicate or not. That is what it comes down to.
Determine illustration details After defining the key message and selecting the most appropriate encoding graphic, the final step is dedicated to the details of your data illustration. We have already mentioned several common pitfalls found in common commercial BI data visualization solutions. Multiple pages, inappropriate details, scale deterioration and poor color selection are just some of the problems. The devil is in the detail, so in designing the dashboard, unnecessary non-data information should be kept to the minimum and focus should be on the message encoded. It is best to avoid using 3D designs in graphics, or grid lines in the bar graphs, and to stay away from excessive decoration in the background. Certainly, sometimes it is necessary to use different sizes, colors or symbols to distinct certain value with the rest of the data set. In other circumstances,, unnecessary visual distractions should be eliminated.
Summarize the ideas
Business data visualization is an integrated and important part of today’s business intelligence platforms. However, sophisticated software is not a substitute for well-designed data visualization, which really gives you the big picture of what’s going on in your company.
To create effective business data visualization, there are three stages:
The mission statement is first. Every
•
chart must have a message. Messages can be statements, recommendations, warnings or explanations over certain business facts. Think from the viewer’s perspective: what is important to them. Spend some time deciding the
•
underlining structure of the data and what relationships there are. Decide which type of chart best fits your
