Note: Originally, we wrote this article following an invitation from Survey Magazine. It was first published in this quarterly magazine dedicated to the world of studies and marketing research in November 2019 before being presented on our site.

Scientists agree that visual information is processed 60,000 times faster by the brain than textual information. One could debate on the precision of this figure but one realizes it intuitively: an image is better than a long speech. Quite equivalently, there is no doubt that a DataViz is better than a long report.

DataViz challenges and opportunities

What is DataViz and why?

For those who have missed several issues of Survey Magazine, DataViz is the contraction of two words: Data and Vizualization.

It is a set of visual communication tools and techniques that allow information to be presented clearly and effectively. In French, we would translate this term by data visualization and we could reduce its meaning to the graphs which represent them.

However, in the information age, the Internet of Things (IoT) and Big Data, these techniques serve above all a philosophy: to give meaning to the immense quantity and the frightening complexity of the data available. through an apparent simplicity.

As if that were not enough, this data and knowledge have a much shorter life expectancy than before. They must be used faster, at the risk of becoming obsolete.

Declarative data vs behavioral data

As market research professionals, we are obviously not the biggest data producers in the world, far from it. However, we are still producers of data regardless of our method of collection. Furthermore, the data that we create through our market research and surveys are not really comparable to all the data that we can see in the visual above. When a web giant like Facebook or Google collects data, it does so transparently for the “respondent”, in the sense that the latter does not have to answer any questionnaire, this behavioral data is a measure reality.

Our role as analysts-aggregators

So an analysis of Google Trends trends can reveal quite surprising realities because the person who searches on Google will deliver to this engine things that he would not deliver to any human being. Our collection instruments, especially the declarative survey, are more often the reflection of an opinion than of behavior. Obviously, we have to learn to use all this data that we do not produce, but that is not the question. Where the data giants draw their insights from quantity, our role will tend towards analysis-aggregation. This is where the DataViz tools that connect to multiple different data sources make sense.

Empowering the expert-client with self-service data

We may be experts in analysis, but we are generally not experts in any of our clients’ businesses and we probably do not intend to be or to become one. When a client comes to us, their need is usually to make one or more strategic decisions for their business. Our role as analyst-aggregator will be to provide it with all the information necessary so that this decision is fair or at least as fair as possible.

Facilitate decision-making

For a satisfaction survey, it will be a question of deciding on the actions to be taken to improve it. If he wants to study the size of a market and its trends, it may be to make the right investment decisions. For a new concept, it could be to decide whether to launch a product or even start a business, and so on. Data is his need but it is decision making that remains the objective. Good decision-making requires business expertise, which is why we have no choice but to work with experts when it comes to a particular sector and sometimes even for data collection!

DataViz = self-service data!

As experts, the aim of our work has always been to facilitate this decision-making. This requires, above all, the provision of reliable data which makes it possible to eliminate, at best, the effects of chance. This also involves empowering the client on the aspects of data exploration and analysis. On this aspect, we also have a training and facilitation role which ideally begins with the presentation of our results. Now is the perfect time to demonstrate the strengths of DataViz and self-service data. Ultimately, this will also free up our teams, in particular those responsible for studies, on unnecessary return trips with our customers.

Today, the deluge of data is there, and the DataViz is certainly one of the key instruments that can serve as a bulwark of confusion. How? ‘Or’ What ? By putting the immeasurable amount of available data within the reach of our human cognitive capacities. For us, this is also an opportunity to put our statistical tools and indicators within the reach of our customers and therefore for a new generation synergistic business analysis.

Build DataViz for surveys

When we say DataViz, we first think of the proper use of the graphic elements that compose them, it is obvious. Because yes, in Data Visualization there is the word visualization. That’s right, there are graphs more suited to certain types of data. We have known this for a long time and the DataViz philosophy reminds us to order, but there is much more important than the simple choice of a graph.

In the word DataViz nothing evokes it and yet it is one of its main components, it is about interactivity. Without it, we would never have invented a new concept for making graphics with a new philosophy. It is this interactivity that is opening the doors to the new opportunities offered by DataViz. It offers the infinite possibilities of a new dimension: the exploration of data and their manipulation by the end consumer of the data.

When building a data visualization, it is therefore imperative to adopt an interactive mindset. As you will soon realize, this article itself would probably have benefited from being more interactive.

Explore without limits but in simplicity

Why have multiple graphs for different demographic crossovers when you can have everything in one visual? Then just add filters around to limit the results to a geographic region and / or an age group. A well thought-out DataViz can provide access to a huge amount of data through simple visual elements.

Gamified dashboards

However, be careful not to get lost, simplicity is essential. Interactivity must bring power and gamification (gamification) but not complexity. For this we can use dashboarding techniques, that is to say organize information as we would present the KPIs to a decision maker, all on a single screen so that the end user can in the blink of an eye have a view general information. In general, a good interactive DataViz must provide answers in its overview. Interactivity can be used to deepen but it should not confuse the information presented.

Filters can obviously take the form of drop-down lists, radio buttons and other check boxes, but we can also be more imaginative.

A DataViz to measure satisfaction

Here is an example from one of the latest DataViz published in our Market Insights blog which concerns the satisfaction of Moroccans about their holidays in summer 2019:

Each graphic element becomes a filter. Click on the yes in the sector graph, then on the image representing women and finally the one representing the first age group of 18-24 year olds and the results displayed in terms of satisfaction no longer represent this demographic target. The use of images can facilitate contextualization, serve understanding and make exploring the results more fun.

Everyone can be a statistician

Another opportunity offered by DataViz is access to statistical tools and above all their simplification. Thus, any neophyte can use and exploit the analytical and predictive power offered by statistics. We can obviously use the average, median and standard deviation classics with reference lines or mustache boxes, which already allows us to do a lot, but we can also go further.

The key is interactivity

With the help of interactivity and a little imagination, we can allow the client to find himself correlations between the data by calculating for example on the fly the Pearson coefficients, By modifying the data on the fly , it also becomes possible to quickly compare trends from linear or multiple regression calculations.

In the context of a survey, a simple and interesting example is the dynamic display of the margin of error according to the variant sample size. Thus, the end user keeps an estimate of the accuracy of his information throughout the exploration.

If we take the example of DataViz above, in the reliability indicator section, we can see the margin of error evolve as the sample is refined. The user of the DataViz can know in real time whether the results displayed show significant differences or not. This is a good example of how we can empower the client on the right exploration of their data while making the process more fun and playful.

It should be remembered that statistics is not an exact science. In view of recent innovations in data analysis, there is a good chance that it will evolve significantly in the years to come.

Imagination at the service of data

Finally, do not hesitate to be imaginative by combining graphics and images or even inventing new ways to visualize data. There is still plenty of room for innovation and creativity. We already know data visualizations that take the form of videos like these famous rankings of market capitalization in bars that we can see evolving over time.

However, the [near] future will certainly lead us to even more surprising data explorations. By using tools like virtual reality for example, like Minority Report by Steven Spielberg, pushing the concept of data you can touch to its climax.

Renewed ethical considerations

When we consciously highlight certain data, this is in order to facilitate the client’s work. He is the expert in his profession, it is he who must make the decisions that arise from our studies. However, we are responsible for this data and this is even more the case when it comes to DataViz which is a very short form of the results of our work.

Simple but accurate

If the DataViz allows us to see more clearly in the deluge of data, it is also because we intentionally omit part of the data or at least highlight one part rather than another. As market research, data analytics and statistics professionals, we are most able to sort this out. In order that insignificant data is not misused, we must be transparent and clear about the significance of the data we present.

Human limits defined by our cognitive biases

We need to fight more against all forms of cognitive bias. For example, you have to master the scales of the graphs and keep the same when comparing several variables. We also need to think about our choices of shades or color gradations so that they adequately reflect the data. We also need to control the effects of interactivity, especially in extreme cases to avoid portraying overly exaggerated situations. Finally, we must never forget the audience to which we present our results because the interpretation of colors and shapes can sometimes change drastically from one culture to another.

We then have an educational role vis-à-vis our customers on these new technologies. When the DataViz is well exploited, our autonomous client is empowered to explore the data himself to gain the necessary insights. Its conclusions can then be presented to many other people, all influenced by the choices we made upstream.

A beautiful story yes but no fight between good and evil

When telling a story through a DataViz or even a computer graphics, it is often necessary to introduce a narration, disturbing elements and adventures. This is how you can transform a bit boring data into an interesting or even fascinating story. It is then up to us to know how to remain neutral and try as much as possible not to tip our stories towards too Manichean patterns. What we choose to say or show through a DataViz is ultimately the result of our beliefs. It is only by being aware of these aspects that we can control them and assume the ethical responsibilities intrinsic to our business and to DataViz.

Riad Mawlawi, Digital & Business Development Director at Sunergia