Do you want to do more with your data? Data visualization literally makes your data visible. These tips will help you use data more efficiently when making important decisions.

Companies today have large amounts of data at their disposal. This data tells everything your business needs to know about how well you’re doing. Therefore, those who put data at the heart of business operations make the best decisions. Learning how to handle data efficiently gives you a significant competitive advantage in the long term.

The big challenge that many companies struggle with is letting the data do the talking. The art of making data accessible to a broad audience is called data visualization. Data visualization helps you separate the wheat from the chaff, giving you the data that delivers the most useful insights for your business on a silver platter. You can read about how to get started in this article.

From data to knowledge

The saying “data is knowledge” is often used to describe the added value of data visualization or to Business Intelligence to specify, but converting data into knowledge requires a whole process. After all, you are always initially dealing with large amounts of unstructured data from different sources. An Excel file with hundreds of rows and columns can make a data engineer’s mouth water, but the average worker will drown in it in no time.

By visualizing data, you ensure that everyone in the company can grasp the information overload. The human brain is visually oriented and is therefore better at remembering visual information. Whether you’re presenting to your company’s top executives, clients, or potential investors, people are far more likely to stick to your words when you come up with impressive charts than when you rattle off a bunch of meaningless numbers.

Data visualization helps you spot trends, patterns, and outliers in unstructured data. But be careful: the visualization of data does not just tell a story. You can make the most beautiful graphic, if the information displayed does not make sense, you will quickly fall through the basket. So be careful not to be exposed as a data charlatan.

Getting started with data visualization: How do you get more out of your data?

The benefits of data visualization should be clear to you by now. But how do you ensure that you continue to see the trees through the data forest? This is one of the biggest difficulties for companies starting with data visualization. We share some useful tips to bring data to life and make better decisions based on concrete insights.

What data do you need?

Before you start measuring, you first need to know what purpose the data will serve. The output of the data analysis can be very different in nature. Therefore, before beginning the analysis and visualization process, set clear parameters against which you want to test the raw data. Both the data itself and the target audience of the visualization will guide your approach.

After you have a clear direction for the visualization, it’s important to use the right data. A visualization can contain quantitative and/or qualitative data. Quantitative data is data made up of numbers: think, for example, the number of units of a product sold or financial results. The advantage of working with quantitative numbers is that they are easy to graph and leave little room for interpretation.

Qualitative data, on the other hand, examines people’s thoughts and experiences with certain concepts. This type of data is therefore, by definition, more subjective and also much more difficult to measure, but contains deeper information. A base of quantitative data supplemented by qualitative data often gives you the big picture.

Keep your data in order

Now it is time to collect the necessary data. These can come from a variety of sources, such as B. Contact forms on your website, product purchases, conversations with customers or publicly accessible databases. No matter what data you use, always keep the GDPR legislation in mind when storing and processing the data. For example, you cannot easily include sensitive customer information in the visualization.

After data collection, you are left with a plethora of unstructured data. Before you can pour them into a visual report, you must first scan through the records and filter out unusable data. Otherwise, the quality of your visualization will be seriously affected. There is an apt saying in the IT world: Garbage in, garbage out. A messy input leads to an even messier output.

A line or a cake?

How you want to visualize the data is also an important decision. There are many ways to make data visible. Well-known techniques are tables, bar charts and pie charts, but you can also work with maps or scatter charts. Of course, what type of chart or graph works best depends on what you want to show.

For example, bar charts are a good choice for comparing things or for visualizing a historical development, but are less suitable for visualizing a relationship between two objects. Pie charts let you show percentage ratios, but it’s harder to highlight noticeable patterns (some experts say you should never use pie charts). Bad chart selection can completely ruin your visualization.

content over form

The visual aspect is crucial in data visualization, but it shouldn’t be your primary concern either. The chart should support your data, not distract from it. With all their enthusiasm, people sometimes go wrong here. An abundance of colors and special effects counteracts the clarity of your visualization.

The best advice we can give you is this: keep it simple. Only use colors to clearly distinguish information in the charts. The arrangement of the data can also play a role. It is best to place the most important information at the top, this is where the eye of your audience catches the eye first. Finally, don’t go overboard with the number of lines, bars, or pie slices you include in the chart: a chart that looks crowded quickly draws attention away.

The rule of thumb is the “4C” formula coined by an American professor named Jeffrey Shaffer. This rule establishes four golden criteria that any data visualization must meet: clear (Clear), Nice (Clean)short (Concise) and captivating (captivating). Let your data tell the story.

An abundance of colors and special effects counteracts the clarity of your visualization.

Use data visualization software

Collecting, cleaning, analyzing and visualizing data can be very time consuming. Fortunately, you don’t have to do any of this manually. There are a variety of software packages that automate (parts of) the data processing process. Data visualization software turns data into an easy-to-read chart in no time. You select the data you want to display, choose a pre-made template and you’re done.

In another article, we will go into more detail about the range of data visualization software. To do this, we want to find out which software is used most frequently by Belgian companies. You can also take this survey by simply filling out the survey below.