Apart from data transformation and manipulation, data visualization is one of the most important elements of business analytics. Data visualization means with images and graphics presenting results of analyses that enriches user with information and eases understanding of trends, discrepancies, samples and search for other characteristics in data. Graphic manner of, displaying of data eases understanding of complex rudiments and relations among data.
Modern tools for data visualization
Today’s tools for data visualization as Pyramid 2020 or Power BI exceed standard diagrams, that are known from the most widespread analytical tool Microsoft Excel. Data can be easily shown not only in tables, but in more sophisticated manners, as numerical strips and meters, geographical maps, thermic graphs, scatter and bubble chart, word clouds, radar charts and combined graphs. Displays can be dynamic or interactive and provide user organizing, filtering and drilling possible or other more advanced analytical functions. Special modulation of visualizations is key performance indicators (KPI). They can alert users about important changes when new business data appear or provide different calculations and comparisons.
What does visualization include?
Technically speaking, visualization of data is scooping wide spectrum of knowledges and abilities that include understanding and data management, familiarity with analytical tools, sense for formation and colours, logic of the assembly of infographics and control panels. Furthermore, you must have business knowledges of understanding of business process demands.
We can improve user experience with correct use of graphic elements, suitable colours and interactive contents. This way the user can pay attention to important data and crucial indicators.
Relevant visualization help users to understand the meaning of the data so they can put them into visual context. With the use of software for visualization of data, patterns, trends and correlations, that would stay hidden in text, are exposed and easier recognised.
Visualization is important with Big Data analyze
Software for visualization of data also has an important role when we analyse large amounts of data – Big Data and with advanced analytical models. Because large amounts of data are generated by all industries day by day, it is practically impossible to use them usefully in raw shape. Therefore, use of visual software tools is urgent. Most of them already has advanced visualization, possibility of using artificial intelligence (IA) and machine learning on base of R, Python and SAS technology.
It helps analytics and data scientists
Visualization is also important for advanced analysts and data scientists. When the data scientist is writing complex algorithms for predicting or machine learning, it is crucial, that they can visualize results and monitor their effectiveness, because in general it is easier to interpret visualization of complex algorithms instead numerical data.
Principles and directions with data visualization
When preparing visualizations and their variations on control panel we advise these principles and orientations:
1. We start with setting goal
With the goal you define what you want to communicate and to whom and in what manner. Think about, what intention will visualization serve for. Will they be base for deciding, search of discrepancies and mistakes or trends? Maybe users need meticulous data or more aggregated on higher levels. visualizations can help users to recognise relationships, discrepancies, samples in data therefore we need to know what we want to communicate.
2. Visualizations must answer our questions
When analysing data try to assume the role of the user. What are the questions they want answers. Ask yourself, what do they want to know about the data. Then try to think, how will users read your visualizations. More precise you are, the better it is. Decide if you want to investigate and emphasise the best (largest, highest) or worst (smallest, lowest) elements, compare certain data points or prepare trends via longer time periods. After data research and searching for specialities we have to choose the most suitable visualization that can serve an answer to business questions.
3. Use visual hierarchy
Visual hierarchy is the way, how you organise your visual elements on the control panel or a report. Hierarchy determines how important is each of them. Determine order in a way that we sense things. This helps people to understand the data and the story that we want to tell. So, when you create a data project you should organise your visualizations according to their meaning. You assign every element number (for example 1-10). Then try to reflect this sequence in your plans. Down below you can find some elements that can help you with this.
- size and vastness – large things will look more important,
- colour and contrast – light colours will be noticed first,
- typography – larger, bolder fonts will attract more attention,
- perspective – things, that seem closer will look like more important than those in the background,
- distance and place – elements that stand independently will be noticed first.
4. Evidently mark important things
This is based on your visual hierarchy. Marking can guide users through data, and it warns them about the most important parts of the graphics. The data that they should focus on must stand out.
5. Know your data
Even though you can change almost everything to data and encode visually, knowledge of the context of the data is as important as understanding of alone data. This knowledge will serve also for verification, if you have the best data to support your goal.
6. Put your audience first
Visualization of data is rarely one size fits all, so the message can be lost, if it isn’t adapted to target group of users. Therefore, you must concentrate on visualization of things that your audience has to know. For example, don’t force administration and management with details, as they aren’t important to them and they divert them from things that are important.
7. Check, which devices will be used for viewing of visualizations
People will check data on different devices. Apart from computers, to access data people more and more use mobile phones and tablets. Therefore, you must pay attention to limitations of devices and responsiveness of visual elements on each device. Only if visualization works on all devices, it will achieve its intention.
8. Choose right visualization
Know advantages of each type of visualization and key characteristics of data that is showing them best. Visualizations can work excellently together if they complement each other and are explained in control panel. However, don’t forget: too much of them is often too much.
9. Carefully use titles and inscriptions
Give your visualizations context in a way that you include simple and convincing title. Then mark axles, so that it is easy to read them. Reduce use of keys and other explanatory elements and allow them that visualization communicates without additional explaining. If you decide for use of elements, as comments or stories, this is definitely added value.
10. Design to detail
The excessive design makes search of important information difficult; it is harder to remember them, and it is easier to overlook them. The key here is that data visualizations are simple. Remove all artistic details, unnecessary headers or inscriptions. Finally, make certain, that everything on visualization serves its intention.
11. Let data speak for itself
Data are the most important ingredient of data visualization. Use visual orientations so that you guide users and attract their attention, however, let the data tell the story, not the designs. Well planned story helps to explain data and adds depth.
12. Feedbacks is good thing
Take your time for accurate adaptation of visualization. The reactions of those, that know the data, its context and audience can help. They might see something they need, or they will have some good idea that could improve visualization of data.