How important is coding for data visualization
What is data visualization?
Data visualization is just as important as pruning big data beforehand. A lot of data only makes sense through visualization, read this blog post to find out why. Mainly it is due to the people. Rarely on the machine itself.
What is data visualization and what makes sense? A brief introduction.
The demand for data and analysis is greater than ever. For example, a study found that 80% of executives rate IT data as the most trustworthy data source in companies. During these times, the topic continues to gain momentum:
"The impact of the global pandemic on the economy has made it clear to companies that they must use the data age to survive."
However, there is a huge difference between collecting data and using it effectively. For example, to increase profits and optimize the business. Data must be prepared, processed and organized in order to be used. An important part of the process is making data easily accessible and understandable for employees.
For this reason, data visualization, the process of graphically displaying data sets, is becoming increasingly important in many companies. Reporting in particular rarely manages without the graphical processing of complex data structures.
Why is data visualization necessary?
For most people, reading a multicolored, well-organized chart is much easier than digging through data in an Excel spreadsheet or report.
After all, the brain has to store information in order to process it. By visualizing data - i. H. Presented neatly and in an organized manner, making them easy to read - it is much easier for most of us to understand them at a glance. In fact, scientists have found that the human brain processes visual information 60,000 times faster than plain text. Visual representations also make people perceive connections in the first place. In addition, people who only read information only remember around 10% of the content after three days. However, with visual representations 65% of the message gets stuck.
Choosing the right visualizations
We are moving beyond the table age into the digital age. The proliferation of modern platforms and software such as Tableau or PowerBI have made it easier to present data visually for end users. They are also much more “exciting” than spreadsheets.
Visualizations can vary depending on the type of data category you are working with. When it comes down to that, there are a seemingly infinite number of ways to represent data. Before you start, it will help to have a clear understanding of what you are trying to get across. That way, you can tell a compelling story with your data.
Here we have put together some categories for you to visualize data in a meaningful way.
If you want to compare several distributions of numerical data, either a histogram, a 3D area chart, a point diagram or a box plot can help.
In general, however, histograms should be used when there is a similar sample size and few different comparisons need to be made. Otherwise, the chart may appear overly full and difficult to read. On the other hand, you can use a box plot to see if the dataset is symmetrical or skewed. Interesting too!
Comparisons indicate differences between values. Whether different data characteristics or points in time are involved sometimes also decides which visualization can be selected.
To compare multiple items, you can use column or bar charts to present the data. Tables with embedded charts can also be of interest if the data allows it. You can also show a time course. Line or bar charts are particularly suitable for this, but also radial charts if the data behaves cyclically.
Relationships describe data connections in different tables. To visualize correlations, you use either a scatter plot (also called a scatter plot), a bubble chart, or a heat map.
Relationships and special connections can also be demonstrated using arc diagrams, network diagrams, and tree diagrams. Heatmaps and Marimekko diagrams can also be used.
It always depends on the complexity of the data. In order to choose a meaningful, understandable visualization, you should slowly approach the data and try out a bit. Take courage!
Compositions of data or part-to-whole relationships can be visualized in many different ways.
To narrow down the selection, you should first determine whether the data is dynamic (over a period of time) or static. Dynamic data can be visualized using stacked columns or area charts, for example. Static data is best organized using pie charts, waterfall charts, tree maps, or even stacked bar charts.
If you have location data - whether zip codes, federal states, country names or your own custom geocoding - then you would like to have your data displayed on a map. Just like you use your GPS if you don't know your way around a city. You want to have an informative view of the data to help you find your way around. The location diagram map also combines the visualization of a composition in the pie chart with the location on the geo map. This gives you a quick overview.
As you can see, there are innumerable types of visualization options. These are just a few examples to help you choose the right one for your data. Try different visualizations. Over time, you'll get a feel for how to present your data in the most effective way possible.
Data visualization challenges
Data visualization can be very easy with a small set of data. If not, things can get extremely complicated. It largely depends on what you want to analyze and communicate.
With that in mind, let's examine some common challenges related to data visualization.
Business data is becoming more and more complex every year. Companies today pull data from sources such as IoT devices, sensors and apps, from websites and data warehouses - which are often disconnected.
Because of this, organizations need to be very careful how they approach large and complex datasets. It is important to have a thorough understanding of each individual data structure in order to choose the right visualization.
Part of the challenge of visualizing big data is to simplify it enough to be workable and compelling - without making it too easy. For example, when you are working with millions of data points, it is easy to draw conclusions while overlook the subtleties and patterns.
With new software for data processing on the market, the number of employees in companies who are supposed to deal with the analysis of data is also increasing. Often, however, there is also a lack of sound technical knowledge. While this can be made easier with the right tools, caution should be exercised. Without suitable instruments, the data should be left to trained experts in order to avoid wrong conclusions.
You can find our Tableau course here - if you want to become a trained expert.
Equip your university with ours FLIPPED CLASSROOM out.
Sharing and Caring
Accepting and accepting errors is a fundamental part of the data analysis and visualization process. Obviously, this can be frustrating - especially when you're analyzing data on your own. Cut off from other company information. Because of this, many companies are starting to use platforms that pull corporate data from multiple sources. This enables easy collaboration and sharing across the company.
So in the future, departments will have to work together to discover unique trends. By already working together on the data sources and networking knowledge throughout the company.
This article was also published on our Medium Blog. Follow us for more blog posts and exciting information. Stay informed.
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