Descriptive Statistics and Data Visualization

ART04x - edited feature imageTurn Your Statistics Into
Something More Interesting

Data is quickly becoming a defining thing in the business world. It is the lifeblood of every company decision and thus, it defines what companies do. A company which doesn’t pay attention to proper statistics can be at a serious disadvantage from companies who do, especially companies that use descriptive statistics and data visualization.

Data has to be good if a business wants to remain relevant and successful in the business world.

The first step would be to collect the data, which is quite easy in many ways. Then the gathered information needs to be analyzed and understood. But what comes after that?

Simple – descriptive data and data visualization.

Descriptive Statistics

Descriptive statistics describes data – it summarizes and organizes all of the collected data into something manageable and simple to understand. The descriptions can include the entire data set or just a part of the data set.

One of the most important things to know about descriptive data analysis is that it focuses on the data instead of on the implication that can be far reaching and go beyond the represented data.

This is the main difference between inferential statistics and descriptive statistics. Inferential statistics uses complicated calculations to make predictions while descriptive statistics doesn’t.

This is just the basic information you need to know about descriptive statistics, but it’s worth understanding the basics before we dive in any deeper.

Examples of Descriptive Statistics

If you want to understand the role of descriptive analysis, you need to know some examples of it. The first thing to know here are all of the types of statistical analyses that you could find in real life.

  • The first one is the central tendency which is represented by the median, mean, and mode. The mean is the average of the dataset and the median is the value of a data point in the middle of a set while mode is the value which is more frequent and appears often. One of the common examples of mean is GPA, for example. The student’s academic success is measured by the average of their grades.
  • The second type is the frequency. This is a measure of how commonly and frequently something happens. This is often seen in descriptive statistics when summarizing polls or surveys, and their responses. For instance, if 46% of people say yes to some questions.
  • The third type would be the measure of position, which includes quartile and percentile ranks. This type of descriptive analysis and statistics describes different points of data and how they relate to each other. This is used when comparing the data points against each other.
  • Finally, the fourth type is the variation of dispersion which is commonly used as a tool to determine the range of values that the data encompasses and it identifies the maximum and minimum values as well. The variance of information can also be attained and it helps determine certain points.

Why is Descriptive Statistics Important?

Descriptive statistics is a vital point of any business strategy. Raw data comes in the form of a huge spreadsheet filled with numbers and it’s not often organized properly. It’s even complex for data experts.

The clutter of numbers is often hard to read and interpret.

Descriptive statistics organizes all of the mess and clutter, making the information usable. This is the crucial step to do before you embark on the journey of data visualization.

Data Visualization

The term itself essentially means that you should take the data you have and that you should convert it to a visual form which is simpler to digest and understand. Instead of looking at numbers or spreadsheets, you can get a picture which shows you the information.

Descriptive statistics turn the data into something more understandable than raw data but data visualization goes further than that and creates a visual which quickly tells a story.

For example, a pie graph shows information much better than a bunch of numbers. And everyone has seen a pie chart many times already.

Pie graphs are very simple but they are effective when used properly. But there are also different forms of data visualization like:

  • Bar charts
  • Line graphs
  • Scatter plots
  • Diagrams
  • Spider charts

And there are many more. This is the ultimate visual aid and it’s a key ingredient in using the data in a helpful way.

Why is Data Visualization Important?

From a business perspective, data visualization is crucial. Data scientists can look at the raw data and see something important, but people who are not data scientists, which is the majority of the decision making teams in companies, can’t use raw data.

That’s why data visualization is necessary when you need to get a point across. It makes the data clear, understandable, and it eliminates the confusing aspects of it. With good data visualization, you can get a lot of success and open the discussion of what to do with the data you provide.

Data Visualization and Descriptive Statistics

When you combine descriptive statistics and data visualization, you turn the data into something valuable for your company. One of the most important functions of data is to help the business leaders make proper decisions. It increases the effectiveness and makes decisions more solid.

There are many ways in which you can use them together to benefit a business. It makes it easy to notice some patterns and identify how different points are related to each other. Business leaders also like to take a look at historical trends and how they can use them for the business’s benefit.

The raw data makes it hard to figure out everything while descriptive statistics and data visualization makes it very easy to understand, making correlations clearer.

With these two crucial pieces, businesses can become much more versatile. The data is visually displayed so that everyone can easily understand and companies can identify untapped markets.

Then they can determine which pieces of their operations can be more effective and more productive, improving the performance. They can also find out how to improve customer experience and get feedback.

They can also prepare for growth or changes in the market, prepare for downturns, as well as prepare for staying ahead of competition, and they can handle all opportunities or challenges.

Raw data would make all of this impossible or hard to figure out but these two techniques –  data visualization and descriptive statistics –  can make it easy.

“When it comes to data visualization and descriptive statistics, these are extremely useful to companies of all sizes. Both can help the companies prepare for all sorts of situations, from bad ones to good ones and they can make the decision making process a lot easier,” says Jim Scott, a marketing writer at and

The Proper Tools

To make data effective and useful, companies need a proper data visualization tool. There are many different data visualization tools that companies can use. From simple to more complex, it all becomes a matter of preference for companies.

“Finding the right data visualization tool is extremely important in all cases as it can make a difference between a good data representation that actually helps and bad data representation that confuses people. Choose wisely in this respect,” says Elise Crowley, a team leader at and


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Ashley Halsey

Ashley Halsey

Ashley Halsey is a data scientist and writer at and who has been involved in many projects throughout the country. Mother of two children, she enjoys travelling, reading and attending business training courses.

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