A company which neglects proper descriptive statistics and data analysis already finds itself at a disadvantage compared to its competition. With data quickly turning into the lifeblood of the business world, it must be put to good use for a business to remain relevant and successful. The first step is collecting the data, but in many ways, that’s the easy part. Once all that information has been gathered, what should companies do next? How does one make use of the large sets of data now at hand? That’s where descriptive statistics and data visualization come in.

**What is Descriptive Statistics?**

The basic definition of descriptive statistics is that it describes data. It’s a way to summarize and organize all that data you’ve collected into something more manageable and easy to understand. These descriptions can include either the entire dataset or merely a portion of it. One of the important things to know about descriptive data analysis is that it only focuses on the data itself and not on possible far-reaching implications that go well beyond the data that’s represented. That’s the difference in descriptive vs. inferential statistics, the latter of which uses more complex calculations to make wide-ranging predictions.

**Descriptive Statistics Examples**

To better understand the role of descriptive analysis, it’s helpful to know some examples of descriptive statistics, and that starts with the types of statistical analysis you could encounter. The first type is the central tendency, mostly represented by the mean, median, or mode. The mean, of course, is the average of the dataset. The median is the value of the data point in the middle of the set. And the mode is the value which occurs most frequently. One common example of descriptive statistics related to mean is the student’s grade point average (GPA). In this way, a student’s academic performance can be measured by averaging his or her grades.

The second type is referred to as frequency. In other words, it’s a measure of how frequently something happens. You’ve probably seen this in the descriptive statistics used in summarizing polls and survey responses — 61% of people answering “yes” to a specific question, for example.

The third type is a measure of position. This includes quartile and percentile ranks. Essentially, this type of descriptive statistical analysis helps to describe how different points of data relate to each other. The measure of position is best used to compare the data points to each other.

The fourth and final type of descriptive statistics is variation or dispersion. This type is most commonly used for determining the range of values that the data encompasses, identifying the maximum and minimum values in a descriptive statistics example. The variance of the information can also be attained through this approach, which can help determine if there are certain outliers among the data you’ve collected.

**The Importance of Descriptive Statistics**

Data analysis and descriptive statistics are vital components of any business strategy. Take a moment and think about what raw data looks like. It may come in the form of an enormous spreadsheet filled with numbers. Sometimes the data isn’t even that organized. Even to data experts, this clutter of numbers can be hard to read much less interpret. Descriptive statistics organizes all of this information into something that is much more usable. This step usually has to be done before moving on to the next one — data visualization.

**What is Data Visualization?**

Like the term suggests, data visualizations is taking the data you have and converting it into a more visual form. Instead of having to look at numbers and spreadsheets, you get a picture that represents that information. While descriptive statistics can break data down into something more digestible, data visualization goes even further, taking that data and creating a visual that instantly communicates a story.

If you’ve ever seen a pie graph (and that’s probably a given), then you know what this looks like in action. Pie graphs are very simple examples of visualization, but they’re very effective in what they do. Think of bar charts, line graphs, spider charts, scatter plots, and diagrams and all the information they can convey in a moment. Think of it like the ultimate visual aid. It’s easy to see why data visualization is a key ingredient in interpreting data.

**The Importance of Data Visualization**

From a business perspective, data visualization is indispensable. Data scientists may be able to look at raw data and discover key findings, but communicating what data says to those who lack expertise in data science will always be needed. If you need to get a point across in a short amount of time, data visualization is the way to do it. It makes the data clear and cohesive, eliminating the fluff and showing the most important points. With good data visualization, there will be no dispute over what the data is, rather the only discussion would be what to do with the data presented.

**Data Visualization and Descriptive Statistics in Business**

Combining both descriptive statistics and data visualization transforms them into a valuable asset for any company. One of the most important functions they serve is to help company leaders in making key business decisions. Data has normally been used when coming to a crucial business decision and the use of descriptive statistics and data visualization only amplifies that effectiveness.

There are many ways in which the two are used to inform business decisions. Through data visualization, it’s easier to notice patterns and identify how various data points relate to each other. Business leaders can also look at recent historical trends and determine where those trends might go and how best the company can capitalize on them. With raw data, many of these instances would be hard to figure out, but after employing descriptive statistics and utilizing data visualization, the correlations can quickly become evident.

With these vital pieces, businesses suddenly become much more versatile. With the data visually displayed for everyone to understand, companies can identify untapped markets where their products or services might flourish. They can determine which parts of a company’s operations can be made more efficient, thus cutting down on costs and optimizing overall performance. They may also identify ways to improve the customer experience by getting real time feedback from customers. Businesses can even prepare for future growth or possible downturns, keeping organizations ahead of the curve and ready to handle all the opportunities and challenges that await them.

**The Right Data Visualization Tool**

All of this requires the use of an effective and versatile data visualization tool, the exact kind that Import.io provides. With this tool, you can become proficient in understanding the data that you collect. A good data visualization tool like this is extremely helpful in turning abstract data into something much easier to grasp. As part of turning data into a visual element, the data gets cleaned, shaping into a manageable item and filtering out data values that may unnecessarily interfere with the message communicated through the information. Only through this process does data visualization turn data into something you can use to help strategize and plan ahead.

**Use Data Today**

Data analysis, descriptive statistics, and data visualization should become part of a business’s arsenal. Data has so much to offer in terms of informing business decisions and planning business strategies. Missing out on these capabilities means missing out on possibilities and opportunities to grow and find greater success that’s sustainable.