A Guide on Data Visualization Techniques
Every business decision should be powered with data in order for it to bring value to a company. As the number of available data sources increases due to technological advancement, the amount of available data grows as well.
Big Data is a valuable asset that helps companies better understand their customers, competitors, market state, and make accurate decisions. But it’s not enough to collect valuable insights - you also need to present them in a format that will be understandable for all the parties involved. This is where data visualization steps in and helps companies all over the world share their insights in an easy, simple, and extremely understandable manner.
What is data visualization and why is it so important?
Data visualization is a representation of the collected data in a suitable format (graphic, for example). A pie chart is the most popular example of a data visualization technique. There are about 16 types for data visualization and each has its own purpose and peculiarities.
Hardly one business meeting goes without displaying a pie chart or a graph - but what makes data visualization so important? The thing is, data visualization helps key stakeholders and other parties involved see complex analytics and data in a simple format that can be easily analyzed. With the help of visualization, decision-makers can easily identify dependencies, analyze the state of a business or a market, and make forecasts based on the collected data.
Five things to consider before visualizing your data
As mentioned above, there are different types of data visualization and different purposes for each. In order to choose the most suitable method, it is important to consider the following factors:
- Audience: who are the people that will be looking at the data? End-users of a product, for example, will demand simplicity (i.e. users of a fitness application) while stakeholders and top management will require more complex insights with a maximum amount of useful data.
- Data type: is it metrics, a relationship between the two variables, or a comparison? Each data type will demand a specific visualization method to be used.
- Color: even though it sounds not as important as other points, the choice of the right color actually plays a big role. With the right colors, you can emphasize the needed elements, use colors for comparison, and show positive and negative figures with corresponding colors.
- Dynamics: you can go with either a static visualization or a dynamic one, depending on the data type (whether it changes constantly or on a monthly basis).
- Your goal: why do you need to visualize this data? The purpose of the data visualization will dictate the choice of the corresponding visualization method.
The most common visualization methods and when to use them
Below we will have a look at the most popular and well-known visualization methods and the cases when they should be used. We hope our review will help you decide the perfect visualization method for your next presentation.
A bar chart organizes the data in rectangular bars that can be placed horizontally or vertically. This visualization method is perfect for comparing the data sets and is extremely easy for understanding. Another great benefit of a bar chart is that you can change any data set that you need and this change will not affect the other bars.
Bar charts work great for less than 10 categories, otherwise, the chart may become too cluttered. As well, you can use bar charts to compare parts of a whole.
Best for: comparing two or more values of the same category.
A line chart displays the data in the form of lines and is mostly used to show information on certain progress, trends, or changes that happen over a period of time. While a line chart is simple and easy to understand, it is important to differentiate lines with colors so a viewer can better understand them.
Line charts are suitable for continuous data and are not so great for the data with lots of starts and stops. As well, try sticking to not more than four lines, otherwise, the chart will get cluttered.
Best for: the display of progress, trends, changes, used for continuous data.
A scatter chart (or a scatter plot) is one more commonly used visualization method that helps understand the dependencies and relationships between the data sets. This is a perfect choice for analyzing multiple data points as it helps clearly see the dependencies and similarities. The two axes of the chart are the value axes that you use to analyze the data.
Scatter charts might be too complex if you want to immediately analyze the presented information. As well, this type of visualization does not present the information in a precise manner which should also be kept in mind.
Best for: showing relationships or dependencies (i.e. customer feedback based on the help desk response quality).
A pie chart is an absolute favorite of many users as it’s incredibly easy to understand and helps quickly scan the presented information without going into too much detail. A pie chart usually represents one data set that is divided into categories and all of its portion should make 100% when summed up.
Pie charts are commonly used by marketing professionals as they help clearly present information about the market shares, spendings and budget, customer demographics, customer preferences, etc.
Best for: comparing the data and quickly scanning it.
Heat maps allow placing the elements on corresponding areas (i.e. geographic regions) and see the dependencies and relationships. Such maps typically imply using a certain color and its gradient or saturation to emphasize certain data sets.
Heat maps cannot display individual or precise metrics but help analyze the dependencies and the measurement range.
Best for: showing relationships, emphasizing important information.
Histograms are great for tracing the data distribution over a period of time. They show how the data has been changing, display the peak points, and show where the values are concentrated the most.
As well, histograms help spot any unusual activity, such as gaps, unusually high or low peaks, etc. In this way, histograms are a valuable strategic asset as they help analyze the right and wrong decisions of the past.
Best for: analyzing the data over a period of time.
Data visualization is an obligatory practice for any company despite the industry, type, or size. Whenever there is important information to be presented, you need to transform it into an understandable format so do not neglect data visualization methods. And considering the fact that Big Data is still in the state of growth and development, every company will need to master its data visualization skills in order to use the data for the company’s benefit.
Irina LinnikView all articles by this author.