Data visualization is information and data depicted in the form of charts, graphs, and other graphic elements. Data viz constitutes a crucial part of the data analysis, aiming to make information easier to understand and act upon. Visualization tools serve to process and present collections of data in a comprehensible way in order to help their readers pinpoint patterns and anomalies in substantial data sets which may be otherwise difficult to navigate. Visualizations are created to communicate interdependencies between pieces of information and convey them using images.

Making sense of big data

Alright, so you've collected a ton of big data but what do you do with it next? How do you turn raw information more into a gemstone? Data visualization comes in to make sense of it by converting a data set into digestible insights. When done right, it highlights the most useful pieces to tell a story.

With the amounts of data available to businesses and organizations, data visualization has become somewhat of a necessity. A chart, graph, or map will work great when you're trying to showcase, understand, and explain big data sets. Data visualizations facilitate the process of communicating information that might've otherwise been difficult to follow and used for spurring improvements.

Now, there's a fine line between the visual and functional aspects of the process. Simply creating pie charts for the hell of it won't cut it. A delicate dance between source data and charts has to take place. The two ingredients have to be mixed just right to make the desired impact.

Dealing with big data, in particular, you have to turn to more advanced methods of data visualization. A scatter plot or a heat map generated by powerful software will aid the eventual audience in interpreting the information and drawing actionable insights.

Big data can be extremely useful but its analysis poses several challenges you have to be aware of. For the best possible results, data visualization tools are required to produce the most efficient and useful outcomes.

You have to make sure you have both the right hardware and personnel talent to design the proper analysis process for the types of data at your disposal. The accuracy of the data visualizations you'll produce will reflect the quality of information you collect, so you need to set up appropriate processes and have individuals managing them well in the first place.

Big data visualization can fall into one of several categories.

Temporal

Visualizations belong in the temporal category if they meet two requirements: they have to be linear and one-dimensional. These will usually include lines that stand on their own or overlap with each other, in both cases having a start and endpoint in time.

Hierarchical

This category of visualizations arranges groups within larger groups. Use it if you wish to showcase different clusters of information, especially if they stem from a single starting point.

Network

One data set is often related to another on multiple levels. This style of data visualization demonstrates relationships between these sets without the need to provide a lengthy explanation.

Multidimensional

Here you're dealing with two or more variables with the goal of creating a 3D data visualization. Due to many overlapping data sets, these charts and graphs tend to be the most eye-catching.

Geospatial

This style of visualization usually involves a map of sorts, with data spread over it, as it relates to actual physical locations.

Why is data visualization important?

Like many other things, data visualization had modest beginnings. Early analysts used to design spreadsheets containing elements like a bar chart, line chart, pie chart, and perhaps some other simplistic graphs.

Interestingly, humans are naturally predisposed to recognize and follow patterns. Having a visual summary of a large chunk of information makes it easier to see trends. In the business context, the value of data increases when it's presented as a pie chart or a heat map. It can then reach and be understood by more people. Communicating ideas isn't far behind actually having them in terms of importance. A stakeholder may not care much about raw numbers but will appreciate seeing a line trending upwards.

Data visualization is a modern-day art form that helps us better understand the information we extract from the outside world. Clearly, there's a difference in how efficiently data analysts will be able to work with spreadsheets versus data visualizations.

With big data increasingly becoming a tool and a product, it's more important than ever to conduct analyses and draw conclusions on the basis of it.

You may be wondering whether you really need data visualization and if it's actually worth the effort. It's yes and yes because it makes complex data sets way easier to communicate and understand. You have to keep different audiences in mind, and visual is the universal language.

Information presented in the form of charts or graphs allows businesses and other organizations to collect insights on crucial aspects of their operations and then present the findings to stakeholders, as well as for internal data analysis.

Simple pie charts and bar graphs are something many of us have already come across in our personal and professional lives, however, the type of data visualization used to depict a particular set of data will be closely related to what kind of information you have at hand. In certain contexts, some will be more effective than others.

How is data visualization used?

Any project starts with visualization - the ability to convey ideas in a visual form is highly useful. In today's data-driven world it's more important than ever to be capable of using data to make decisions, explain ideas, and tell stories.

You'd be hard-pressed to find a field of science or business that wouldn't benefit from data visualization - government, finance, marketing, history, consumer goods, service industries, education, and sports all use data to improve their operations and achieve goals. The thing about information is that its raw form doesn't do much on its own. It has to undergo analysis to produce meaningful insights. Luckily there's a range of digital tools available for this.

Data analysis and visualization are extremely useful skills in many lines of career. With the amounts of data, businesses have accumulated, it's only natural they turn to visualization to make something of it.

It's worth pointing out that data visualization has a quite precise goal when being employed. An area chart, a map, a pie chart, or another type of graph can be used for one of several main purposes, mainly to depict:

Frequency of events

When data is collected over a period of time, it becomes logical to try and figure out how frequently certain events occur.

Changes over time

Time is a very important factor in many types of data. The information you may want to elicit via visualization is whether there are any trends and tendencies happening over time.

Correlations

Being able to draw a line, or establish a relation between two data points is a huge advantage of data visualization tools.

Scheduling

A good plan of action should be followed by great execution. Certain plans may get a little complex and thus difficult to adhere to and this is where a Gantt chart comes in, laying out all the tasks needed to be completed and showing the required time.

Inspecting a network

For instance, a particular area of the market can be considered a network. Marketing professionals refer to data visualization to better understand their audiences, establish relations between clusters within, or pinpoint outliers.

Analyzing risk and value

There's a multitude of factors involved in determining such complex factors as risk and value so trying to read the information presented in a plain spreadsheet may be too tall of a task to produce meaningful conclusions. Data visualization tools introduce order and help you see more clearly.

Data visualization is also frequently applied in fields like:

Politics - where a map may be used to display voter distribution.

Sciences - where experimental data is turned into conclusions.

Sales and marketing - where data visualization helps track trends affecting the operations of businesses over periods of time.

Logistics - where visualization tools can help determine the most efficient shipping routes.

Healthcare - where variables are placed over a map to show their geographical distribution.

Finance - where various charts help detect trends.

Data visualization examples

Although the original data visualization methods are still being used, the entire design aspect of it has evolved to deliver many other, more visually appealing techniques for showcasing data.

The most commonly used data visualization methods include:

• Charts
• Tables
• Graphs
• Maps
• Infographics
• Dashboards

The more specific visualization techniques are as follows:

What makes data visualization effective?

The selection of the particular method will be influenced by what exactly you're trying to show in the visualization. It may be a change over time, a relationship between variables, a hierarchy between data points, or a social structure, to name just a few.

In essence, data visualizations help one to:

• quickly take in the information, get a better understanding of it, and make data-driven decisions
• plan future steps required for improvement in particular areas
• keep the audience interested and engaged with the information served in a visually attractive form
• easily share the data with relevant people
• skip the need for data analysis, since the visualization already is the product of information processing, thus making it more accessible
• stay ahead and be more successful by being able to act quickly on the insights resulting from the use of visualization tools

Final word

In its most fundamental sense, data visualization is about telling a story behind numbers. The highly touted big data gains actual value only when given an appropriate visual form.

Efficient data visualization is the extremely important, final step of data analysis. Forego it, and you risk losing important insights that may otherwise help push your business or organization forward. If, after presenting your charts and diagrams, you get more questions regarding the information displayed, instead of what is actually being shown, you know you've done it right.

To reach this point, you have to be familiar with actual applications of data visualization, as well as all the forms it takes. The next step is about processing data and bringing the visualization to life in order to be able to make conclusions. In other words, the graph you produce should answer any relevant questions you may have.

At Synergy Codes, we're very familiar with turning data into visualizations that drive decisions and allow your organization to flourish. With us, you're not limited to what off-the-shelf data visualization tools offer but instead, you can create graphics that meet your needs. Describe your vision to our team and we'll bring it to life.