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Why Data Visualization is So Inevitable?

Data Visualization
Data Visualization

We have heard a lot about data science and data analysis and how this technology has become mainstream, considering today’s time and need. However, what is data visualization and how is it a part of this entire niche? Many who have already been a part of data analysis would have definitely come across this term. Reason being: Data visualization is an inseparable part of data science.

Extracting meaningful information from the raw data is just the beginning. It all ends at how simple and accessible one can represent useful data and make it available for others to understand. That is where data visualization pitch in.

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In this article, we will be discussing the meaning of Data visualization and the various ways to represent data. We would also be covering the importance of this term and a few recommendations for tools to accomplish the visualization of data.

What is Data Visualization?

As mentioned above, the visualization of data is the representation of meaningful information in terms of visuals. This could be graphs, charts, or any other format that is capable of establishing the dependency of various data sets. This offers viewers a quick and simple way to apprehend the data at hand. And, make inferences without needing to go through a complex and tedious group of unorganized data.

To start with, let us take an example to understand why data visualization is so inevitable. How would browsing through hundreds or thousands of excel rows and columns feel like? If you need to find those years when trends of Java Experts were higher than C++ professionals, what would you prefer: the list of rows and columns of an excel spreadsheet of just a simple graph?

The example says it all. When putting data into a visual format, it becomes easier for extracting the inferences in a matter of time. One can point to the trends and patterns easily, without investing hours of time. Plus, it becomes simple to analyze larger batches of data.

What makes it even more fun is that data visualization is not just important for data scientists, but it actually helps professionals from all sets of jobs. Either you are an engineer, marketing personal, designer, whatever, the need for better representation of data would always be the part of your role.

How to Represent Data in Simple Visual Format?

With time, we have been able to release many visualization tools offering manual as well as completely automated solutions. For example, Xplenty is a cloud-based solution for pulling data from over 100 sources offering plenty of data integration use cases for forming relations and creating visualizations.

Another popular software is HubSpot. It is designed for sales reporting improving the representations of different variables through charts and graphs. If you belong to the field of business intelligence, you may like to refer Tableau. The tool provides interactive graphs, charts, and other visual formats for simplifying data analysis.

So, what are the various visual format one can use to represent data? There are quite a few. Here are some of the most popular ones that users consider when preparing data visualization representing important values.

  • Line Chart
  • Bar Chart
  • Area Chart
  • Bubble Chart
  • Gauge
  • Histogram
  • Map

There are other formats too. Depending on the type of data one wants to represent, one can choose among the various visual formats.

Role of Data Visualization

According to research, almost 52% confirm that they invest too much time filling spreadsheets while 53% think that enormous data entered goes unanalyzed. Doesn’t that sound alarming? Why are we investing time filling spreadsheets when most of the data entered would go waste? Are we spending money in the wrong place? And, are we losing opportunities without knowing they exist?

One may refer spreadsheets as a part of data visualization, but the problems connected with it are too obvious to ignore. That is where the bigger picture starts seeping in, filling the loopholes generated with weak solutions. Making data visualization, a need for the hour.

By adding huge value to data analysis, visual formats has become an extremely connected part of the overall niche. Here are some of the reasons why data visualization is valued so much:

Quantifying Change and Frequency Happening Overtime

These are the most basic usages of data visualization. With most data accompanying time elements, for determining a change in the trend or frequency of change, these two values become so usual. However, this does not signify that the role of data visualization is any less when quantifying these changes. On the contrary, the analysis of change in trends becomes extremely easy with data visualization.

Factoring Relationships

This one is one of the most valuable roles of data visualization. Without proper visualization, determining the relationship between the two variables would become highly difficult. And, how can we miss finding dependencies of data when counting on our projected plans which are usually based on the inherent relationships between variables.

Making Schedules for Deliverables

Have you ever worked with a complex project where determining milestones seems to be undeniably impossible? Well! In this case, you can always turn to data visualization. Using the Gantt chart, it becomes lucid of what part of the project would eat what amount of time.

Processing Huge Networks

The job falls in the kitty of market professionals who consider different variables falling within a network to extract the target customer data to approach. For instance, one must analyze different clusters of audiences, influencers lying within those clutters, outliners, and more. This helps in analyzing the audience that may be interested in our products and services. With data visualization, this task becomes a piece of cake.

Highlighting Risks

To find out intricate values such as risks and profitability of given variables, we must consider data visualization. This is so because spreadsheets would never be able to offer accurate inferences and predictions. On the other hand, color codes and various other visual representation would make work easier.

The Conclusion

Can you imagine the amount of data we deal with on a daily basis? Either it’s the contact book on your phone or huge databases of companies, we all must go through numerous variables to find the one we are looking for. If not data visualization, was it ever possible to predict trends in advance? Doesn’t that already talk a lot about why it is so inevitable?

What do you think?

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