Visual Data Representation: Importance, Techniques, Tools, and Google Data Visualization Software

More and more spheres of modern life ground on data collection and analysis. The sphere which can benefit most from data analysis is business, however, speed matters. So, to make prompt and timely decisions, it’s necessary to have a clear idea of what the collected details mean, which is best achieved by giving it visual context.

The importance of data representation visually should not be underestimated. Visual details are easier to comprehend since such information looks more natural for the human mind. Thus trends, patterns, and outliers even within large data sets can be identified fast. That’s why one of the most reasonable investments in business today is purchasing an effective tool for data visualization that can help with the process of decision-making through interactive visual data representation.

Why is Data Visualization Important?

Speaking in general, data visualization is delivering information in the most efficient way. Initially, the raw data gathered is modeled and delivered for effective data-driven decisions. Going deeper into the matter we’ll see that data scientists working in the field of data analytics create multiple machine learning algorithms to analyze and compile the essential data into visual objects.

In such a way they shape the information into images in a universal, fast, and effective manner. It helps businesses, and not only, identify the areas that require improvements, the factors that influence customer behavior, and the options to improve sales. The role of data visualization in e-commerce and business is crucial for business owners, stakeholders, and decision-makers since it allows them to make better predictions in sales and overall business processes. Recognizing the patterns faster due to the graphical or pictorial forms, the future development of the company can be estimated with better accuracy and optimized if it’s necessary in a due time.

When it comes to some complex projects, images speak louder than words as well. So, when a collected data is properly visualized, it can be better understood. What is more, it helps to monitor the processes within the project and ensure they go the right way.

Computer scientists can use data visualization for innovative developments in the sphere of machine learning and artificial algorithms developments.

In fact, data visualization matters for almost any career, even teachers and tutors, for instance, can monitor students’ test results and overall progress in studies with the help of this method.

The list can be continued, however, let’s proceed to the data visualization techniques existing.

Data Visualization Techniques

The methods to put information together and visualize are multiple: graphs and tables, dashboards comprised of them, etc. The choice depends on the details necessary to outline, the scope of data, and purposes. Some visualizations can be created automatically, others require manual work. Let’s look at some kinds in more detail:

• Infographics are about extensive data collection, systematization, and a comprehensive representation it visually. This approach is ideal for exploring complex topics.
• Heatmap Visualizations use a graph with numerical points that are highlighted in certain colors to indicate high-value or low-value data points. This method is based on the studies that humans interpret colors better than letters or numbers and heatmaps, thus, help the users identify the information clearer.
• Fever Charts represent the changing data over a certain period. It allows to compare certain indicators from the previous and ongoing period, projecting future results. Decision-makers can then interpret wide and varying data easily.
• Area Charts (or Graphs) are excellent to picture the data’s time-series relationship, no matter if you monitor the number of jobs or specialists in the market since the year 2000, for instance, earnings of individuals in a particular sphere, or demand for the product or service in a certain period.
• Histograms are good for measuring frequencies. These graphs visualize the distribution of numerical data displaying, as a rule, a range of values that are easy to interpret.

There are plenty of visualization tools available online, and the users can choose those that best match their approach to data representation and purposes. Since Google is nowadays the richest source of data, no wonder google data visualization is in demand and there is a versatile, yet easy-to-understand and to-access tool for the purpose — read on about it.

Google Data Studio

It is a free tool allowing the users to create interactive dashboards, as well as customized and clear reports that can be scheduled and are easy-to-share.

This Google data visualization software is really great because it includes the following features:

• Live data analysis with the built-in connections to over 200 data sources, Google ones (like Google Sheets, Ads, Analytics, Big Query), and external.
• Reports and visuals full customization through various visual elements (tables, graphs, scoreboards, maps, and so on), as well as the possibility to use third-party designed visual elements and to design your own, to add as many pages and charts as you need.
• Dynamic controls multifaceted filters for reports and pages.
• Advanced formulas.

Google Data Studio can be a life-changer in any sphere based on data, whether it’s a business, a hobby, or anything else. Still, the choice is not restricted by this tool alone.

Conclusion

Data visualization works better because the human brain comprehends visual information and spots patterns and trends better when they are presented in some visual form. Data visualization thus helps users to gain insight and make better decisions faster. Being an expert in data scraping, we realize the importance of data visualization for our clients and provide it on demand.

If you need data visualized in a clear and meaningful way, just contact our expert, schedule a free consultation and DataOx will do it for you!

Originally published at https://data-ox.com on March 2, 2021.