In the era of rampant digital transformation and the rise of data science, every industry has been impacted by new-found technologies like data analysis and visualization. About 90% of the entire world’s data was built in the last 4 years, and 463 exabytes of data are expected to be made every day globally.

Companies can now attract and interest new consumers, allow personalized experiences and keep the existing customers interested through data analysis and visualization. The customer data available to firms has been ever-increasing, leading to more and more possibilities to elevate business growth.

Rest assured, data analysis and visualization are the prime future, and every sector – education, business, or even healthcare will be driven by them. Read on to know more.

Data Analysis

Data Analysis or Data analytics is the science of inspecting data to find details or information that aids decision-making or elevates knowledge on specific topics. Data is subjected to operations in this process.

Data analysis finds use in several industries and aids in achieving business and research targets through precise conclusions. This mainly enhances the working of those operations which were undefined.

The process of data analysis has also been altered to a mechanical sector based on varied codes and algorithms which process raw data to give rightful conclusions.

Types of data analysis

There are two main types of data analysis – based on the information you presently have. These two types are Qualitative data analysis and Quantitative data analysis. Let’s look into both of these in depth.

Quantitative data analysis

Quantitative analysis has got more to do with the statistical aspect of data. This type of data analysis informs us about what is occurring and if the trends indicate a rise or a fall. There are two different categories in Quantitative data analysis, namely:

•         Descriptive analysis: Descriptive analysis will allow you to observe the trends or patterns in a certain data set. This analysis has processes like calculating frequencies, percentages, and measurement of the central tendency – mean, mode and median.
•         Inferential analysis: This category of Quantitative data analysis is used to examine the correlations and differences between specific data sets. The processes like t-Tests, ANOVA, and Chi-Square are included in Inferential analysis.

Qualitative data analysis

Qualitative data analysis deals with non-numeric data like video, audio, images, transcripts, recordings, and texts. You can understand how the data changes through qualitative data analysis.

Data Analytics techniques

There are many components of data analytics that can be utilized to process any kind of data set. Primary techniques include text analytics, data mining, and business intelligence.

•         Text Analytics: Text analytics involves the analysis of vast amounts of text and figuring out algorithms. The typical applications are Pattern Recognition and Linguistic Analysis.
•         Data Mining: This technique involves the breakdown of huge chunks of data into small packets that help achieve a particular purpose or target.
•         Business Intelligence: Business intelligence aims to transform data into business strategies for a certain commercial entity. Pricing in major multi-national firms takes place through the process of business intelligence.

Data Visualization

Data analysis helps us achieve insights, but this same data becomes even more valuable when visualized. Data visualization represents data with standard graphics and images like plots, charts, infographics, and animations.

These visual images depict complex data relationships simply and provide data-based insights in an easily understandable way. It truly improvises our understanding of concepts and analysis, which leads to more effective and fast decision making.

Data visualization has a number of applications and purposes – but these are not limited to the data and engineering teams. Management also uses data visualization for the benefit of organizational hierarchy and structure, whereas data scientists use the process to find trends and patterns.

 

Types of Data Visualization techniques

Now that we know what data visualization is, you may ponder the chart types or what kind of graphs you should use? This brings us to the major types of data visualization and when you should use the same or the audience that it works with.

•         Temporal – If data visualization is linear in nature and is one-dimensional, they are temporal data visualization techniques. Some examples of data visualization are polar area diagrams, timelines, and line graphs.
•         Hierarchical – These data visualizations help to order smaller groups present in larger groups. Do you remember the tree diagram from school? If yes, that is an excellent example of hierarchical data visualizations.
•         Network – Network data visualizations work on the fact that many data sets together are connected to each other and have a visual data network. Examples include matrix charts, parallel coordinate plots, non-ribbon chord diagram plots, etc.

Data Analytics Vs. Data Visualization

In the race of Data Analytics Vs. Data Visualization, there is no true winner. Data analytics and data visualization both play integral roles in the digital transformation revolution, but they also have certain differences.

For instance, data visualization has an end goal of visually communicating information in an understandable and clear way. On the other hand, data analytics helps businesses better their decision-making process through the correct analysis of data.

Data visualization can be considered as the pre-requisite step of data analytics. Both the processes together provide conclusions and insights from varied data sets.

Data visualization could either be interactive or static in terms of tools, techniques, and methods. Through the interactive data visualization, you can go down into the details of graphs through mobile ad computers.

Whereas data analytics could be predictive analytics, descriptive analytics, diagnostic analytics, or prescriptive analytics.

Wrapping Up

And that’s a wrap on the comprehensive guide to Data Analysis and Visualization in the current technological paradigm. With data becoming the most valuable asset to businesses and humanity, these processes are essential to bring about change and create value.

It is essential that students and professionals must prepare themselves for this data revolution and gain skills to facilitate better decision-making and practical application of technology.