Data Visualization and Decision Making

Data is a significant asset for any business, and we have access to enormous amounts of data!

Yet, we need tools to process this data to take advantage of it, and here it comes data visualization.

This article will explain its concept, how it works, its importance, and the languages used for it.

Are you ready to capitalize on your data?

What is Data Visualization?

Data Visualization (DV) encloses recasting information into a visual context.

For example, a map or graph can make it easier for our brains to process and understand the data.

This approach, in turn, helps us pull valuable insights faster.

Data visualization is particularly convenient when dealing with large data sets.

Visuals ease pattern identification within big chunks of data.

Data Visualization vs Data Analytics

Data visualization and data analytics are both fields within Data Science.

While visualization shows data visually, analytics studies it to get actionable insights.

You could say data visualization is one of the tools used in data analytics to get better insights.

About its workforce, data visualization falls into the skills of data engineers. Meanwhile, data analysts are responsible for analytics.

Data Visualization and Big Data

As we've seen before, Big Data pulls unique insights with massive data collections.

The amount of data usually exceeds the traditional database software's analysis.

Due to simplifying analyses, data visualization has become a vital tool for Big Data.

Also, it helps present information collected from Big Data faster.

Yet, these scenarios need more complex representations, like heat maps and fever charts.

Proper Big Data Visualization requires a specialist, IT, and management involvement.

Given the amount of data, it is also essential to install quality control practices.

Benefits of Data Visualization for Businesses

1. ‍Time. It's easier to process visual information, and visual representations get the bottom line faster for large amounts of data. ‍
2. Understanding. Visualization helps better interpret complex datasets, their correlations and their insights.
3. Engagement. Good visuals can improve user engagement. The better they understand data, the more likely they will respond positively.‍
4. Decision-Making. Visual data also speeds up processes to make more informed business decisions faster.‍
5. Distribution. Visualization makes relevant data readable and available across the company and for all decision-makers.

How Does Data Visualization Work?

There are a lot of different techniques when it comes to data visualization.

Below, we'll look at Noah Illynsky's four pillars of effective data visualization.

We'll also check use cases and techniques to get you started.

According to Noah Illynsky, there are four pillars to achieving effective data visualization:

1. Purpose. This stage covers why you are creating a specific visualization. Who the content is for, what they need to know and how they will consume it?
2. Content. Content is which data you'll include in your visualization, with a focus on relevant data for your purpose. Too much data may hinder understanding.
3. Structure. Here, the focus is on how you'll visualize the content, such as comparison, change, composition, correlation, and geography.
4. Formatting. Formatting revolves around appeal, affecting font style, colors, icons and density. Yet, these will only help if you have a clear purpose.

Data Visualization Techniques

1. Data Visualization Maps

Heat Maps use color variations to display data and help identify trends.

Scatter Plots show the relationship between two variables on an x- and y-axis, with dots as data points.

Likewise, as a variation of scatter plots, bubble clouds use different circles in a two-dimensional field.

2. Data Visualization Charts

Pie Charts are easy to read and help illustrate proportions.

This technique is one of the most common for data visualization.

Line Charts show how variables change over time.

Bar Charts or graphs are also quite common. They apply mainly for comparisons.

Likewise, Gantt Charts show project timelines or task progression.

3. Populational Data Visualization

There are several ways to show populations' subgenres in data visualization.

For instance, Population Pyramids use stacked bar graphs to illustrate a population's distribution.

Pyramids break down the population by age and sex group, also enclosing Choropleth Graphs.

These show numerical values for specific geographical regions using color, shading, and patterns.

Choropleth graphs help viewers see how variables change depending on the area.

4. Histogram Data Visualization

Histograms show data distribution over a period, and are great for seeing the frequency of a specific occurrence.

Also, Treemaps show hierarchical data through nested figures, comparing parts of the whole when having many categories.

Timelines help picture events in chronological order. While simple, formatting them can make data stand out.

5. Business Data Visualization

For businesses, Bullet Graphs are a fantastic way to show performance.

It includes the actual and target values for a specific business metric.

Also, Area Charts are line chart versions showing many values over time.

These are great for showing changes in quantities over a set period.

Last but not least, Correlation Matrices display correlation coefficients between variables portray relationships.

Best Languages For Data Visualization

Data Visualization with Python

Developed by Guido van Rossum, Python is part of the general-purpose languages category.

On Data Visualization, Python is well-suited to handle massive amounts of data.

You can use it to build deep learning models and perform non-statistical tasks.

Also, you can combine it with several DS libraries, like Matplotlib, Keras, and TensorFlow.

Data Visualization with R

R is a statistical programming language and a software environment.

With powerful graphics and cross-platform compatibility, R doesn't need a compiler.

Also, it has a robust ecosystem with extensive statistical packages, such as ggplot2, Leaflet, and Plotly.

R is also great for building statistical models and creating graphics.

Data Visualization with JavaScript

Netscape created JavaScript (JS) as a lightweight programming or scripting language.

JS is also object-based, general-purpose, dynamic, and interpreted.

JS is less popular for data visualization than R and Python, yet it has excellent libraries!

Some examples are D3, Charts, and Victory.

Conclusion

As you can see, Data Visualization is a big part of Analytics.

It's especially relevant in a world where data multiplies by the millisecond.

Visualization simplifies data so businesses can make better decisions faster.

We hope this article has given you a comprehensive overview of this topic!

Further, we hope it gives you all info you might need to tackle Data Visualization.