Data is a significant asset for any business or organization. We now have access to enormous amounts of data! Yet, we need tools to process this data to take advantage of it. This scenario is where data visualization comes in. This article will explain its concept, how it works, and its importance. We'll also cover the various tools and languages used for data visualization. Are you ready to capitalize on your data?
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 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.
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. These include 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.
Data Visualization helps businesses gain insights faster. Companies can check needed advances, predict sales, and know more about customer behavior. Further, they can easily bring said info to stakeholders. Below, we'll focus on how data visualization makes it possible.
It's easier to absorb and process visual information. Visual representations help get the bottom line faster for large amounts of data. You can then take swift actions when needed, positively affecting your business.
As we've mentioned, visual information is easier to understand. Visualization helps you better interpret complex datasets and their several correlations. It can also lead to deeper insights than looking at raw data.
Good visuals will improve viewers' engagement by maintaining their interest. This edge helps stakeholders and board members see your points more clearly. The better they understand your data, the more likely they will respond positively.
Visual data not only makes conveying information easier. Also, it speeds up processes considerably. It allows you to make more informed business decisions faster.
Visualization makes relevant data readable and available across your company. Every decision-maker in your company can assess performance and make appropriate decisions.
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. These are purpose, content, structure, and format. We'll go over these up next.
This stage covers why you are creating that specific visualization. What do you hope to achieve? You should consider who the content is for, what they need to know, and how they will consume it. The purpose of your data visualization should be as specific as possible.
Content is the selection of which data you'll include in your visualization. Here, you should focus on what data is relevant for your purpose. Too much data may hinder understanding.
This step focuses on how you'll visualize that content: the page layout. You should decide on what structure to use according to your purpose. The most common meanings are comparison, change, composition, correlation, and geography. You can represent the same data set differently depending on your focus. Types of structure include bar charts, line charts, scatter plots, and pie charts. We'll look at these and others in a later section!
Finally, formatting revolves around appeal and focus. It affects font style, colors, icons, and pattern density, among other aspects. These arrangements will only help your data visualization if you have a clear purpose. Also, it won't be valuable without relevant content and adequate structure. Formatting should help viewers focus, not distract them!
We know what data visualization is and its main principles. Let's move to some of its uses!
Market and consumer data research costs hundreds of billions of dollars every year. Visualization helps teams see how their strategies affect consumer trends over time.
Tracking performance is standard in the investment sector. Charts are a common tool for price movement analysis. Looking at how prices change over time, experts can notice trends.
We have all seen country maps displaying how each state or district has voted. Data visualization makes it easy to process this information.
Choropleth maps are quite popular in the healthcare industry. These allow health professionals to see how variables change depending on the territory. Plus, dashboards with a patient's medical history can be helpful if a new doctor comes on board. The latter makes finding the most relevant information about the patient easier.
Visualizations in Data Science are for individual use or to show to a specific audience. It's common for scientists and researchers to use programming languages or proprietary tools. In turn, this approach allows for more complex data analysis.
So, how exactly do we visualize data? Here are some examples!
Heat Maps use color variations to display data and help identify trends. In this context, Scatter Plots show the relationship between two variables on an x- and y-axis, with dots as data points. These are useful when dealing with large data sets. Likewise, as a variation of scatter plots, bubble clouds use different circles in a two-dimensional field.
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.
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. This type also has 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.
Histograms show data distribution over a period. These are excellent tools 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. Further, Timelines help picture events in chronological order. While simple, formatting them can make data stand out.
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, there are Correlation Matrices. This table displays correlation coefficients between variables using cells to portray relationships. They also use a color scale to show correlations.
There are a few languages you can use for data visualization. Here, we'll cover the top three.
Developed by Guido van Rossum, Python is a prevalent language. Released in 1991, it's part of the general-purpose languages category. As a result, it's versatile and usable in various projects. 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.
R is a statistical programming language and a software environment. First released in 1993, it focuses on statistical computing and data visualization. R has powerful graphics, offers cross-platform compatibility, and doesn't need a compiler. Also, it has a robust ecosystem with extensive statistical packages. These include ggplot2, Leaflet, and Plotly. R is also great for building statistical models and creating graphics.
As the data visualization market grows, there are countless tools developed. Most of them have a business intelligence approach. These help users create automatic dashboards. Further, they allow tracking KPIs and interpreting results. This section will describe some of the most popular data visualization available.
Released by Microsoft in 2014, Power BI is a scalable business intelligence platform. Power BI offers real-time analytics and trend analysis. As expected, it integrates with various Microsoft products and services. One of its most relevant features is its end-to-end data protection.
Power BI has a mobile app and is easy to learn for Excel users. The platform supports many backend databases, including Teradata, PostgreSQL, Oracle, and Google Analytics. On the downside, Power BI uses a lot of CPU, and its desktop version is incompatible with Mac. It also isn't possible to work with many varied data sets. Power BI offers Pro and Premium versions. You can find more about them here.
Tableau is an extremely popular data visualization and analytics platform. One of its most popular features is how fast it outputs graphical representations. Tableau is powerful and flexible. You can quickly build visualizations with this tool with top-class performance. The platform has Salesforce's backup and Slack integration. Unfortunately, Tableau has a steep learning curve and can be a bit pricey. You can take a look at its plans and pricing here.
This business intelligence platform relies on Artificial Intelligence (AI). IBM Cognos Analytics interprets data for you, providing actionable insights. Another benefit is sharing data and importing it from various sources. Regarding its negative aspects, IBM Cognos Analytics lacks customer support. It also has high-cost maintenance: you can look at pricing on this page.
This tool uses AI to help users learn and use data efficiently. Qlik Sense includes an associative data analytics engine and scalable multi-cloud architecture. The tool has automated action triggers, a drag-and-drop interface, and AI insights. Also, it offers search and conversational analytics and a mobile version. On the downside, Qlik's user interface might need some work. You can request a free trial, after which you can visit their pricing site.
Jupyter Notebook is a web-based application. It helps you create live code, visualizations, and narrative text documents. Further, it's handy for Statistical Modeling and Interactive Computing. Further, it's useful for Data Cleansing and Machine Learning, among other tasks. It allows for rapid prototyping and provides visually appealing results. As for the negatives, it's not easy to collaborate in Jupyter Book, and code reviewing can sometimes be complex. This software is free! You can install Jupyter by following these instructions.
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.