All about Data Visualization in the Field of AI

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Robert Koch

I write about AI, SEO, Tech, and Innovation. Led by curiosity, I stay ahead of AI advancements. I aim for clarity and understand the necessity of change, taking guidance from Shaw: 'Progress is impossible without change,' and living by Welch's words: 'Change before you have to'.

Data visualization is a fun and exciting way to present data that can be used in all sorts of ways. The following are some definitions, applications, techniques and benefits of data visualization: Definition: Data Visualization–the representation or communication (through text or images) of quantitative information using charts.

But what does data visualization have to do with AI and how is it applied here? You can learn all about data visualization and its application in AI in this blog post.

Table of Contents

• How is Data Visualization Used in AI?
• Why do we need data visualization?
• Challenges and concerns with data visualization
• Types of data visualization techniques
• What is Big Data visualization?
• How to use data visualization in AI
• Examples of data visualization in AI
• The future of data visualization with AI
• Conclusion
• FAQs

Data visualization is a process by which information is presented in a visual format, such as graphs, charts, maps, infographics, and so on. Data visualization allows for an easy understanding of data sets and makes them easier to share across teams. It makes data analysis faster and helps identify the patterns and outliers in the data with more ease. Data visualization can thus also be regarded as a form of visual reporting and representation of data.

Data visualization is one of the final steps in the data analysis process, where the findings and insights gathered from data are presented in a visual format. It is also considered to be part of the data presentation architecture methods.

History of data visualization

Data visualization has been an integral part of data science ever since humans have tried to make sense of the world around them. But modern data visualization is said to have originated in 1644 when Flemish astronomer Michael Florent van Langren used visual representations for statistical data. Since then, data visualization has evolved with data science as a tool to understand and communicate information effectively.

Data visualization provides a means to understand large data sets without processing them as computers do. Regardless of skill level or technical knowledge, anyone can look at data visualizations and gather insights from the data. It saves time, helps archive results and analysis in an easy-to-understand format, and helps disseminate the information most effectively.

Human brains are well equipped to consume visual information. It is indeed a fact that a picture speaks a thousand words. When data is presented visually, we can process information more efficiently and make relevant decisions accordingly. Our eyes can grasp visual cues much faster compared to bulky textual information. Thus, by using data visualization, even new concepts and complex ideas can be simplified.

With the huge amount of data being generated every minute in this digital world, understanding the information behind all this data comes from careful analysis. And this cannot be done at the required speed unless we have visualizations.

Here are some more specific reasons why data visualization is required:

• Faster analysis and improved decision-making – Data visualizations are essential to identify data trends and patterns quickly. Pictorial representations can easily show data trends. For instance, you may have a million records of housing data. If you want to, say, know the demographics of housing ownership, it might be a hassle to go through all the records and find the aggregates. With a pie chart of the same data, you can easily get to know which age groups constitute what percentage of home ownership.
• Presentation of data and information dissemination – Sales pitches, and marketing pitches, will all require data-based insights and derivations that support the strategy that is being proposed. To let the stakeholders easily understand the data behind the decisions, it is important to get them to understand it quickly and easily. Data visualizations allow for such easy information dissemination.
• Faster analysis and improved decision-making – Data visualizations are essential to identify data trends and patterns quickly. Pictorial representations can easily show data trends. For instance, you may have a million records of housing data. If you want to, say, know the demographics of housing ownership, it might be a hassle to go through all the records and find the aggregates. With a pie chart of the same data, you can easily get to know which age groups constitute what percentage of home ownership.
• Simplify complex information – When trying to make sense of large amounts of data, visualizations can go a long way in helping you identify new patterns and errors in data. Different graphs and charts can be used to quickly notice any outliers and grasp the emerging patterns in data. Data visualizations are also more effective and faster than tabular forms or reports.
• Better strategizing and forecasting – Data visualization can help in identifying future patterns and thus help with predictions about market conditions and business parameters. This could help decision-makers carry out proactive strategizing and make informed decisions.

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The challenges and perceived disadvantages of using data visualization usually come from how well you implement it. For instance, choosing the wrong type of visualization for a set of data can result in confusion or minimize understanding. Here are some possible concerns that could arise from data visualization:

• Improper data visualization design could present inaccurate or biased information – Interpretation solely based on visualizations might lead to inaccurate conclusions. Correlation among data may not always denote causation, and failing to take further analysis can lead to wrong conclusions.
• The core messages that are to be derived from the data could get lost in the visualization – When creating effective visualizations, you must consider the core message to be delivered, avoid distracting components, and be mindful of the overall design, color scheme, and technique you use to present the data. The data visualization should be both visually appealing and easy to understand with little mental effort.

There are multiple types and subtypes of data visualization techniques available. Understanding which type to use for the best presentation of information is a big part of data presentation. Some of the major visualization techniques are:

1. Pie Chart
2. Bar Chart
3. Histogram
4. Gantt Chart
5. Heat Map
6. Box and Whisker Plot
7. Waterfall Chart
8. Area Chart
9. Scatter Plot
10. Pictogram Chart
11. Timeline
12. Highlight Table
13. Bullet Graph
14. Choropleth
15. Word Cloud
16. Network Diagram
17. Correlation Matrices

The different visualization techniques can be broadly classified into the following types:

• Charts – Charts present data with the help of visual aids like legends, data points, and color variations to show the correlation between different data points. For example, sales data can be shown as a line chart representing sales performance over time.
• Plots – Plots mark data points over a 2D or 3D plane marked by the respective axes. Scatter plots, bubble plots, and histogram plots can all be used to visualize data relevant to two or more variables.
• Maps – Maps such as heat maps and dot distribution maps can be a layout of geographical or spatial information showing data distribution across the map.
• Table – Data is presented in the form of rows and columns.
• Graphs – Graphics can show the relationship between different objects with the help of diagrammatic cues such as points, lines, segments, areas, curves, and more. Graphs may also use axes to show data for certain variables.
• Infographics – An infographic combines data visualization and text information to present a particular concept or details about a wider topic.

Besides the regular charting and visualization techniques, a data analyst can develop newer visual models depending on the project’s needs. They may also use a combination of visualization techniques to solve the issue. Some techniques used in the case of semi-structured and unstructured data are:

1. Kernel density estimation
2. Box and whisker plot
3. Word clouds
4. Network diagrams
5. Correlation matrices

Big Data refers to the use of data visualization techniques to present large amounts of data to the degree handled by tools like Big Data and machine learning systems. Unlike small data sets, dealing with huge data sets comes with several challenges. Big Data analysis requires huge processing power and advanced data algorithms to sift through millions of datasets and get the required results presented in the visual format.

Data analysis tools use informational dashboards with a good choice of visualization techniques to present data coming from multiple sources, including IoT, social media, website content, and so on. Some of the popular tools used for big data visualization are:

• Jupyter
• Tableau
• Google chart
• D3js

AI and data visualization are used to power advanced data analysis tools. They are used conversely with each other. While AI models can be used to create advanced data visualizations from large sets of data, data visualizations can, in turn, be used by AI to gather information and insights as well.

AI has long been used for the automation of data collection and analysis. Applying it to data visualization allows us to create customized dashboards, real-time data visualizations, and more. AI is also instrumental in creating interactive data visualizations that allow for a more engaging experience.

AI can be used to build advanced infographics and interactive dashboards in a short period. AI can help make visualizations faster and more accurate as well.

Conversely, data visualizations can be fed as inputs to AI models for easier information gathering. For instance, AI robot systems can simply scan data tables and arrive at the desired outcome or the action that needs to be taken. Applications such as AlphaGo use advanced AI models to play complex strategic games.

A good number of tasks still require human interference, even when AI is supposed to make it all automatic. For instance, while an AI-powered automatic car can allow for autonomous driving, it would still require a human operator to make crucial decisions while driving. This kind of hybrid approach is also called visual analytics, where human interference is combined judiciously with AI software operations to achieve the best possible results.

Applications such as Wolfram Alpha and Microsoft PowerBI Natural Language Querying are examples of this kind of approach. Some ways AI can enhance data visualization are:

• Optimize the data discovery process – AI can allow dynamic real-time data analytics and integrate it with interactive real-time dashboards and visualizations.
• Improve processing capabilities – AI models can make the best use of available computing resources and allow data visualization tools to identify deeper connections in Big Data.
• Better search capabilities – AI can help build more powerful search capabilities into data visualization dashboards.
• Add-on features – With the help of AI models, visualizations can also include advanced functionalities aided by tools like natural language processing, image processing, and more.
• Better predictive models – Visualizing future trends can be made more accurate and faster with the help of reliable machine-learning models. This can help organizations incorporate data visualization into their business strategy and derive better actionable items based on data.
• Validation of AI models – Using a combination of both AI and visualization techniques can help build stronger systems that are viable and practical to be put into production.
• Visualization recommendation – AI techniques can be used to get different variations and choices of visualizations depending on the needs. This is similar to how AI systems are currently used to recommend books, shopping items, music playlists, and so on, depending on the user’s preferences.

Visual analytics can be used alongside human speech interpretation software and is expected to evolve into more advanced applications. AI applications have been able to render digital images just from human speech input, a considerable feat that can evolve even more in the coming years. In the future, AI could make it possible to interact with a visual system with just gestures and speech commands. AI can be used to generate completely new images, fonts, pictures, and art based on just text prompts.

Conversely, AI can also be used to generate textual information from a given image. AI can quickly grasp the information presented in an infographic and summarize the content. It can answer queries about project progress from a project schedule Gantt chart. There are countless scenarios where AI systems could be used to both enhance and interpret data visualizations.

Another possible implication of AI and data visualization could be the automated processing of medical images and visualizations. AI tools can be used to decipher medical data, both images, and unstructured data, to derive accurate diagnoses.

AI applications can also be used to enhance an input media file-based visualization. The developments in the AI and data visualization space can thus possibly give rise to AI applications that could act like human business analysts, translators, radiologists, and more.

Data visualization is one of the best tools available in the modern world to tell a story. It aids in communicating ideas backed up by data. Making the best use of it will help you make better decisions and open doors to more learning and faster development.