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.
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.
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:
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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:
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:
The different visualization techniques can be broadly classified into the following types:
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:
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:
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:
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.
Data Visualization refers to the representation of data in a visual context, such as charts, graphs, or maps, to help people understand complex patterns, trends, and insights in data. By using visual elements, data can be easily and quickly absorbed and interpreted.
Common techniques in data visualization include the use of bar graphs, pie charts, histograms, scatter plots, and heat maps. More advanced methods might include geospatial data mapping, treemaps, parallel coordinates, and network diagrams.
Data visualization can be applied in a wide range of fields, including business, health care, social sciences, finance, and more. In business, for instance, it's used to visualize sales trends, customer behavior, and operational efficiency. In health care, it can help track disease spread, analyze patient records, or illustrate health trends.
Data Visualization aids organizations in making informed decisions by turning large and complex datasets into easily understandable visuals. It helps to identify patterns, trends, and outliers in data, which can lead to improved strategies, optimized operations, cost reduction, and potential risk identification.
When creating data visualizations, it's important to keep your audience in mind and make sure the visuals are clear, accurate, and easy to understand. Choosing the right type of chart or graph to represent your data is crucial, as is ensuring the data you're using is accurate and reliable. Good data visualizations should not only look good but also clearly communicate the information without causing confusion.
