Big Data Visualization with Generative AI
Selecting the right visualization tools is crucial for effectively analyzing big data. A perfect visualization tool has the power to generate accurate visual diagrams that can guide decision-making. On the other hand, inadequate visualization can result in losses for the organization.
Daily on Facebook, a staggering 4 petabytes of data are uploaded, comprising a diverse range of content such as videos, images, and text. Identifying patterns and extracting meaningful information becomes incredibly challenging without visualizing this vast amount of data.1. Facebook uses "HiPlot" to analyze and visualize it
2. Another prominent organization, IBM, uses "Big SQL," integrated with other visualization tools like Zeppelin notebooks, data science, Tableau, and Cognos. Amazon uses AMZ Base, Amylaze, SellerApp, etc.
Selecting efficient big-data visualization tools will help change complex and extensive volume data into simple, human-readable visual diagrams. This pictorial diagram helps analysts predict that it will lead to business improvement. Following are the examples of data visualization:
2. Bubbles Clouds
3. Bullet Graphs
4. Heat Maps
5. Time Series Chart
Data visualization is the representation of data through the use of common graphics, such as charts, plots, infographics, and even animations.
Challenges Faced in Data Visualization
It is a large-volume, complex dataset. So, such data can not be visualized with the conventional method as it has many limitations.
1. Perceptual Scalability
Human eyes cannot extract all relevant information from extensive data. Sometimes, the desktop screen has its limitations if the dataset is large. Too many visualisations are not always possible to fit on a single screen.
2. Real-time Scalability
It is always expected that all information should be in real-time, but it is hardly possible as processing the dataset requires time.
3. Interactive scalability
Interactive data visualization helps to understand what is inside the datasets, but visualizing them takes a long time as their volume increases exponentially. But the challenge is sometimes the system may freeze or crash while trying to imagine the datasets.
How Generative AI Driving Data Visualization
1. Data Augmentation
Using GenAI, you can increase the diversity and size of the dataset to improve the accuracy and robustness of visualizations and insights.
2. Anomaly Detection
Anomaly Detection is critical for Data Visualization to create the correct pattern and distributions. GenAI can quickly help to identify anomalies or outliers in the data. GenAI can easily trim down the manual effort behind it.
3. Data Imputation
Impute missing values by learning from the existing data patterns and distributions, which helps generate more complete visualizations and improves the overall quality of insights. Using GenAI, you can quickly achieve it.
4. Data Synthesis
To explore hypothetical scenarios or simulate data for what-if analyses, which can provide a broader understanding of potential outcomes and patterns in Data Visualization. Again, GenAI can easily do it for you.
5. Code Generation
A natural language interface for code generation helps reduce the efforts of BI developers in writing complex code/functions. Example - You can easily create your required DAX query of Power BI from ChatGPT or BART.
How is data visualisation with Generative AI essential for businesses?
With Generative AI and Prompt engineering, Enterprises can get real-time insights which in terms helps in improving decision-making. Generative AI tools give a chance to drive depth in the vast data. As a result, one can find new patterns or errors in the data with Knowledge Graph and Patten analysis.
1. Better Data Analysis
Visualizing tools that generate reports helps the organization's management committee decide what will happen in advance. Visualization tools generate information that is very important to understanding the current growth of the organization.
2. Decision making
The human brain responds quickly to visual diagrams instead of text data. Visualization tools generate charts that help make fast decisions and grow the business simultaneously.
3. Help in sensing complex data
It is stored very unstructuredly. As per the definition, it contains various data like video, audio, images, and textual data. Such combined dataset reading is hard for humans as that dataset is in a complex format. With the help of its tools, meaningful, relevant information in simple pattern extraction is possible from such datasets. Sometimes, pertinent new patterns can be explored even if there are any errors in the datasets.
Once its tools read the dataset, they will plot diagrams. So, it saves time and money, and its visualization is impossible without any means.
5. Error detection and correction
Its tools are also helpful in finding errors in the dataset. If a dataset contains any error, it is possible to take some actions to solve that. And it is possible to arrange the dataset as per requirement.
Data Visualization Dashboard designs and techniques are used to display visual objects like charts and graphs. It is used to communicate the message and understand patterns easily and frequently. It also helps to understand the relationship between the data better.
Big Data Visualization Tools
Nowadays, there are many tools. Some of them are:
1. Google Chart
3. Microsoft Power BI
4. D3 (Data-Driven Documents)
1. Google Chart
Google Charts is one of the most accessible tools for visualization. With the help of Google Charts, you can analyze small and complex unstructured datasets.
We can implement simple charts as well as complex tree diagrams. Google Charts is available cross-platform as well.
The Tableau desktop is a very easy-to-use visualization tool. Two more versions of Tableau are available. One is "Tableau Server," and the other is cloud-based "Tableau Online." Here, we can perform visualization operations by applying drag-and-drop methods for creating visual diagrams. In Tableau, we can create dashboards very efficiently.
3. Microsoft Power BI
This tool is mainly used for business analysis. Microsoft Power BI can run on desktops, smartphones, and tablets. This tool also provides analysis results very quickly.
D3 is one of the best data visualization tools. D3.js is an open-source visualization tool.in
Datawrapper is a simple tool. Even non-technical persons can use the Datawrapper tool. Data representation in a table format or responsive graphs like a bar chart, line chart, or map draws quickly in the Datawrapper.
Databox is another visualization tool. It is an open-source tool. The whole data set can be stored in one location in the Databox tool. Then, discover the insight data and perform visualization operations. In the dashboard, you can view or match data from different datasets. Many more tools are available per requirements and based on datasets. Visualization tools are chosen.
Best practices for Big Data Visualization
It is unstructured, and such data can be easily stored on a NoSQL database like MongoDB or relevant information needed to extract from the data and hold on a SQL database. Then, from that dataset, with the help of its tools, some charts, like bar charts, pie charts, etc., need to be plotted. Then, from those visual charts, analyses can be performed.1. NoSQL database is mainly used for storing unstructured data like it.
2. Choose appropriate tools
3. Use different algorithms as per requirement.
4. Visualize the dataset.
The future of Data Visualization is Real-Time Insights with Generative AI
In today's data-driven era, nothing holds more value than data itself. Companies and organizations across industries have recognized its importance and actively embraced it. To keep up with this trend and foster personal growth, we must familiarize ourselves with big data and its visualization tools. By understanding datasets and investing our time in this valuable field, we can unlock countless opportunities for success. As the demand for skilled professionals in big data continues to rise, it becomes clear that this field offers an exciting path for future growth and advancement.