Big Data Visualization

What is Big Data Visualization?

Big data visualization is the process of transforming extremely large, complex datasets into graphical representations. The goal is to share insights from big data analytics and/or allow stakeholders to explore the visualizations to gain new insights.

How Big Data Visualization Works

Big data visualization involves gathering unstructured, structured, and semi-structured data from sensors, social media, transactional systems, network logs, and various other data sources.

After the raw data has been stored, it is cleaned to remove errors, duplicates, and inconsistencies. This step is important because inaccurate or incomplete data can lead to flawed visualizations that can seriously impact data-driven conclusions and decisions.

Once the data has been scrubbed, it needs to be transformed into a suitable format for big data analytics. The results can then be visualized with charts, graphs, or maps.

Types of Big Data Visualization

Big data visualization examples can be categorized based on their purpose and the nature of the data they represent:

Big Data Visualization Tools

Big data requires special visualization tools that can handle the large volume, variety, and velocity of big data. They often use techniques like data sampling and distributed processing to ensure visualizations render quickly and smoothly, even when there are millions or billions of data points.

The most sophisticated tools provide a single pane of glass (SPOG) dashboard that allows users to filter the data or drill down to get a more granular view of the data being visualized.

It’s important to note that not all big data visualization tools have the same capabilities. The best choice for a specific use case will depend on factors like data complexity and real-time requirements.

Some big data analytics tools have the ability to create charts, graphs, and other visual elements, but dedicated big data visualization tools provide a wider range of visualization options. Popular dedicated big data visualization tools include Apache Superset, Plotly Dash, and Grafana.

Big Data Visualization Use Cases

Big data visualization was traditionally used for scientific and government research. Now that data aggregation, storage, and processing capabilities have advanced and can be purchased as cloud services, more industries have adopted big data visualization to gain insights from disparate data sources and make data-driven decisions.

Applications of Big Data Visualization

The impact of big data visualization has spread across various business domains. For example, this particular aspect of digital transformation has had a big impact on customer relationship management. Today, businesses of all sizes can use big data visualization to share insights into customer behavior and create more targeted and personalized marketing campaigns.

Here are some other fields where big data visualization has been helpful:

• In healthcare, big data visualization has made it easier for healthcare professionals to quickly review and understand patient medical histories.
• Financial services use big data visualization to share insights about large volumes of transaction data with their analysts.
• The telecommunications industry uses big data visualization to monitor network performance in real time.
• In the public sector, big data visualization tools can help local administrators simulate the impact of different ideas for urban planning.

Big Data Visualization Pros and Cons

Big data visualization, while incredibly powerful, comes with both advantages and disadvantages.

Challenges in Big Data Visualization

Big data visualization presents several challenges, primarily stemming from the sheer volume, variety, and velocity of the data involved.

Data visualizations can be unintentionally misleading if not designed carefully. The complexity of big data requires the user to have both a deep understanding of the data and the visualization tools that can work with it.

The Bottom Line

If you find yourself asking, “How does data visualization help with big data?” the answer is quite simple. The human brain is hardwired to process visual information more quickly and efficiently than raw data.

Big data visualization’s meaning is not significantly different from the traditional meaning of data visualization. Both types of visualization use knowledge graphs, charts, maps, and other graphical elements to represent data and make it easier for stakeholders with different skill sets to understand.

While most data analytics tools can create basic visualizations, dedicated big data visualization tools offer more options for handling extremely large datasets, integrating real-time data, and providing advanced interactive features that allow users to explore and manipulate the data to achieve specific objectives.

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