What are common Visual Big Data Analysis Technique​

Business intelligence in large volume data sets is known as big data since basic tools cannot process them.

These datasets usually come handy but sometimes it is very difficult to derive meaning from it without adequate resources. Visualization plays a critical role by:

Simplifying Complexity: Converting large datasets into more comprehensible forms through the use of other forms of media.

Identifying Patterns: To make trends, correlations or outliers exposed with a single glance.

Facilitating Decision-Making: Another broader category is about empowering stakeholders with viable information.

Decision makers across industries including business, government and research fields decide on visual analytics to manage aspects such as finance, health care, marketing and urban development.

Quantitative data analysis and report writing can just be imagined as; Now, let us discuss certain visual techniques that are applied to make it possible.

Common Visual Techniques for Big Data Analysis

1. Bar Charts

One of the simplest methods of comparing discrete data is by using the bar charts. They use rectangular bars to represent data and they must be in proportion to the value it is being used to represent.

• Use Case: For example when we are consolidating revenue by product type or summarizing survey that was conducted or even checking trends of monthly expenses.
• Big Data Adaptation: Real-time applications like Tableau and Power BI provide the feature of interactive and dynamic bar charts that may contain large data and may be updated in any instance.

2. Line Graphs

When figures are being represented along the Y-axis and categories along the X-axis, line graphs are preferred. They join distinguished data points with a line, and one can easily observe transitions or variations.

• Use Case: Watching changes in a company’s share value, the number of visitors to a website, or the condition of the atmosphere.
• Big Data Adaptation: Sophisticated tools process tens and hundreds of millions of pieces of time-series data with zooming and corkscrew capabilities.

You may also read: Popular Big Data Visualization Tools: A Detailed Comparison

3. Scatter Plots

This type of graph uses dots on a graph to depict the relation between two variables. Especially it is used for searching for correlation, clustering or outlier.

• Use Case: A focus on the quantitative comparison of marketing expenses and sales figures.
• Big Data Adaptation: Scatter charts when used in conjunction with clustering algorithms can be used for clustering large data sets hence finding imperceptible patterns.

4. Heatmaps

Maps are graphical representation of the intensities of some phenomena where the point or area of higher intensity has been colored differently from that of a region of low intensity. They are most helpful in representing large sets of data in the easiest and least space consuming ways.

• Use Case: Charting website click through rates, finding the locations or countries on the map, or the correlation matrices.
• Big Data Adaptation: Realtime heatmaps that may cover the traffic on the networks or the hotspots in the data from the sensors of IoT.

5. Treemaps

Treemaps represent the hierarchal data through one rectangle placed over another rectangle. The size of the rectangle in each pair is relative to the value of the data category it outlines.

• Use Case: Showing amounts of files saved in a storage system or sales figures by category and subcategory.
• Big Data Adaptation: These are usable with large data, with the capability for drill down, to examine a particular hierarchy.

6. Network Graphs

Network graphs show the existence or lack of a relationship between nodes. Nodes could be anything – an individual, a group, an organization, or anything – and edges are the relationship of one node/thing to another.

• Use Case: When studying social networks, reviewing a supply chain, or searching for signs of fraud.
• Big Data Adaptation: Software Gephi and Neo4j exist to plot millions of nodes and edges and show interrelations.

7. Geographic Maps

Choropleths are specifically important when dealing with geographic relationships within a set of data since maps provide a basis for visual comparison of data sets with similar attributes. This assists in providing spatial dimension to data analysis.

• Use Case: It becomes rather easy to plot sales distribution on aspects such as regions or even monitor delivery routes.
• Big Data Adaptation: Geographical Information Systems (GIS) deal with geographical big data, that allows layers of data like population density or weather.

8. Word Clouds

Word clouds are visual representations of a number of words in which the size of a word determines the number or significance of the word.

• Use Case: Converting customer response feedback, rating, or comment to customers.
• Big Data Adaptation: NLP (Natural Language Processing) tools prepare a large amount of text data for meaningful word clouds.

9. Box Plots

In analyzing data distribution, box plots give information on the outliers. It shows the minimum, first quartile or lower half, median or second quartile or second half, third quartile or upper half, and maximum of the given data set.

• Use Case: For example, after completing a test, using the obtained scores; after organizing a survey, using the obtained responses; or in the process control where data on the state of processes is collected.
• Big Data Adaptation: Box plots are extended to accommodate multidimensional data and are further used with statistics.

10. Dashboards

Dashboards is a tool that incorporates a number of different visualizations in singular GUI, providing connected data.

• Use Case: Monitoring of KPIs, financial performance, or operation metrics in real-time.
• Big Data Adaptation: Tools such as Tableau, Power BI, and Google Data Studio connect big data frameworks which makes data visualizations shareable and interactive.

Tools for Big Data Visualization

Several tools allow the creation of these visualizations and each is designed for given purposes and types of data. Here are some popular ones:

• Tableau: Exploding it for its ease of use, and reduce visualization’s ability to handle gigantic data points. Gs is best known as a library for dynamic, responsive visualizations and for creating interactive dashboards.
• Power BI: A Microsoft tool that is a business intelligence toolkit with a complete integration of other Microsoft applications.
• D3.js: A JavaScript toolkit for the development of additional, web-based charts.
• Google Data Studio: This is an open-source software to create reports and fundamentals for developing dashboards for marketing data.
• Python Libraries (Matplotlib, Seaborn): Topology, survey, and Sampler are the development tools that have been put into use for coding-based analysis and customization of the visualization of the software.

Choosing the Right Technique

Choosing a visualization technique depends on several factors:-

Data Type: Categorical, numerical, temporal, spatial, or hierarchical.

Audience: One set of reader(s) may require a complex infrastructure of diagrams while another set of readers may require only conclusions.

Objective: Whether a researcher is analyzing a set of variables in order to discover relationships between different variables or comparing two or more categories of data, or even tracking changes that are occurring in real-time.

Here’s a quick guide:

• Use bar charts or treemaps for comparisons.

• Use line graphs or scatter plots for trends and relationships.

• Use heatmaps or geographic maps for spatial analysis.

• Use dashboards to combine multiple metrics.

Conclusion

There are two stages which are visualization and a link between the result and the data since it helps to transform raw data into useful information. With properly applied methods and the help of particular instruments, enterprises, and experts can reveal patterns, solve difficulties, and create novelties. In a world increasingly where we rely on data, no matter if you are a data scientist or a decision maker, it is imperative that you understand these visualization techniques.