What Are Common Visual Big Data Analysis Techniques​?

Ever seen a sky full of stars? Isn't it beautiful, awe-fully inspiring, but impossible to make sense of?

Do you feel the same when looking at your big data? It's insightful and important but complex and confusing.

If you are nodding along, then it does.

Big data can easily become a cluster of constellations with endless data points and zero clarity. But what if you can actually understand and make use of your data sets? 

Get ready to do just that as we bring you the top 10 big data analysis techniques in this blog. 

Do you know the amount of data created, consumed and generated daily? We are breaking down the numbers for you, so take a back seat because they will blow up your mind. 

With data creation predicted to reach 147 zettabytes by the end of 2024 and 180 zettabytes by 2025, daily data created is breaking down its records, surpassing human imagination. 

Data is generated endlessly every single day, and its sources are diverse, including:

• Images 
• Videos
• Social Media
• Google Searches 
• The Internet
• YouTube
• File Sharing 
• E-mails 
• Maps 
• Forecasts 
• Stocks (and many more)

Every move or action you make today is equal to data creation. When analyzing this vast data, many people do not see the forest for the trees. Why? The truth is there is too much raw information, and focusing on the details in between this vastness is very difficult.

This is when data visualization specialists use big data analysis methods, trends, techniques and tools to give meaning to your data.

Big data's sheer volume, variety and velocity require data visualization best practices to understand and make sense of this ever-multiplying data. Ready to make your data understandable and acted upon? Let’s see how these big data analysis methods can help.

When choosing a data analysis or big data visualization tool, it is important to understand that just picking any chart or graph won’t work. All big data visualization tools are different, so before you start making a decision, ask yourself a few simple questions, such as:

• What relationship between your data sets are you trying to explore? 
• Do you want to analyze a single value over time or multiple values? 
• What are you looking for? Outliners or analysis trends?

Similar questions and the key components of data visualization will guide you in finding the right big data visualization tools. To help you in your decision making, here are some of the most common big data analysis techniques.

1. Regression Analysis

Regression analysis is a statistical method for finding a relationship between a dependent variable and one or more independent variables.

Using this big data analysis method, you can determine how changes in one variable are related to changes in other variables. It's like trying to figure out how one thing affects another.

For example,

How are marketing and sales influencing your business revenue? Or what's the relationship between customer acquisition and marketing budget?

Regression analysis lets you see patterns, trends and hidden connections between different data points. You can predict outcomes and estimate what the upcoming results might be. 

How and Why to Use Regression Analysis?

With mountains of data, it can sometimes be confusing to distinguish what matters most from noise. Regression analysis helps you find the important parts of your data by making it easier to understand. Your decisions with regressing analysis will be based on evidence, not guesses.

• You can start by deciding what you wish to analyze
• Ask yourself questions
• Gather and clean your data
• Choose the type of relationship you want to showcase
• Use the right tool to perform your regression
• Analyze the output and
• Find your answers

Benefits:

• Simplifies complex data 
• Helps make predictions
• Provides quantitative backing 
• It’s flexible across industries

Limitations:

• Relies on assumptions
• Overfitting for complex models 
• Sensitive to outliners (It can distort your results)
• Multicollinearity can skew the analysis

Big Data Visualization Tools You Can Use:

• Heatmaps 
• Scatter Plots 
• Regression Line Plots
• Line Charts 
• Interactive dashboards

You can use Power BI, Excel, Tableau, and D3.js for regression analysis of your data. From analyzing business performance to predicting customer behavior, regression analysis helps you bring clarity to your data. It helps you see what’s happening and where to go next. 

2. Time Series Analysis

Another statistical big data analysis technique is time series analysis. This technique analyzes data points collected or recorded over a period of time, allowing you to understand how things change over time.

You can spot patterns, identify trends, seasonality and even forecast future outcomes, helping you track changes and predict the future.

For example,

You can track your business sales over regular intervals, identify the pattern and see if they grow every year or month.

It can also help you check if your website’s traffic has a peak on weekends or predict your next month’s traffic. 

How and Why to Use Time Series Analysis?

Time series analysis is one of the best visualization tools for preparing for what might happen next. It allows you to assess seasonal patterns, quarterly patterns and other cycles over a set period of time. 

To use a time series analysis method start with gathering your data. 

• Collect the data that changes over time
• Visualize it on a timeline
• Identify the trend, repeating patterns and the noise 
• Choose a model to make predictions
• Analyze and forecast predictions 

Benefits:

• Identify long-term trends 
• Better decision-making 
• Highlights lows and highs 
• Easy to understand time-based data

Limitations: 

• Results can be misleading with inaccurate or missing data
• Cannot handle sudden changes 
• Random fluctuations do not provide meaningful insights 
• Difficult to process highly complex data

Big Data Visualization Tools You Can Use:

• Line Plot
• Histogram
• Seasonal Decomposition Plot
• Area Chart
• Time Series Plot 
• Rolling Average Plot 

QlikView, Google Data Studio, MATLAB, Tableau, and Python are some of the tools commonly used for time series analysis. 

Time series analysis is a great tool for time-based forecasting, including weather forecasting and preparing for season demands. For best results, you only have to provide clean and consistent data. 

3. Cluster Analysis

Another visual data analysis technique in big data is cluster analysis. It is a method of organizing your data into groups with similarities. Using these clusters, you can identify similar data points in one cluster.

Thus, instead of making rough guesses on how to organize your data, cluster analysis brings out patterns naturally so you don’t have to (particularly with predefined categories). It’s similar to organizing your closet by color or type of cloth.

For example,

You can cluster data points together for customers who buy product A and a different cluster of customers who buy product B. Each cluster or bubble represents a data point. The size and color of the bubble can represent different attributes of that particular cluster. 

How and Why to Use Cluster Analysis?

The most common types of clutter analysis are dendrograms (tree-like diagrams showing hierarchical data), bubble graphs and multi-dimensional scaling plots. 

You can use these techniques when you want to group, classify or work on customer segmentation, market analysis or high-dimensional data. 

To use a cluster analysis method, start by asking yourself what outcome you want. Do you want to identify patterns in your data or segment different types of customers? 

• Start with a goal 
• Clean your data 
• Standardize it based on variables or scale 
• Choose a cluttering method
• Use tools to run the analysis 
• Plot the clutter to visualize it

Benefits: 

• Helps find hidden patterns
• Helps target strategies better
• Simplifies complexity 
• Versatile for all industries

Limitation:

• Sensitive to the quality of your data
• Different algorithms produce different clusters for the same data 
• Understanding and labeling the clusters can sometimes be complex 
• Sometimes difficult to find out how many clusters you need

Big Data Visualization Tools You Can Use:

• Bubble Chart
• Dendrograms
• 3D Scatter Plots
• Elbow Curve
• Spider Plot 
• Heatmaps
• Box Plot

Cluster analysis will help you organize your chaotic data and make intuitive decisions. You can use tools like Tableau, Power BI, QlikView and Google Data Studio.

The ocean of data available is not stopping any time soon. Does that mean you cannot grasp the individual drops from it? No. Visual data analysis techniques in big data empower you to comprehend the finer details of your data within the bigger picture.

It may not be as simple as dressing up your graph to make it look better, but it is a powerful tool for big data.

Don’t want to let your data go to waste? Consult our team of data visualization specialists. We will transform your data the same way we’ve transformed countless others.

1. What is visualization data analysis?

Data visualization is a technique for converting raw information or data into a visual format, such as a map or graph. It is done to make data easier for us to understand, interpret and make data-driven decisions. 

2. What are the types of big data visualization categories?

Various forms of data visualization exist, including but not limited to bar charts, line charts, scatter plots, pie charts, and heat maps. These represent commonly used methods for presenting and interpreting data.

3. What are the benefits of analyzing big data?

The benefits of analyzing bid data are many, including:

• Improved decision-making
• Increased agility and innovation
• Better customer experiences
• Continuous intelligence
• More efficient operations
• Improved risk management

4. What is big data analysis?

Big data analytics refers to the methods, tools, and applications used to collect, process, and derive insights from varied, high-volume, high-velocity data sets. These data sets may come from various sources, such as web, mobile, email, social media, and networked smart devices.

5. Why is big data used?

Big data allows you to collect and process real-time data points and analyze them to adapt quickly and gain a competitive advantage. These insights can guide and accelerate the planning, production, and launch of new products, features, and updates.