Challenges of Big Data Visualization and Their Solutions

Data visualization is a quick and simple technique to depict complicated ideas graphically for improved comprehension and intuition. It must find diverse relationships and patterns concealed by massive data. We can use traditional graphical representations to organize and represent data. Still, it can be challenging to display Huge amounts of data that is very diverse, uncertain, and semi-structured or unstructured in real-time. Massive parallelization is required to handle and visualize such dynamic data. In this article, we will cover the major challenges faced by big data visualization and its solutions.

The World Economic Forum estimated a $100 billion increase in global business and social value by 2030 due to digital transformation. PwC has estimated that thanks to artificial intelligence (AI), global GDP will increase by $15.7 billion by 2030. We are witnessing the renaissance of Big Data, and these advances offer companies both opportunities and challenges when it comes to making the right business decisions. Let us discuss the challenges of big data visualization. 

Content

• Data Quality
  • Accuracy
  • Completeness
  • Consistency
  • Format
  • Integrity
  • Timeliness
• Not Choosing the Right Data Visualization Tools
• Confusing Color Palate
• Analytical & Technical Challenges

Data Quality 

The data quality plays a crucial role in determining its effectiveness. Not all data is created in the same way, and each has a different origin, hence its heterogeneity.

No matter how powerful and comprehensive the big data tools at the organization’s disposal are, insufficient or incomplete data can often lead data scientists to conclusions that may not be entirely correct and, therefore, could negatively impact business.

The effectiveness of big data in analysis depends on the accuracy, consistency, relevance, completeness, and updating of the data used. With these factors, the data analysis ceases to be reliable.

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Accuracy

The most important thing is: How accurate is the data? How much can you trust it? Is there certainty about the collection of relevant data? The values ​​in each field of the database must be correct and accurately represent “real world” values.

Example: A registered address must be a real address. Names must be spelled correctly.

Completeness

The data must contain all the necessary information and be easily understandable by the user.

Example: If the first and last name are required in a form, but the middle name is optional, the form can be considered complete even if the middle name is not entered.

Consistency

The data must be the same throughout the organization and in all systems.

Example: The data of a sale registered in the CRM of a company must match the data registered in the accounting dashboard that you manage.

Format

The data must meet certain standards of type, format, size, etc.

Example: All dates must follow the format DD/MM/YY, or whichever format you prefer. Names should only have letters, no numbers or symbols.

Integrity

The data must be valid, which means that there are registered relationships that connect all the data. Keep in mind that unlinked records could introduce duplicate entries to your system.

Example: If you have an address registered in your database, but it is not linked to any individual, business, or other entity, it is invalid.

Timeliness

Data must be available when the user expects and needs it.

Example: A hospital must track the time of patient care events in the ICU or emergency room to assist doctors and nurses.

Not Choosing the Right Data Visualization Tools

The selection of big data tools often focuses on the technical plane, leaving aside everything that is not directly related to the analysis. Acting this way means ending up implementing solutions whose visualization potential is narrower.

When this happens, the consequences do not take long to appear:

• Causing difficulties in understanding the data.
• Subtracting agility from the process of extracting and sharing knowledge within the organization.
• Increasing latencies in taking action.
• Being able even to divert decision-making, which would lose effectiveness.

Many data visualization tools, such as Tableau, Microsoft Power BI, Looker, Sisense, Qlik, etc., offer data visualization integration capabilities. If your organization already uses one of these tools, start there. If not, try one. Once you select a tool, you’ll need to do a series of prototypes to validate capabilities, ease of use, and operational considerations.

Here is a detailed list of considerations:

• Do the chart types meet the business needs
• How easy is it to integrate? 
• What are the flexibilities in device design and compatibility? 
• Is the security configurable for the required end user/consumer rights? 
• Is it performing fast enough to integrate into an application? 
• Are the platform’s costs and pricing models aligned?

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Confusing Color Palate

Using colour correctly enhances and makes a chart more understandable while allowing users to identify the most relevant information easily. For example, to highlight positive or negative aspects or show changes and evolution over time in a dashboard.

Despite the importance of using the correct colours, you must remember that the analysis is fundamental. Many companies fall into the trap of using corporate colours in visualizations, but there are better options than this. You must consider what you want to convey with the messages and make an appropriate choice.  

Analytical & Technical Challenges of Big Data Visualization

Some other problems faced in big data visualization –

Visual noise

Most of the objects in a dataset are too relative to each other. Users cannot divide them as separate objects on the screen.

Solution – It is necessary to ensure the data is clean through data governance or information management. Also, removing the outliers from the data or creating a separate chart for the outliers can help remove the visual noise.

Information loss

Big data reduction methods with visible data sets can be used, but this may lead to information loss.

Solution – To minimize information loss, choose the right type of visualization for the data, effectively encode the data, and provide clear and concise annotations.

Large image perception

The human visual system has limitations in processing large images. Data visualization methods are limited by aspect ratio, device resolution, and physical perception limits.

Solution – Clustering data into a higher-level view is a potential solution. This way, users can easily visualize smaller groups of data. In addition, techniques such as detail-in-context views, overviews with fisheye views, and progressive refinement can help users understand large images in big data visualization.

High rate of image change

Users observe data and cannot react to the number of data change or their intensity on display.

Solution – Developing proper domain expertise will help you understand your data better.

High-performance requirements

The massive size of big data sets requires high computing power and fast data processing capabilities to visualize the data in real time.   

Solution – This can be handled by increasing memory, high-performance computing systems, and powerful parallel processing. You can also adopt a grid computing approach, where many machines can be used to put data in memory. 

Conclusion

It should be noted that many people talk about other Vs., such as volatility, value, viability, and visualization. Some speak of 3V, others of 5V, even 6V. The truth is that each company must classify its importance according to its strategy, purpose, and objectives. It is valid that not all organizations opt for the same methodology. We hope this article helped you to understand the challenges of big data visualization and ways to overcome them.