Data Visualization and Data Analytics – Know the Difference
Understanding the analytical insights takes time. Not every person has the required aptitude to make sense of an extensive database/worksheet. Therefore, data visualization helps you and your team understand the output of data analytics. This article will show you how data analytics differs from data visualization.
Data Visualization: Graphs, Colors, and Heatmaps
The creation of visually informative design elements is the general description of data visualization services. Also, the technical terminologies of this process vary across organizations.
The use of data visualization integrates various visual cues with your reports. Forms of visualized data can have a more traditional appearance. i.e., simple graphs.
Furthermore, modern forms of visualization are not static.
Real-time monitoring tools enhance your data visualization assets with animation. Periodically updated data affects the velocity and color saturation in the animated visuals, but the animated data visualization requires more computing power.
Also Read: Data Science vs. Data Management
Data Analytics: Descriptive, Diagnostic, or Predictive Insights
Enterprise data analytics solutions discover the patterns in a dataset to address business problems. Note that the usage of data analytics has increased across all industries.
The patterns or trends guide corporations in critical business decisions. Also, you can understand your strengths and weaknesses by suitable analytical methods.
Mathematics plays a crucial role in data analytics solutions. Note that statistical analysis is the core element of this process. Additionally, a data analyst must be well-versed in programming languages such as python.
Difference Between Data Visualization and Data Analytics Services
1 | Purpose
Data visualization attempts to improve the comprehension of data using design elements. The diagrams, graphs, charts, and animations facilitate a faster learning curve.
The goal of a data analyst is to generate reliable advice by contemplating the trend analysis. Also, data analytics applies statistics to verify or cross-examine the effectiveness of business decisions. For example, insights can reveal new risk factors causing decreased lead conversions.
Sometimes, data analysts leverage simple visuals for visual analytics. But do not worry about the data visualization vs. visual analytics debate.
Analysts do not pursue advanced data visualizations unless the company asks them to do so.
2 | Difference Between Data Processing Models
The collection of complete data is necessary for data visualization. Otherwise, use machine learning (ML) models to guess the values in the information gaps. Some data visualization services feature ML options in their offerings. Finally, you generate mathematically accurate designs.
Statistical modeling is more important in data analytics solutions. Everyone might know about the mean, median, and standard deviation, but data analysts need advanced methodologies to account for the errors in sampled data.
The end outcome is an insight. E.g., sales decline despite the increase in contact form interactions.
3 | Skills Vary in Both Workflows
3.1 | Data Visualization Skillset Combines Math with Design
Data visualization demands a designer’s mindset. You require a fundamental understanding of user attention flow and how humans learn visually. Otherwise, you cannot utilize its full potential.
Also, you must have some knowledge of design philosophies and accessibility guidelines. E.g., minimalism art and ideal contrast ratios for users with partial vision impairment.
Your team needs the mathematical and geometric skills to detect if visualized data features outliers.
An outlier is a value that deviates a lot from the statistical mean. Therefore, your entire analysis becomes misguided. Too high or abnormally low values (outliers) originate from a mistake or machine error.
3.2 | Data Analytics Solutions Use Python for Advanced Statistics
The multi-variate analysis is a holistic and complex application of statistics. As you can guess, data analytics relies on this skill to extract meaningful insights.
Data analysts must learn how linear algebra and calculus help resolve business challenges.
Machine learning, R, SQL, MATLAB, NoSQL, and python are essential computer skills you need in data analytics. Also, you must know how to share data across different software tools.
4 | Applications of Data Visualization vs. Visual Analytics Usage
Data visualization assists business communication while data analytics accelerates corporate decision-making. Each method has its appropriate place in corporate problem-solving.
Visual data is beneficial in training and education. This approach includes consumer education regarding product usage and refund policies. Also, patients in a healthcare institution understand their reports better using visualization.
You can use the elements of data visualization to teach a large audience. Their technical proficiency will not stop them from understanding pie charts or histograms.
The business employs data analytics solutions to find improvement opportunities and risk factors. E.g., a decrease in sales correlating positively (increasing) with growing negative press and low public ratings.
Conclusion
Data visualization services are as vital as data analytics operations. One improves your internal communication and customer experience. The latter gives you statistically derived insights and business ideas.
Both data visualization and data analytics are different from each other. You will not be confused about these two techniques ever again.
We hope you liked this article about the difference between data visualization and data analytics. Now, you know what to expect from a data visualization expert or analyst.
A leader in data analytics solutions, SG Analytics empowers organizations to maximize their operational efficiency across multiple industries. Contact us to integrate data-driven actionable insights and unlock impressive business growth.
