Data Visualization Vs Data Analytics

Many of our learners have had decent confusions regarding data visualization and data analytics to be as a similar factor in the data world, which happens to be entirely false. Data visualization and Data analytics are completely different scenarios although many think them to be as same. Let me try simplifying it for you all.

If you study about the data of students present in the class since past two years and form a pattern in a pictorial form then that is data visualization but when you use the same data, study it and make guesses on how many students will be attending the classes till the end of the year and check the ratios then that is data analytics which can be done by studying the results obtained from visualizing the data. Talking about it in a more professional manner,

Data Visualization

Data visualization is a concept where you take raw data in the form of graphs, tables, line graphs, column charts, charts and images as an input and revive pictorial or graphical representation as an output inherent in the data. These are done by insightful 3D visualizations.

It changes the way we make sense of the information to create value by discovering new patterns and trends. It truly empowers travel managers and reporting users by providing clear and actionable insights into their programs. It helps the finance controller, HR managers to the security manager.

Data is visualized using certain tools and most of the tools allow the application to manipulate the data as per user requirements. It mostly deals with raw and unstructured data. End-to-end analytic tools employ data mining algorithms to cleanse the data which evaluates the cleansed data using different evaluation models and software tools that subjects it to the algorithms, and then decides how to display the results.

The places where data visualization is used are:

• Decision making
• Finding solution to problems
• To find relationship among the data
• Comparative analysis

Advantages:

• Identify areas that need improvements and attention
• Clarity with costumes influencing factors
• Predicting sales volume

Data Analytics

Data analytics is the study of data with respect to the future. What they do is identify or discover the trends and patterns inherent in the data. It is the science that works in analyzing the data in order to convert information to useful knowledge.

Big data is often combined with machine learning to create predictive analytics that bring the value of light.

Things needed to be good data analytics:

• Very good in mathematics
• Able to implement algorithms
• Programming language

Advantages:

• Identifying underlying models and patterns
• Is an input source used for visualization of data
• Helps improve business through prediction

Best jobs and careers in the field of data analytics and visualization:

• Data engineers
• Data analyst
• Data visualizer
• Business intelligence professional
• Data scientist
• Business stake holder
• Database visualizer
• Data administrator

What makes the difference?

Both data visualization and analytics deal with data. Visualization tools generate beautiful and easy to comprehend reports. Sadly, only the robust backend capability, which handles the messy data and processes the data by applying advanced algorithms, gives an accurate report. Data analytics offer the complete picture in a whole, while visualization summarizes the available data in the best way possible.

DATA VISUALIZATION

DATA ANALYTICS

The goal is to communicate information clearly to users by presenting them visually.

Helps the business to make more-informed business decisions by analyzing data.

Helps data analytics to get better insights.

It draws conclusion about the datasets. It might also act as a source of visualization.

Tools used:

• Plotly
• dataHero
• Tableau
• QlikView
• Dygraphs

Tools used:

• Hive
• Polybase
• Excel/spreadsheet
• Clear Analytics

Can be

• Static
• Interactive

Can be

• Perspective analytics
• Predictive analytics
• Diagnostic analytics
• Descriptive analytics

Widely used in finance, banking, healthcare, retailing etc.

Widely used in commercial, finance, healthcare, crime detection, travel agencies etc.

Data engineers performs data visualization.

Data analysts performs data analytics

Although visualization is important, it cannot be the sole component of the solution for data processing, both Data visualization and Data analytics together will draw good conclusions when seen from a business point of view.