Difference Between Data visualisation and Data analytics
Data is very powerful. It’s not easy to get clear takeaways by looking at loads of numbers and stats. Data needs to be classified and processed, for easy understanding. The human brain process visual content better than it processes plain text.
Data visualization is nothing but, representing data in a visual form. This visual form can be a chart, graphs, lists or a map etc. This representation helps people to understand the magnitude of the data.
Data analytics is the method of examining data sets (structured or unstructured) in order to get useful insights to draw conclusions about the datasets. Data analytics techniques and technologies are widely used in many organizations.
Head to Head Comparisons Between Data visualisation vs Data analytics (Infographics)
Below is the Top 7 Difference between Data visualisation vs Data analytics
Key Differences Between Data visualisation vs Data analytics
Below are the lists of points, describe the key differences between Data Visualization vs Data Analytics:
- Data visualization is the presentation of data in a pictorial or graphical format. Data analytics is also a process that makes it easier to recognize patterns in and derive meaning from, complex data sets.
- Data visualization enables decision makers to see analytics presented visually, so they grasp difficult concepts or identify new patterns.
- Looking at a visualization of an attribute in-depth will lead to the analytics of that attribute.
- The analytics process, including the deployment and use of big data analytics tools, can help companies improve operational efficiency, drive revenue and gain competitive advantages over business rivals.
- Descriptive analytics focuses on describing something that has already happened, as well as suggesting its root causes.
- Prescriptive analytics help companies anticipate business opportunities and make decisions that affect profits in areas such as targeted marketing campaigns etc.
- Predictive analytics help mining historical data sets for patterns indicative of future situations and behaviors
- In visualizations, we have static and interactive visualizations.
- Static visualizations focus on a specific data store, User’s can’t go beyond a single view to explore additional stories beyond what’s in front of them. The story is specifically captured in an engaging single page layout.
- Interactive visualizations help users to select specific data points to build a visualized story of their choosing.
- Data Analytic insight takes discovery to the next level by allowing practitioners to not only explore their data but to understand the underlying factors and impacts beyond simply asking WHY.
- Using charts, graphs, and design elements, data visualization can help business explain trends and stats much more easily. Data visualization also exposes patterns, trends, and correlations that may otherwise go undetected.
- Data analysts translate numbers into plain text (English), whether its sales figures, market research, logistics, or transportation costs.
- Computers made it possible to process large amounts of data at lightning-fast speeds. Today, data visualization has become a rapidly evolving blend of science and art that is certain to change the corporate landscape over the next few years.
- Data analytics is a trending practice that many companies are adopting. Before jumping in and buying data analytics tools, organizations should first get to know the landscape.
- Let’s take an example to understand, Data Visualization very clearly.
For example, let’s take Thanksgiving Day as a use case in our scenario, as we all know that, the sales, on Thanksgiving day, will be very high and purchasing will be at its peaks.
To help the business owner to understand the purchase history respective to the items, a pie chart or a graph will help him/her understand better than looking at the numbers in the purchase history. So that business owner can plan his business according to the trend.
- Let’s take an example of the Data Analytics to understand the power of analytics.
We all do On-line shopping and we must have seen this message in our mailbox -“We Missed You” message from our favorite e-commerce website if we don’t shop for a while. The scene behind this message includes ‘detailed examination’ of our orders and orders history. The analytics tools giving the intelligence to the business to attract the customers to increase the revenue.
Data visualisation vs Data analytics Comparision Table
|Data Visualization||Data Analytics|
|The goal of the data visualization is to communicate information clearly and efficiently to users by presenting them visually.||Every business collects data; data analytics will help the business to make more-informed business decisions by analyzing the data.|
|Relation||Data visualization helps, data analytics to get better insights||Together Data visualization and analytics will draw the conclusions about the datasets. In few scenarios, it might act as a source for visualization.|
Popular Course in this category
Data Scientist Course 43 Online Courses | 170+ Hours | Verifiable Certificates | Lifetime Validity
4.8 (676 ratings)
Related CoursesTableau TrainingAzure Training Course
Tools, Techniques, and Methods
Data visualization can be static or interactive.
Interactive data visualization is a little bit newer, It lets people drill down into the very minute details of the charts and graphs using the computers and mobile devices, and then interactively change which data they see and how it was processed.
Data Analytics can be Prescriptive analytics, Predictive analytics, Diagnostic analytics and Descriptive analytics
Hive, Polybase, Presto
SAP Business Intelligence, etc.
|Industries||Data Visualization technologies and techniques are widely used in Finance, Banking, Healthcare, Retailing etc||Data Analytics technologies and techniques are widely used in Commercial, Finance, Healthcare, Crime detection, Travel agencies etc|
|Who performs||Data Engineers||Data Analysts|
|Big data processing, Service management dashboards, Analysis, and design.||Big data processing, Data mining,
Analysis and design.
|Identify areas that need attention or improvement
Clarity which factors influence customer behavior
Helps understand which products to places where
Predict sales volumes
|Identify the underlying models and patterns
Acts as an input source for the Data Visualization,
Helps in improving the business by predicting the needs
Conclusion – Data visualisation vs Data analytics
When it comes to enterprise needs, the difference between Data visualisation and Data analytics, are strikingly clear. It’s also clear that visualizations, though important, cannot be the sole component of the solution for data processing, both Data visualisation and Data analytics together will draw good conclusions for the business.
Choosing the visualization tools and analytics tools varies from organization to organization, according to the type of data it handles and how big the organization is.