Data visualisation vs Data analytics – 7 Best Things You Need To Know

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:

1. 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.
2. Data visualization enables decision makers to see analytics presented visually, so they grasp difficult concepts or identify new patterns.
3. Looking at a visualization of an attribute in-depth will lead to the analytics of that attribute.
4. 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.
5. Descriptive analytics focuses on describing something that has already happened, as well as suggesting its root causes.
6. Prescriptive analytics help companies anticipate business opportunities and make decisions that affect profits in areas such as targeted marketing campaigns etc.
7. Predictive analytics help mining historical data sets for patterns indicative of future situations and behaviors
8. In visualizations, we have static and interactive visualizations.
9. 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.
10. Interactive visualizations help users to select specific data points to build a visualized story of their choosing.
11. 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.
12. 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.
13. Data analysts translate numbers into plain text (English), whether its sales figures, market research, logistics, or transportation costs.
14. 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.
15. 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.
16. 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.
17. 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

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.

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