What is Data Analytics ?

Data analytics is referred to as the process of analyzing various data sets in order to make the decision about the information they possess, increasingly with specialized software and systems.

• Data analytics technologies are majorly used in the commercial industries that allow organizations to make business decisions.
• Businesses can better understand their customers, improve their advertising campaigns, personalize their content, and improve their bottom lines based on the Data.
• The different processes and techniques of data analytics have been automated into mechanical processes and algorithms which operate over raw data for human consumption.
• A business can optimize its overall performance with the help of Data Analytics.

What is Data Visualization ?

Data visualization may be defined as the graphical representation of information and data in a pictorial or graphical format in the form of charts, graphs or maps.

• The different Data visualization tools provide an accessible way to see and understand trends, patterns in data and outliers.
• Analyzing massive amounts of information and making data-driven decisions require Data visualization tools and technologies.
• The idea of using pictures to understand data has been used for a long time. Some common types of data visualizations are Charts, Tables, Graphs, Maps, Dashboards.

Comparison of the Concepts

1. Purpose

The primary motive of the data visualization is to communicate information clearly and efficiently to users by the visual presentation. On the other hand, data analytics helps businesses to make more-informed business decisions by analyzing the various categories of data.

2. Relation

Data visualization allows data analytics to get better insights. Working together Data visualization and analytics can draw the conclusions about the datasets. In a few scenarios, it might act as a source for visualization.

3. Tools and Techniques

Data visualization can be either static or interactive. Interactive data visualization is a little bit newer concept which 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. Some of the commonly used tools are Plotly, DataHero, Tableau, Dygraphs, QlikView, ZingCHart etc.

But, on the other hand, Data Analytics can be Prescriptive analytics, Predictive analytics, Diagnostic analytics and Descriptive analytics. Some of the commonly used tools include Trifecta, Excel /Spreadsheet, Hive, Polybase, Presto, Trifecta, Excel /Spreadsheet, Clear Analytics, SAP Business Intelligence, etc.

4. Industries

Data Visualization technologies and techniques are widely used in Finance, Banking, Healthcare, Retailing etc. Similarly, Data Analytics technologies and techniques are widely used in Commercial, Finance, Healthcare, Crime detection, Travel agencies etc.

5. Platforms

Data Visualization has platforms like Big data processing, Service management dashboards, Analysis, and design. On the contrary, Data Analytics uses Big data processing, Data mining, Analysis and design.

6. Benefits

Data Visualization has the following benefits :

• It helps to identify areas that need attention or improvement.
• It can provide clarity about factors that influence customer behavior.
• It also helps to predict sales volumes.

The benefits of Data Analytics are :

• Data Analytics can identify the underlying models and patterns.
• It acts as an input source for the Data Visualization.
• It also helps in improving the business by predicting the needs.

Data Analytics or Data Visualization ?

Data analytics has proven its value for a long time by allowing businesses to examine structured and unstructured datasets and extract useful information, so that the key stakeholders can make more-informed and more effective decisions. Analytics can be prescriptive, predictive, diagnostic, and/or descriptive to produce insights, observe trends, compare metrics, and more.

But analytics cannot do what data visualization can do. Data Visualization helps to communicate and explain that picture with precision and brevity while in a format that the brain consumes exceedingly quickly. The data itself is not changed by data i.e., further analysis is not done. But it is difficult to learn two-dimensional tables of data as the mind tends to gloss over a large amount of it, scan for highest and lowest values, and miss the details in between. On the contrary, Data visualization does not have that problem as the visuals are often compelling as they literally draw the picture of the metrics in question.

Working Hand in Hand

Both data analysis and data visualization are both very important BI tools to mine the power within an organization’s vast collection of data. Working together, they can deliver the most impactful and actionable insights for key stakeholders to run with. But data visualization is only as good as the analytics that support it. But the insights it depicts depend on the integrity of the analytics provided. Poor data models, or unclean or incomplete data even if well presented visually, do not serve anyone. On the contrary, clean, sophisticated, and thorough data analysis can provide the raw materials to build influential and valuable data visualization tools like dashboards to give decision-makers the insights they need to drive their business.

Any of the concepts alone cannot serve as the sole component of a powerful, dynamic solution for data processing in today’s competitive marketplace. Since efficiency is the key, they should work hand in hand to leverage the power of the data.

Conclusion

• From this article, we learnt the concepts of Data Visualization and Data Analytics along with their salient features.
• We also compared both the concepts and understood their importance in the different business domains.
• We even found the answer to the question : Data Analytics or Data Visualization ?
• Since data analysis and data visualization are both very important BI tools to optimize the overall performance of any business, they should work hand in hand to serve as the sole component of a powerful, dynamic solution for data processing in today's marketplace.

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