In the complex data community, the phrases data analytics and data visualization appear to have become interchangeable. Many job postings emphasize data visualization abilities while omitting to include the need for analytical skills. Hence, people get confused between these two. Therefore, for helping you we are providing major differences between the two. Let’s explore.
Overview of Data Visualization
For thousands of years, data visualizations have existed in various forms, ranging from hand-drawn graphs to computer-created pie charts, graphs, and cartograms. but, the current focus on them stems from their ties to big data and business intelligence.
The two most critical problems for corporate businesses are business intelligence and big data. Both of these use visual material. Content marketing and social media management also use visual representations. On the other hand, newer visualizations are more interactive.
We can see these kinds of visualizations all over the internet. Using various visualization approaches, you may show practically any type of data. Also, some of them let you modify variables like whether a team wins its next game to see how it affects the projected outcome.
Organizations rely on static and interactive visualizations developed by teams of data scientists and designers for internal usage. Visualization is a chart in a quarterly report or a dashboard that employees may use to track key performance indicators (KPIs) across the firm.
A dashboard with the appropriate KPIs enables easy tracking of a certain effort or project’s progress. The finest dashboards present current data in an easy-to-navigate interface/format. It also avoids the information overload that can result from poor design.
Overview of Data Analytics
Data analytics is a subject that is more abstract than data visualizations. Typically, the phrase refers to a set of tools for evaluating digital data, the insights it generates, or both. The following are examples of common data analytics tools:
A BI software can take data from a variety of sources and perform a variety of tasks, such as preparing it for storage in a data warehouse or mining it for critical patterns. These are intended to help firms identify effective solutions in a world where business difficulties are always evolving. As a result, users using BI software may see real-time data on a single and easy-to-use dashboard.
OLAP (Online Analytical Processing) utilities:
OLAP is a set of database technologies that are optimized for fast querying and reporting. Users can use OLAP to search for specific insights which are like “cubes.” These cubes reside between the user and the data warehouse on the backend, and they contain dimensions for diverse data types such as time, geography, and product line.
It includes techniques such as machine learning, which involves teaching computers to execute complex tasks without human intervention, and different artificial intelligence applications. Advanced analytics is a useful resource for businesses. It is because it allows them to extract more value from their data assets, regardless of where they are kept or what format they are in.
Analytics, regardless of the methods employed, assist organizations in answering questions and improving their performance. For example, a data scientist might use an OLAP tool to locate a sales report for a specific year and compare the information in it to that of another year to better understand long-term trends.
Data Analytics vs Data Visualization
Data Analytics is an exploratory procedure that frequently begins with a set of questions. It necessitates a high level of perseverance, as well as curiosity and a desire to find solutions. Analytics is the process of evaluating data sets to make decisions based on the information available, which is increasingly done with the help of specialized software and systems.
In the commercial world, data analytics tools are utilized to help businesses make better decisions. These can also help organizations better understand their customers, improve advertising campaigns, personalize content, and increase profits.
Data analytics approaches and processes are majorly those mechanical processes and algorithms that operate on raw data. Therefore, a company’s performance can be improved through data analytics by regularly studying data.
Data visualization, on the other hand, refers to the visual display of data, which can range from simple charts to complex dashboards. Hence, effective visualizations cut the time it takes your audience to process information. Also, these will help you gain useful insights half of the time.
Data visualization tools provide an accessible way to see and understand trends, and patterns in data. Thus, data visualization tools and technologies are essential to analyze information and make data-driven decisions. General types of data visualizations are Tables, Graphs, Maps & Dashboards.
Tabular Difference between Data Analytics & Visualization
|Basis of Difference||Data Analytics||Data Visualization|
|Definition||Data Analytics is the process of analyzing data sets to make decisions about the information one has.||Data visualization is the graphical representation of information and data in a pictorial or graphical format.|
|Benefit||Identify areas that need attention or improvement.||Helps in improving the business by predicting the needs to represent data.|
|Industries||Data Analytics technologies and techniques are widely used in Finance, Healthcare, Crime detection, Travel agencies, etc.||Data Visualization technologies and techniques are widely used in Finance, Banking, Healthcare, Retailing, etc.|
|Tools||Trifecta, Excel /Spreadsheet, Hive, Polybase, Presto, Clear Analytics, etc.||Plotly, DataHero, Tableau, Dygraphs, QlikView, ZingCHart, etc.|
|Performed by||Data Analysts||Data Engineers|
When it comes to enterprise needs, the difference between Data Visualization and Data Analytics is clear. It’s also clear that visualizations, though important, cannot be the sole component of the solution for data analysis, both Data visualization and Data analytics together will draw good conclusions for the business.
Hence, we can conclude that both are essential for growing businesses. Also, businesses must have knowledge about the two in order to gain success. But, we should also take care of the differences between the two.