Data Analytics and Data Analysis: Understanding the Differences

Data Analytics and Data Analysis: Understanding the Differences

It’s common for non-technical individuals to get confused between data analysis and data analytics. However, there are significant differences between the two terms, including the outcomes, tools used, and applications. Despite their similar-sounding names, data analysis and data analytics are distinct.


What Is Data Analytics and Data Analysis?

Let’s begin by understanding the meanings of data analytics and data analysis before exploring their differences in the context of big data. 

Data analytics is a wide-ranging field that leverages data and tools to inform business decisions, while data analysis is a narrower subset of data analytics that entails particular processes.

The process of data analysis involves examining past events to establish what occurred, whereas data analytics is concerned with creating models and forecasting future outcomes. 

In simpler terms, data analysis involves reorganizing existing data, while data analytics employs the analyzed data to make predictions about future events.

5 Differences Between Data Analysis and Data Analytics

Once we have comprehended the meanings of data analysis and data analytics, we can proceed to explore certain distinctions between them.


There is a distinction between data analytics and data analysis in terms of their form or shape. Data analytics is a widely used form of business intelligence for making business decisions, while data analysis is a particular form of business intelligence that concentrates on mathematical and statistical data analysis.


Utilizing data analytics is a common practice for companies to make decisions based on data, whereas data analysis is employed to assess data and obtain valuable business insights.


The process of analyzing data comprises three primary steps, namely, data cleansing, data representation through visualization, and deriving insights to create data narratives. 

On the other hand, data analytics encompasses several processes such as collecting data, filtering data, standardizing it, interpreting outcomes, and offering strategic recommendations.

The Tools Used

Individuals engaged in data analytics utilize several tools which include:

  • R
  • Apache Spark
  • Google Sheets
  • Tableau Public
  • Excel
  • SAS
  • Python
  • ChartExpo

Data analysts may use the following tools:Data analysts may use the following tools:

  • NodeXL
  • Tableau Public
  • OpenRefine
  • RapidMiner
  • Google Fusion Table


In terms of application, data analytics is used for comprehensive predictive analytics. Data analysis, on the other hand, is used mainly for statistical analysis.

Examples of Data Analytics and Data Analysis

For a clearer distinction between data analytics and data analysis, examine how they are utilized in the following instances.

Recently, many millennials and Gen Z individuals have begun investing in the stock market as a form of investment. Imagine a beginner investor who wants to make substantial profits from their investments over the next 5 to 7 years.

As beginner investors, the first thing they do is research and gather information about the stock market, business trends, and what is currently happening in the market.

The activities and techniques involved in this process are examples of data analysis.

After understanding the stock patterns, the beginner investor can predict future stock market prices and make investments accordingly.

This action is an example of the data analytics process.

What are the Reasons People Still Confuse Data Analysis with Data Analytics?

It is noteworthy that even professionals in the field of data science and data analysis often face difficulty distinguishing between data analysis and data analytics.

Although there is a general consensus, the extent and area of data analytics are more comprehensive than data analysis, since data analysis is merely one of the functions performed by data analytics.

It’s possible for confusion to arise when we aren’t clear on the definitions of certain terms. However, in this case, the question isn’t about data analytics versus data analysis, but rather about how effectively we are performing both tasks.

Analyzing Data and Predicting Sales with LOKASI Intelligence

Businesses can use data analytics to predict future trends and prepare strategies by analyzing sales patterns from previous months with the help of data analysts.

Businesses can use LOKASI Intelligence to analyze their data and perform data analytics.

LOKASI Intelligence is a geospatial analytics and location intelligence platform with various data to support user analysis.

This includes telco data, points of interest, socioeconomic status, vehicle data, and more. By combining data from LOKASI Intelligence with user data, businesses can gain valuable and rich insights to predict future sales.

To learn more about how LOKASI Intelligence can help businesses, contact via email: or WhatsApp: 087779077750.


What is the difference between analysis and analytics?

Data analysis is responsible for restructuring existing information or data, while data analytics uses the analyzed information to predict what might happen in the future.

Can you explain what analytics is and give some examples?

Data analytics involves using data and tools to make informed business decisions. For instance, a data scientist may analyze sales history to predict future sales and develop strategies accordingly.

What is the purpose of data analytics?

Companies typically use data analytics to make decisions based on data.

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