What’s the Difference Between Data Analytics vs Data Analysis?

Data analytics and analysis are two different terms, but have one thing in common: Data. Without data, there is no use for these terms.

Data Analytics Vs. Data Analysis

The word analysis means a detailed examination of something. At the same time, analytics is the systematic computational analysis of data. Data Analysis is a subset of Data Analytics as Data Analytics is a broad area that involves handling heaps of data with tools to bring forward helpful decisions for a better output.

Data Analytics is all about exploring past data to produce results that help in making useful decisions for the future. On the other hand, data analysis helps in understanding the data understand what happened in the past.

Why does Data Get So Much Attention?

Both Data Analytics and Data Analysis involve data. Data is the collection of information, and nowadays, data is regarded as the richest wealth. It is because when you have data, you can rule the world. Biggest tech giants like Google, Microsoft, Amazon, etc., analyze data for various purposes- the main reason being to improve customer experience by knowing their preferences.

Now let’s get a deeper understanding of Data Analytics and Data Analysis

What is Data Analytics?

Data analytics define different practices and concepts of activities related to data. Data analytics is studied by data experts- data scientists, engineers, and analysts to make it easy for the business to interpret the data and understand the findings. What you do with data provides value to it. Data analytics involves all steps one takes manual and machine-enabled to visualize, discover and understand patterns in data to make business strategy and outcomes.

Data Analytics helps to:

• Find trends

• Predict actions

• Uncover opportunities

• Make sound decisions

Data analytics involves many systematic and computational steps. Data science, machine learning, and applied statistics involve studying data. Data analytics practice involves getting well-planned reports so that the rest of the business who are not data experts can understand and develop strategies with the help of systematic data presented to them.

Processes in Data Analytics

• Collecting and ingesting data

• Categorizing the data

• Managing the data

• Storing the data

• Performing ETL (Extract, Transform, Load)

• Analyzing the data

• Sharing the data

Tools Used in Data Analytics

The most common tools employed in Data Analytics are:

• Python

• R

• Google Analytics

• SAS

• SPARK

• Excel, etc.

What is Data Analysis?

Data analysis can be termed as one slice of the data analytics pie. Cleaning, modeling, questioning, and transforming data to find useful information comes under data analysis. Usually, an already prepared dataset is analyzed in the process. A machine performs the first round of analysis, mostly in one of your databases or tools. Humans augment, investigate, and interrogates the data to get the best outcomes.

After analyzing data, other data analysis activities involve:

• Provide others access to data

• With data visualization, present the data

• Provide suggestions on the actions that can be taken based on the data

Types of Data Analysis

There are many types of data analysis techniques. Some of the best-known data analysis techniques are text analysis, statistical analysis, predictive analysis, and prescriptive analysis.

The most common tools employed in Data Analysis are:

• Excel

• Tableau

• SPARK

• Google Fusion tables

• Node XL, etc.

Wrapping it Up

Many people confuse both terms- Data Analytics and Data Analysis. Data analytics is a broader field, and data analysis is one key function. The need for business analytics provides both data analytics and data analysis services to help businesses have valuable data presented to them in a systematic manner.