Difference between Big Data and Data Analytics

Big Data includes a large volume of structured and unstructured data which is very complex. Traditional data management tools cannot be used to manage such a large amount of data. This is the reason that Big Data tools were developed to manage it. Data analytics is a process of extracting useful information from the raw data which helps businesses to make decisions. There are many differences between Big Data and Data Analytics and we will look into them in detail.

Big Data

Big data consists of a large volume of data which can be structured, unstructured, or semi-structured. There are many big data management tools that are used to manage the data. These tools are used to store the data and process it. Some of the characteristics of big data include velocity, variety, and volume. The sources from which the data is extracted are stock exchanges, jet engines, social media, etc.

Features of Big Data

Big data comes with a lot of features which we will discuss here.

• Volume − Big data has the ability to store large volumes of data and then processing methods are used to process the data. The amount of data is used to find out whether this is big data or not.
• Variety − Large data sets consist of different types of data which include tabular databases, images, video data, audio data, and many more.
• Velocity − Velocity in big data refers to the speed at which the data is generated. The generation of data is continuous and it is added to the datasets.
• Veracity − The generated data can be complex and may have many inconsistencies. So veracity is needed for the processing and management of the data.

Types of Big Data

Big data is of many types and we will discuss each of them here.

• Structured Data − Structured data is in the form of a specific structure and can be easily processed. This is so because users can go through the data and understand it easily.
• Semi-Structured Data − This is a kind of data which does not follow a specific structure but is still in the form of a structure. Some of these structures can be hierarchy, grouping, etc.
• Unstructured Data − This is a kind of data which does not follow any structure. Such data includes pictures, text, video, audio, and many more.

Uses of Big Data

The uses of big data are as follows −

• Big Data for Financial Services
• Big Data in Communications
• Media and Entertainment
• Big Data for Retail
• Banking and Securities

Data Analytics

Data analytics is a process in which raw data is used and useful information is extracted to make conclusions. These conclusions help businesses to make plans and strategies for the future. Businesses use data analytics to make plans, understand the customers, and develop products on the basis of the customers' needs.

Types of Data Analytics

Data Analytics is of four types and we will discuss each of them here.

Descriptive

Descriptive analytics is a process in which it is determined what has happened to a dataset. This helps users to know what has happened to a dataset in the past.

Diagnostic

This is a type of data analytics in which descriptive analytics is considered and then the reason behind any happening in the dataset is determined. This helps users to know about the causes which led to anything that has happened to a dataset.

Predictive

In this type of data analytics, predictions for the future are determined. This includes data obtained through descriptive and diagnostic analytics.

Prescriptive

The data in this analytics is taken from the predictive analytics. Prescriptive analytics is important as it lets the users know about future events and they can also make strategies to handle the predictions.

Industries where Data Analytics is used

The uses of data analytics are as follows −

• Healthcare
• Gaming
• Traveling
• Energy management
• Risk detection and management

Difference between Big Data and Data Analytics

Big Data and data analytics have many differences which can be found in the table below −

Conclusion

Big Data and Data Analytics have a lot of differences. Big data consists of a lot of data in different formats which are processed and managed by different tools. Data analytics extracts useful information from the raw data with the help of different tools. This data can be used for making plans and future strategies.

FAQs on Big Data Vs. Data Analytics

FAQ 1. In big data and data analytics, which of them deals with the raw data?

Data analytics deals with the raw data.

FAQ 2. What is unstructured data?

Data that cannot be found within any structure is called unstructured data. Such data can be in the form of text, images, audio, video, etc.

FAQ 3. What are the tools used for data operations in Big data?

The tools used for data operations in Big Data are Hadoop, Cloudera, Cassandra, MongoDB, and many more.

FAQ 4. Data Analytics is suitable for which industries?

The industries for which data analytics is suitable are gaming, risk management, Healthcare, etc.

FAQ 5. How many types of data analytics are there?

Data analytics is of four types which include descriptive, diagnostic, predictive, and prescriptive.