Big Data And Analytics - Definition, Benefits, And More

Big data and analytics: Every day huge amounts of data is generated from various sources, such as smartphones, emails, social media, transactions, etc. Big Data is a large collection of data that originates from multiple sources. Many organisations make use of this complex data to their advantage.

They collect this large volume of data and put it to use. Big data and analytics are two terms often used together. But why? Let us understand big data and analytics in this article.

What is Big Data?

Big data is a large volume of data collected from various sources, such as social media, email, text, audio, transactions, forms, etc. Nowadays, there is an abundance of data produced every minute, which can be used by customers and companies to grow their businesses.

For example, keeping a track of customers’ shopping habits to provide more personalised recommendations, assisting in weather alerts, GPS navigation, accessing medical records for effective treatment, tracking taxes, optimising cost of businesses, etc.

Also Read: 5 Unique Ways to Use AI (Artificial Intelligence) in Data Analytics

What is BigData and Analytics?

You must have heard the term ‘Big data analytics’ often used together. But what is Big data analytics and why is it used together? Well big data and analytics are connected. Without proper analytics only collecting data is not sufficient to bring value to the businesses.

Big data analytics is the process of extracting trends, patterns, and finding correlations among unstructured data to help businesses make more informed decisions. Analytics is the use of different statistical techniques and tools to extract important insights from unstructured and raw data.

Nowadays, big data analytics is integrated with advanced technologies like machine learning and AI to extract more complex insights from raw data. Big data analytics can provide more effective decision making, forecasting, real-time tracking, preventing fraud activities, etc.

Why is BigData Analytics Important?

Big data analytics uncovers the potential of the large amount of data available from various sources to be used by businesses for profit and risk management. It helps businesses make informative decisions by studying patterns, uncovering trends, and studying correlations in complex, unstructured data.

Consider an example of a shopping app like Amazon that you must have used at least once. A large number of people visit the app and scroll through the contents daily. You must have noticed getting personalised recommendations based on the items you were scrolling through on the app.

Now, Amazon uses big data analytics to prepare smart recommendation techniques using search history, shares, likes and much more. It helps prepare a more personalised recommendation for the customers based on their needs and requirements.

Also Read: 10 Most Popular Big Data Analytics Tools

How Big Data and Analytics Works?

Big data and analytics consist of various stages, such as collecting, processing, cleaning and analysing large sets of data to use them for strategic decisions. Let us learn about some of these important stages in big data analytics.

1. Data Collection

Nowadays, the world has a variety of technologies and tools to extract unstructured as well as structured data from multiple sources. There are many data storage services that allow data to be used effectively for analysis.

2. Data Processing

The huge amount of data collected from various sources consists of raw and unstructured data arranged irregularly. After collecting and storing the data, it needs to be processed. Data processing is a complex task as the data keeps growing which is a huge challenge for organisations.

There are two methods of data processing, one is batch processing, where large data blocks are processed at once. The second method of data processing is stream processing, where small batches of data are collected and processed at once. Stream processing is more effective than block processing but is often more expensive.

3. Data Cleaning

After processing the data, it needs to be cleaned and arranged in a proper format. The redundant data, null values, missing values and other discrepancies must be removed, which can often create misleading or flawed results.

4. Analysing Data

After collecting, processing and cleaning big data, it is then prepared for advanced analytics, which can extract important insights from the data. It is an important step to convert the data into usable form. Various methods, such as data mining, predictive analysis, machine learning and deep learning methods are used for analysing data.

Benefits of Big Data Analytics

Big data analytics help uncover hidden trends, study patterns, study correlations among raw data and much more. However, let us know some real world uses and benefits of big data and analytics below.

With the help of big data and analytics, quick and informative decision making is possible. For example, if you want to start a clothing business, you will analyse various factors regarding the location to decide whether it will work or not. Some major factors are accessibility of location, population, age group, demographics, etc.
With the help of big data analytics, more personalised recommendations can be provided to customers to improve customer satisfaction.
The insights collected from the big data are used for product development and improvements. Big data analysis helps to look for the scope of improvement based on users needs and requirements.
Big data analytics is widely employed to help in managing and mitigating risk factors by identifying fraudulent activities and unauthorised access. It can help find the root cause of the problem and ways to improve it.
With the help of effective big data analysis, the customer experience significantly improves with more personalised filters, personalised recommendations, quick solutions, etc. For example, with the help of big data analytics, railways, airlines, etc. can interpret tweets related to complaints and address them on priority. This builds a good customer relationship and trust among citizens.
Big data helps organisations identify cost saving techniques or more efficient ways of handling an operation.
It can make forecasts regarding the uncovered trends, study patterns and discover trendy investment options for businesses.

Also Read: Book About Data Analytics: Top 15 Books You Should Read

Four Major Types of Big Data And Analytics

There are four major types of big data analytics given below.

1. Descriptive Analytics

It uses historical data collected from various sources to understand past performance. It performs data mining, visualisation, and aggregation to uncover trends, patterns, and forecast future occurrences.

2. Diagnostic Analytics

It analyses data to find the reason for the occurrence of a certain event. It analyses data to predict the root cause of the anomalies through descriptive analytics. It involves static analysis and often hypothesis testing.

3. Predictive Analytics

Predictive analytics uses available data to forecast future events or results. It uses machine learning algorithms to predict future trends, patterns, and behaviours.

4. Prescriptive Analytics

Prescriptive Analytics focuses on providing suggestions to optimise and fix the outcome. It uses diagnostic, descriptive, and predictive analytics together to recommend the best course of action for various events.

Major Challenges of Big Data and Analytics

Some of the major challenges faced by big data analytics in the current scenario are mentioned below.

Collecting big data from multiple sources is a tedious and complex task. It becomes tough to handle the ever growing data in a limited space.
Filtering quality data by removing irregularities, redundancies, errors, etc is a time taking process and not always 100 percent efficient. Finding these inconsistencies is a much more complex task.
Security concerns such as preventing unauthorised access, fraud, cyberattacks, data loss, etc are rising concerns with the increased dependencies of businesses on these data.
Finding an expert who can easily handle big data analytic tasks is tough and unpredictable. With the increasing number of emerging technologies, tools and technologies are constantly updating themselves. Someone who can handle these tasks and is flexible can be a good option.
Finding the right tool is very important for handling different stages of big data analytics. These tools and platforms play a very crucial role in making informed decisions and making the right predictions.

Also Read: Applications of Big Data

BigData and Analytics Tools and Techniques

There are various tools, technologies and algorithms used at each level of big data analytics. Let us know some of the commonly used tools and technologies in big data below.

- Tableau: It is a tool that allows you to collect, analyse, visualize and share big data insights easily. It helps businesses to find valuable insights from their datasets. It consists of an easy interface with a clean visual analysis interface, which helps to manage data and also share it among various stakeholders.

Apache Hadoop: This tool is an open source platform used for data analytics. It supports multiple platforms and is used to collect and store large amounts of data for businesses. It is quick and can be used for parallel processing. It can easily integrate machine learning algorithms and data science tools to make big data and analytics more effective.

- Spark: It is a powerful big data analytic tool which can be used to perform data analytics, warehousing, real time analytics, batch processing, and much more. It can also integrate AI and machine learning algorithms easily.

MongoDB: MongoDB is a non-relational database tool that can be used to modify and store large amounts of data. It supports various programming languages, such as JScript, Ruby, Python, etc. It provides various features such as data mining, a content management system, real time data analysis, the internet of things, and customer relationship management.

Apache Storm: It is a popular open source platform to process large amounts of data in real time. It is suitable for applications having low latency and support for parallel processing. It is also used in predictive analytics, fraud detections, and much more.
Rapid Miner: It is a data science platform with tools to perform effective data extractions, data preparation, text mining, predictive analytics, etc.
Cassandra: It is a distributed database platform used to fetch large amounts of data from various sources. It provides data storage flexibility, fast processing, fault tolerance and much more.

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Big Data And Analytics FAQs

What are big data and analytics?

Big data analytics is the process of extracting trends, patterns, and correlations among unstructured data to help businesses make more informed decisions based on available data.

Is big data analytics a good career?

Big data has become a crucial element for businesses to grow ahead of their competitors. Demand for skilled big data analytics is increasing day by day. It is a good career option for someone with knowledge of data analytics, data science, and machine learning.

What is an example of big data analytics?

Some examples of big data analytics are keeping track of customers' shopping habits to provide more personalised recommendations, assisting in weather alerts, accessing medical records for effective treatment, tracking taxes, optimising cost of businesses, etc.