Big Data Analytics in Today’s World

Big Data has been the buzzword since the early 2000s. So, most businesses knew that if they capture all the data, apply analytics, and get significant value from it. And this evolved to be big data analytics. Big Data Analytics improves businesses speed and efficiency, giving them a competitive edge like never before. In this article, we will explore the concepts and benefits of Big Data Analytics.

What is Big Data Analytics?

Big Data Analytics uses advanced analytics techniques against diverse and large data sets, which may be structured, semi-structured, and unstructured data collected from different sources and in sizes.

Big Data Hadoop uncovers the trends, patterns, and correlations in many raw data sets to give the businesses the insight for making informed decisions. These processes apply statistical analysis techniques like clustering and regression and apply them to data sets with the help of newer tools.

How does Big Data Analytics work?

Big Data Analytics collects, processing, cleaning, and analyzing large datasets, which helps organizations streamline their big data.

1. Data Collection

It looks different for every organization. Organizations can gather structured and unstructured data from various sources like cloud storage to mobile applications and beyond. Some of the data can be stored in data warehouses where business intelligence tools and solutions can easily access it. Data complex for a warehouse can be assigned metadata and stored in lake data.

2. Process Data

Data, once collected and stored, must be organized accordingly to get desired results on analytical queries. With the explosion of data, making data processing is a challenge for organizations. So they are handled in two ways of processing.

A: Batch Processing

It is useful when there is a longer turnaround time between collecting and analyzing data. It looks at large data blocks over time.

B: Stream Processing

It divides the data into small batches of data, shortening the delay time between collection and analysis, which helps in quick decision-making. One drawback of stream processing is it is more complex and expensive.

3. Data Cleansing

Data requires scrubbing to improve data quality and get accurate results. All the available data must be formatted correctly. And should eliminate any duplicate or irrelevant data. Flawed data can give the business wrong insights, which can go against effective decision making.

4. Analyze Data

Advanced analytics can turn big data into useful information for businesses, researchers in making faster and better decisions.

Types of Big Data Analytics 

Descriptive Analytics

This type summarizes the past data into a form that users can easily read. Descriptive Analytics helps in creating reports, like a company’s revenue, profit, sales, etc. It also helps in the tabulation of social media metrics.

Diagnostic Analytics

As the name goes, it diagnoses the cause of the problem. Techniques like data mining, drill-down, and data recovery are examples. Organizations use diagnostic Analytics because they provide an in-depth insight into the particular problem.

Predictive Analytics

It analyzes the history and present data to make predictions of the future. Predictive analytics uses data mining, AI, and machine learning to analyze the current data and make future predictions. It analyzes the market trends, customer trends, and so on.

Big Data Analytics Tools and Courses

In Big data analytics, several types of tools work together to collect, process, cleanse, and analyze big data. Some of the major players are listed below. These are also the trending Big data analytics courses in the current market.

a. Hadoop

It is an open-source framework that efficiently stores and process big data sets on clusters of commodity hardware. This framework is free, and it can process structured and unstructured data, making it a valuable tool for any big data operation. Learn Hadoop programming now.

b. NoSQL Databases

These are non-relational data management systems that do not require a fixed scheme, making them a great option for big data analytics. NoSQL stands for “Not only SQL” and this database is capable of handling a variety of data models.

c. MapReduce

It is an essential component of the Hadoop framework serving two functions. The first one being mapping, which filters data into various nodes within clusters. The second is reducing the results from each node to answer a query.

d. Spark

Spark is also an open-source cluster computing framework that uses implicit data parallelism and fault tolerance to provide an interface for entire programming clusters. It can handle both stream and batch processing for faster computation. To learn Apache Spark online, enroll now!

e. Tableau

An end to end data analytics platform that allows you to analyze, collaborate, and share big data insights. Tableau Online Training excels in visual analysis, which helps to get an in-depth insight into data across the organization. 

Amazing benefits of using Big Data Analytics

1. Analyzing a large amount of unstructured or structured data from different sources in different formats and types that too at a rapid speed.
2. Rapidly making better-informed decisions for effective strategizing, which can benefit and improve the operations, supply chain, and other strategic decision-making areas.
3. Cost Savings, resulting from new business process efficiencies and optimizations.
4. A better understanding of customer needs, behaviour and sentiment, which can lead to better marketing insights, as well as provide information for product development.
5. Better informed risk management strategies that draw from large data sets.

Sectors actively using Big Data Analytics.

• E-Commerce - It effectively optimizes the prices and customer trends using Big Data Analytics.
• Marketing - Helps to drive ROI marketing campaigns, which result in improved sales.
• Education - Big Data helps in developing new and improve existing courses based on market requirements.
• Healthcare - Analyzing the patient’s medical history. It is used to predict how likely the health issues may reoccur.
• Banking - Analyzing customer income and spending patterns to predict the likelihood of choosing bank offers.
• Telecommunications - Used to predict network capacity and improve existing customer experience.
• Media and Entertainment - Analyzing the demand of shows, songs, movies, etc., to deliver a more personalized recommendation list to users.
• Government - Big Data Analytics helps governments in law enforcement, among other things.

Market Share of Big Data Analytics

The global big data and business analytics market size was valued at USD 193.14 billion in 2019. And is forecasted to grow by USD 420.98 billion by 2027, with a growth rate of 10.9% from 2020 to 2027.

Conclusion

The future of all industries is heavily dependent on Big Data and analytics. Should the business leverage this technology to its full potential, which will elevate their revenue to a whole new level. Follow FolksIT regualrly to get instant technology updates.