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What is Big Data Analytics and Why It Matters

Maximize Business Potential with Big Data: Transform raw information into competitive advantages, driving innovation and smarter operations.

Big Data’s Role in Business Triumph: Harness the power of big data analysis to enhance customer experiences and drive sustainable growth.

What is big data?

Big data can be defined as too large for a relational database to handle and process in a reasonable amount of time. The wide variety of data (text, images, etc.) that make up big data determines it.

High volume (quantity of data), high speed (the speed at which data are generated and processed), or veracity can be used to define big data. Unstructured data is the most common type of big data.

Structured data follows patterns and is easily searchable. Unstructured data, which includes social media posts and emails, chat communications, audio, and video, can be grouped as “everything.”

Unstructured data can be transformed into structured data using data mining tools. Unstructured data can contain vital information, so using tools is important.

Unstructured data is valuable when adequately understood and makes up the majority of data, about 80% of enterprise data.

Big data continues to grow and expand as more data becomes available daily. Big data requires advanced technologies and techniques to store, manipulate, clean, and map it.

History of Big Data Analytics

Big Data analytics has its roots in the early days when organizations used computers to store and analyze large volumes of data. In the early 2000s, when organizations began turning to computers, big data analytics took off.

Big Data analytics is now vital for companies of all sizes and across many industries. Big Data allows organizations to gain new insights about their customers, businesses, and the world.

In the coming years, as the field of big data analytics continues to develop, we will see more and more incredible applications that transform how people live.

Different Types of Big Data Analytics

Here are the four types of big data analytics:

1. Descriptive Analysis

This summarizes past data in a way that is easy to read. It helps create reports such as a company’s sales, revenue, and profit. It also helps with the tabulation and analysis of social media metrics.

Dow Chemical Company analyzed its data to maximize the use of its lab and office space. Dow Chemical Company was able to use descriptive analytics to identify underutilized space. The company was able to save almost $4 million per year by consolidating its space.

2. Diagnostic Analytics

It is used to determine what initially caused the problem. Examples include techniques like data mining, drill-down, and data recovery. Organizations use diagnostic analytics because they give detailed insight into a problem.

Use Case: A report from an e-commerce firm shows that sales are down, even though customers continue to add products to their shopping carts.

It could be because the form did not load correctly, the shipping fee needed to be lowered, or there needed to be more payment options. Diagnostic analytics can help you find out the cause.

3. Predictive Analysis

Predictive analytics uses historical and current data to make future predictions. Data mining, AI, and machine learning are used to analyze data to make future predictions.

It works by predicting market trends, customer trends, etc. PayPal decides on the precautions to be taken to protect its clients from fraudulent transactions.

The company builds an algorithm to predict fraudulent activity using predictive analytics. It uses historical payment data as well as user behavior data.

4. Prescriptive Analysis

This type of analysis prescribes a solution to a specific problem. Perspective analytics is a combination of descriptive and predictive analytics. It relies on AI, or machine learning, most of the time.

Use Case: An airline can use prescriptive analytics to increase its profits.

This type of analysis is used to create an algorithm that automatically adjusts the flight fares depending on several factors, including customer demand and weather.

It can also be used for holiday seasons, oil prices, and destinations.

Big Data Analytics Tools

Here are some of the key big data analytics tools:

  • Hadoop is a tool for storing and analyzing data
  • MongoDB is used for datasets that are frequently changing.
  • Talend is used for data management and integration.
  • Cassandra is a distributed database that can handle large chunks of information
  • Spark is used to process and analyze large amounts of data in real-time.
  • STORM is an open-source real-time computing system
  • Kafka is a fault-tolerant streaming platform used for distributed storage.

Big Data, the buzzword that was once “big” in the last century, has now become the norm for many companies, large and small.

Big data analysis, and companies are increasingly using other associated analysis approaches to support their management and serve their customers more efficiently.

According to Igor Gamanenko, the success manager of Semalt’s client, big data analysis means focusing on the correct information, analyzing the information in real time, and using the results to make strategic business decisions.

As such, it is an integral part of every company that wishes to remain competitive or grow. Big Data has become the basis of most innovation work and greatly helps improve business performance and customer service.

How Big Data Analysis helps in business

Leverage Big Data for Business Success:

Discover actionable insights and boost efficiency through robust data analysis strategies. Drive growth, optimize processes, and make informed decisions using data-driven strategies.

Get a better understanding of your organization:

Big data analysis helps you better understand your organization. You can know the performance of the different business sections and measure their efficiency.

Through this analysis, prescriptive measures can be taken to make the workforce and business processes more efficient.

Understand the market and the strategies of your competitors.

With big data analysis, you can get ahead of the competition. The results of the big data analysis present reliable information about the market.

A lot of feelings about competitors are also unearthed. These ideas and feelings help create strategies and make the right decisions for the business.

New products and services:

A company can better understand what a customer wants by analyzing the large amount of information generated during transactions and interactions with customers.

With this understanding, the company can not only offer better after-sales services, but can also be innovative regarding the production of new products and services.

Control your brand.

To make a respectable brand in today’s business world, you must ensure that your online presence is well managed.

Big Data analysis allows an organization active in social networks to monitor its brand more accurately.

The analysis gives a good idea of ​​how the organization looks on the web. The results of the analysis also report on the areas that may need improvement.

Minimize the risk of making the wrong decision.

Big data analysis provides valuable information that helps prevent wrong decision-making.

Making incorrect business decisions can be lethal to companies, which is why every serious company tries everything it can to make data-based decisions.

There is no better way to make informed decisions than using big data analysis.


When big data analysis is done correctly, a company can discover the most hidden ideas, reduce risks, and improve decision-making. Leveraging your data can easily create a competitive advantage for your business and lay the groundwork for remarkable growth.

Semalt As technology continues to be a major disruption in business, big data and analysis gain more potential to shape the business. In industries such as manufacturing, big data analysis almost claims the core of digital factories.

More and more professionals recognize the importance of analysis. Soon, analysis will be an integral part of real-time performance perception, as well as real-time logistics and the production of quality analysis.

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