Big Data Analytics: What Is It and How Does It Work?

Learn about big data analytics and how it can revolutionize your business.

Dusan Vasic Image
Updated:

October 17,2022

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Big data analytics is the process of examining large data sets in order to generate new insights. It’s typically defined as data sets that are too large or complex for standard data processing and analysis tools. 

We’ll cover all of the varieties, advantages, disadvantages, and precise workings of this technology in this article.

What Is Big Data Analytics? 

Big data, expressed simply, is a large volume of data that can’t be handled, stored, or analyzed using conventional tools and techniques. Instead, various innovative technologies are employed to interpret this data in the most practical manner.

By analyzing large amounts of data, analysts can uncover previously unseen information, including market trends, consumer preferences, and hidden data patterns. Today, we call this process big data analytics, and its benefits include enhanced decision-making and reduced fraudulent activity. 

Thanks to technologies such as business intelligence (BI) tools, we can access structured and unstructured data from multiple sources and input queries to better understand performance and business operations. 

Analyzing big data means combining advanced applications with what-if analysis, predictive models, and statistical algorithms. There are four essential methods for data analysis that are used for uncovering valuable insights

The future of this technology seems to be bright as 97.2% of the biggest organizations worldwide are now investing in AI and big data. We seem to be at the start of a period where this advanced technology might change the business world as we know it. 

How Does Big Data Analytics Work?

To employ big data analytics, organizations need to collect, process, cleanse, and analyze data to make the most of it. Since the technology is so advanced, businesses can get precious insights that help them decide, almost immediately, which steps to take next.

Data Collection 

Today, companies are able to collect both unstructured and structured data from a wide variety of sources, whether from clickstream data, cloud applications, web server logs, or Internet-of-Things sensors. A certain amount of data can be stored in data warehouses, which enables easier access, while raw and unstructured data can be stored in a data lake

Data Processing

After the process of collecting and storing the data is completed, big data analysis technology is used to organize the data to deliver the most accurate results on all potential queries. There are two ways to process data - stream processing and batch processing

The first method analyzes small batches of information simultaneously, which ensures quicker decision-making as it shortens the time between data collection and analysis. However, it’s far more expensive and complex than batch processing. 

On the other hand, batch processing deals with large batches of data. It’s mainly used for situations where there’s a longer turnaround period between analyzing and processing data. 

Data Cleansing

Data cleansing is the process of identifying and cleaning up inaccuracies and inconsistencies in data. It is a crucial part of data management, as it helps to ensure that data is accurate, complete, and reliable. 

There are a number of techniques that can be used for data cleansing, including manual review, automated scripting, and the use of software for data quality management. Scripting tools can be used to automate the process of data cleansing, and software for data quality management can help to identify and correct errors in data.

Analyzing Data

A few of the big data analysis methods used for processing big data into valuable insights are: 

  • Predictive analytics: The process of predicting upcoming opportunities and risks by using historical data. 
  • Deep learning: Another method used for establishing patterns in some of the most complex pieces of data by using artificial intelligence and machine learning. 
  • Data mining: Critical for recognizing patterns and relationships, data mining is done by discovering anomalies and creating clusters. 

Types of Big Data Analytics 

Big data analytics can be sorted into four separate types. Let’s take a closer look at each of them.

Diagnostics Analytics 

If we want to understand why a problem occurred, diagnostics analytics can help us find the answers. Business owners have a chance to recover and mine data that can help analyze the problem and prevent it from happening again. 

Prescriptive Analytics

When it comes to prescriptive analytics, its main goal is to offer a solution to a specific problem. That’s done mostly thanks to a combination of machine learning and AI, working together on solutions for better risk management. For example, big oil companies can identify which events can affect gas and oil prices and act accordingly.

Descriptive Analytics

Descriptive analytics is the process of analyzing data to summarize it and help people understand it better. It involves looking at patterns and trends and using statistical techniques to make the data more understandable. 

Descriptive analytics can be used to answer questions like “What happened?”, “How often did it happen?”, and “What was the most important thing that happened?”. It deals with information that’s easily interpreted - once extracted - and helps companies increase their profits. 

Predictive Analytics 

The field of advanced analytics, known as predictive analytics, predicts potential outcomes by utilizing past information in tandem with statistical modeling, data mining, and machine learning. As a result, businesses use predictive analytics to mine this information for insights into potential threats and profitable openings.

For example, if you want to establish when a machine will break down, you can use an algorithm based on historical data to get an approximate estimation. 

Big Data Analytics Benefits

There are a number of advantages that might assist companies in enhancing their operations, reducing errors, and enhancing overall performance. Here are the main ones: 

Customer Satisfaction 

Marketing efforts, such as targeted ads, can be improved by data-driven algorithms. Moreover, this can greatly affect customer experience and contribute to overall customer satisfaction as they will be able to receive better, more relevant ads and offers.

Cost Reduction 

If businesses apply the useful insights they get from big data analytics, they can cut costs by adopting more efficient approaches. Other than that, since all the business data can be stored in one place, costs can be significantly reduced.

Strategic Decisions

Numerous types of decisions, such as those regarding supply chain and cost optimizations, can be improved by businesses continuously tracking and analyzing data. The insights they get are precise and of extreme importance to both companies’ profit and performance. 

Risk Management

One of the strongest benefits of using big data analytics is finding patterns in data. Organizations can use these results to seek and identify risks and later develop proper solutions for managing them. 

Big Data Drawbacks

Big data analytics is indeed incredibly beneficial for many industries. However, it is still far from perfect. 

These are some of the most common drawbacks:

Data Security and Privacy

In recent years, the issue of data security and privacy has come to the forefront of public discourse. With the advent of social media, personal information is increasingly being shared online. 

This has led to concerns about how this information is being used and stored by companies, making it imperative for any organization to prioritize its data security before even starting to use big data analytics.

This relentless analysis of users’ data and customer behavior for the purposes of better-targeted advertising is practically conducted without the users’ permission. For this reason, an increasing number of people employ techniques such as data poisoning to confuse or sabotage big tech in their attempt to successfully collect their data.

Data Quality 

There is a long list of processes that need to be completed so that organizations can avoid errors, duplicates, and conflicts in their data. Keeping the quality of the data at the optimal level is a complex job that often requires much time and effort. 

Data Accessibility  

Data is becoming increasingly accessible as technology advances. Individuals are able to gather data from a variety of sources, including social media, online search engines, and government databases. This data can be used to improve decision-making, understand trends, and track progress.

However, it is important to note that not all data is equally accessible. The more the organization grows, the harder it is to collect data. For this reason, it is challenging for everyone within the organization to access information easily, and that is why proper solutions need to be brought forward. 

Big Data Analytics Tools

A combination of several methods is necessary to help organizations collect and finally analyze large amounts of data. 

Here are the key tools: 

  • NoSQL databases do not require one fixed scheme, and they are nonrelational types of data management systems. They’re a great solution for unstructured and raw data and work great with many different data models.
  • Apache Hadoop is great for unstructured and structured data, and one of its perks is that it’s an open-source framework that can store and process big data sets. 
  • Tableau is useful for prepping, analyzing, and sharing the data collected. This end-to-end analytics platform is great for self-service analysis and allows you to share the insights you get across your organization.
  • YARN, or “Yet Another Resource Negotiator,” is a component of Hadoop. It’s useful for resource management and job scheduling. 
  • Spark uses implicit data parallelism and fault tolerance while offering a great interface for cluster programming. This open-source cluster works well with both stream and batch processing.
  • MapReduce is one of the Hadoop key components and it has two main functions - reducing and mapping. Reducing organizes each node’s results, while mapping deals with filtering nodes from the cluster. 

Big Data Analytics Use Cases

Big Data is widely used in many industries. We’ll list some that actively use this type of technology.

  • Marketing - Big data analytics is one of the key contributors to driving ROI marketing campaigns, which improve sales. 
  • Telecommunications - This industry uses big data to analyze network capacity, and it’s used to enhance and improve the customer experience. 
  • Entertainment - Big data is used for analyzing the demand for movies, shows, and other content so the creators can understand the users better and offer them the sort of entertainment they’re most interested in. 
  • ECommerce - This industry benefits from big data analytics’ predictive features. In particular, eCommerce businesses benefit from knowing what the trends will be like and how to best optimize prices. 

Final Thoughts

Big data analytics is the process of collecting, analyzing, and extracting valuable insights from large data sets. These big data insights are of major importance to businesses that use them.

This technology is used in many industries to improve marketing campaigns, enhance the customer experience, and predict future outcomes. Big data analytics has some drawbacks, such as data security and quality concerns, but overall, it is a beneficial tool that organizations can use to their advantage.

FAQ
What are the four different kinds of big data analytics?

The four main types of big data analytics are descriptive, predictive, diagnostic, and prescriptive analytics.

Who uses big data analytics?

Big data analytics is used in many industries, such as education, eCommerce, healthcare, entertainment, education, and manufacturing.

What is the future of big data?

According to the results we have today, the future of big data analytics seems to be bright. More and more organizations are benefiting from the meaningful insights gathered by big data by generating more revenue, increasing performance, and growing more quickly.

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