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Big Data Analytics: How It Works and Its Benefits

Big Data Analytics. Working And Benefits.

Data is so pervasive in today’s society that it’s impossible to account for all of the ways it influences daily life. Each day, data is gathered on a very large scale, so much so that it’s now referred to as big data.

This article will take you through the inner workings of big data, how it’s collected, and the role it plays in the modern world.

What is big data?

Today, nearly everyone uses technology to get work done, whether it’s to perform the most basic of tasks or those that are highly complex. During this process, people around the world generate a lot of data even though they may not be aware of it.

Big data is the huge amount of data that is created when people text, email, run an application, facilitate financial transactions, etc. This volume of information is stored in the form of raw, unorganized clusters called big data.

Unsurprisingly, big data is very difficult to handle, mostly because of the 3 Vs: volume, velocity, and variety.

3 Vs of big data

  • Volume:  This refers to the amount of data generated when people interact with any application on the internet.
  • Velocity: This is the speed at which data is being generated. The more people use devices, the faster the velocity.
  • Variety: This refers to all different structured and unstructured data generated on the internet. Some examples are  tweets, pictures, videos, voicemail, etc.

The 3 Vs of big data can’t be processed using normal software. Instead, a different domain known as big data analytics is used to address challenges and derive significant results from them.

Besides what big data theoretically means, how exactly do organizations employ it? When used in conjunction with analytics, big data fusion helps them combine data from many sources to develop a more comprehensive and unified model in order to gain a better understanding of the data. Organizations also invest in artificial intelligence (AI) and machine learning (ML) to sift through data from various sources in an effort to create cohesive and accurate insights.

What is big data analytics?

Today, businesses, irrespective of their size, are using analytics tools to determine how data can be leveraged in business use-cases.

Big data analytics is the field where big data is used to uncover different patterns, customer preferences, and correlations through which sound business decisions can be made. Organizations use this data to gain an edge over the competition.

Thanks to the ever-growing demand for big data analytics, new statistical methods and algorithms are being developed every day. They help address the different needs of customers and businesses.

How does big data analytics work?

Big data analytics works in different phases, ranging from collecting data to cleaning to preprocessing and analysis.

Data collection

Data collection is the process of collecting data directly or indirectly from customers. Different businesses have their own method of doing so. These can be through reviews, social media, surveys, volunteers, previous purchase data, etc.

Data preprocessing

Data preprocessing is the stage in which the collected data or the raw data is transformed into well-ordered data sets for higher accuracy in the complete analytics process. This stage ensures that the transformed data has no missing or noisy data.

Data cleaning

Here, the transformed data is thoroughly filtered to ensure high data quality. Any incorrect or irrelevant data is corrected or removed in the data set.

Data analysis

This is the analytics stage where data is used to extract valuable business insights. Depending upon the organization, different tools and software are used for big data analytics such as ML, deep learning, AI, business intelligence software, etc. However, irrespective of the tools, big data analytics can be summarized into four categories:

  1. Descriptive analysis:  It provides insights into past events and trends and uncovers any pattern, if it exists. Descriptive analysis utilizes techniques like statistics, clustering, etc., which is used in market basket analysis. Applications include company sales, profits, revenue, etc.

  2. Diagnostic analysis: It’s used for in-depth analysis to understand the root cause of a problem. Diagnostic analysis is applied when there is a need to find the reason behind a particular event. Various techniques like data mining and data recovery are employed here.

  3. Predictive analysis: This uses past incidents and market trends to uncover different patterns in them. It applies these patterns to forecast future demands and trends.

  4. Prescriptive analysis:  It runs its own analysis on the result of the above analysis to explore several possibilities. Depending on different actions, it suggests the best potential one to take.

Advantages of big data analytics

The overarching goal of big data analytics is to help organizations identify opportunities and risks. More specifically, it helps in:

  • Saving time and costs: Big data analytics can help organizations optimize their business by focusing more on their profit-making sectors.
  • Risk management: It can help foresee upcoming risks.
  • Better decision-making : It helps organizations improve decision-making models compared to traditional analysis methods.
  • Making better products:  Businesses can gain a better understanding of customer needs, which in turn helps them create products that are more desirable to consumers.
  • Bringing innovation:  Businesses can use big data analytics in their research and development (R&D) to facilitate new innovations that consumers want.
  • Deriving key insights: It helps businesses predict future trends and provides insights into their customers’ buying patterns.

Challenges of big data analytics

Though there are many advantages of big data analytics, it has a few challenges:

  1. Data collection quality:  Data from multiple sources needs to be verified and doing so can be a hectic process for organizations.
  2. Data accessibility: Handling enormous volumes of data can be complicated and can severely affect predictions.
  3. Proper analytical tools: With the increasing number of tools and software for analyzing data, selecting the most suitable can be confusing for those who aren’t well-versed in big data analytics.
  4. Data security: It can be tricky to properly address the security concerns of the data stored since it’s so large and complex.
  5. Not for the short-term:  Big data analytics is not useful in the short-term as leveraging it is a long-running process.

As the saying goes, data is the new oil and big data analytics is no exception. How it’s used to fuel business growth depends on how organizations employ it. So far, it’s helped businesses attain new heights and it looks set to do the same for the future.

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