Why is Big Data Analytics Important?

Big-Data-Analytics

All data can be analyzed. For it to be called big, it has to be extensive, fast-growing, and diverse. Big data in its rawest form becomes even more unmanageable with each passing year, and conventional data processing software has been and remains unable to handle it properly. That’s where big data analytics comes into play.

Numerous data analytics services provide tools for accurate and straightforward data analysis that helps organizations keep abreast of everything that takes place during their internal business processes. The value of big data analytics is hardly overplayed and remains a necessary addition for many industries. Let’s find out why.

What is Big Data Analytics?

Most of the unprocessed data that exists in the world has various hidden patterns or abnormal sequences, otherwise undetectable if you only use your own pair of eyes or rather primitive data analysis tools, such as basic spreadsheets. 

Instead, the analytics software and solutions specifically designed for big data allow for a more complex and extensive analysis necessary to predict market trends, customer behavior, unusual correlations, etc. 

Big data analytics solutions are able to process and analyze all three existing types of big data – structured, unstructured, and somewhat structured, which means they are equally helpful, regardless of whether the data has a clear-cut format or not.

Types of Big Data Analytics

Like all software, depending on its usage and purpose, there are different types of big data analytics.

1. Descriptive

A lead-in to all the following types of big data analytics, the descriptive method analyzes historical data in extra detail to provide the needed clarity and prepares it for the upcoming assessment.

2. Prescriptive

This method is used to provide answers and a course of action for specific questions and problems, and how to avoid them in the future.

3. Predictive

Also used for predicting the future, this type of analytics heavily utilizes mathematical and statistical models to search for prior patterns.

4. Diagnostic

This method answers not what has happened, but why, and does so by comparing historical data with useful information from other sources. 

Methods of Application

Big data analytics is successfully applied in various industries and practices.

Business

Numerous businesses use big data analytics to help them make thought-out decisions based on research and historical data analysis, as well as improve on their internal processes.

Industries

Many industries, such as banking, manufacturing, healthcare, education, etc. benefit from utilizing such analytics – from upgrading the level of customer care to preventing fraudulent behavior.

Data Science

The most prominent examples are data and text mining, with the latter also implementing artificial intelligence technologies to better go through disorganized text.

Machine Learning and Deep Learning

As an AI subset, machine learning, along with big data analytics, provides even faster and more accurate models of data analysis. Deep learning, in turn, helps to extract layers of hierarchical data from an unsorted mess.

Predictive Analytics

This type of analytics turns both real-time and past data into actionable insights using statistics and machine learning methods. It’s heavily used in risk assessment, fraud prevention, and marketing campaigns.

Value of Big Data Analytics

With both intrinsic value and such that is acquired through different types of analysis, the benefits of big data analytics are currently countless:

  • Helps break down complex and bulky data into manageable chunks;
  • Improves decision-making process as it gives a reason behind any type of action and the likely outcome;
  • Keeps businesses informed about numerous opportunities;
  • Helps design new products that will have definite success on the market;
  • Elevates customer experience by providing tools for personalization, recommendation, and better-targeted campaigns;
  • Minimizes and mitigates risks by scanning for potential threats and disruptive patterns;
  • Real-time issue detection in supply chain and other business processes.

Bottom Line

There’s a rather expected success that follows organizations that have decided to invest in data analytics software to transform their raw data into tangible opportunities. Click here to contact our team if you need a reasonable solution for your business intelligence and data analytics needs.