What is big data analytics? - upGrad Campus

What is big data analytics?

October 8th, 2021

What is big data analytics?

Big Data Analytics - These are the most heard words these days. Why is it trending? Because of the big amount of data produced these days by the consumers, this data acts as a treasure for organizations.

Big data analytics describes the process of collecting, structuring, and analyzing large amounts of raw data to help make informed decisions. This process applies statistical analysis techniques to analyze extensive data with the help of various tools. The software and hardware capabilities made it possible for organizations to integrate vast amounts of complex information. 

Let’s Have a Broader View of How Big Data Analysts Work?

The huge information or ‘Big Data’ mentioned above needs to be utilized in a meaningful way which is done through big data analytics. This analysis and review of data guides organizations to make smarter business decisions which lead to higher profits and satisfied customers. The big data analytics process includes collecting, processing, organizing, and analyzing data. Organizations access both structured and unstructured data from a variety of sources using the latest technology. The collected and stored data are organized properly to ensure accurate results on analysis. The organized data is analyzed through advanced analytics processes to turn them into big insights for business operations.

Companies and enterprises use various big data analytics tools to manage a huge volume of data generated. Big Data Analytics tools also help businesses to analyze data efficiently which saves time and money. There are various types of tools that serve to improve the process of data analysis and gaining insights to facilitate data-driven decisions. 

Big Data Analytics Tools

Some of the key big data analytics tools are listed below:

  • R-Programming is a programming language specifically designed for statistical analysis.
  • Lumify is a tool that enables the exploration of connections and relationships between data. 
  • RapidMiner is a software platform used to integrate data and machine learning. 
  • Microsoft Azure is a platform handled by Microsoft and provides services like computing, analysis, and storage of data.
  • Apache Spark is a robust big data analytics tool that can quickly possess a large amount of data
  • Xplenty is a cloud-based platform that allows data flow across sources and destinations. 

Types of Big Data Analytics

Big data analytics has various stages and types that have to be followed to get desired results. In general, the types of big data analysis are as follows:

  • Prescriptive - The objective of prescriptive analytics is to recommend the best possible solutions for a present situation as the analysis suggests from the available data. 
  • Predictive – It is used to forecast future conditions based on suggested patterns in the data. It is the most commonly used type of analytics for predicting the outcome depending on how a company responds to a situation.
  • Diagnostic - This type of data analytics is used to diagnose the response and corresponding outcome mostly related to a past event or situation.
  • Descriptive - Descriptive analytics uses statistics and segmentation techniques to analyze what has happened. This can be time-sensitive so it should be used with more recent data.

Big Data Analytics Applications

Big data analytics applications in various sectors of industries are as follows:

  • E-commerce – Data analysis to predict customer trends and optimizing prices 
  • Marketing – Implement data-driven marketing strategies for better ROI and improve sales
  • Education - Develop new courses and improve existing learning methods based on data about student requirements and preferences
  • Healthcare - Analysis of patient’s medical history helps to address health issues and proactively decide on treatment procedures 
  • Media and entertainment – Analysis of customer feedback data to deliver a personalized program list
  • Banking – Analysis of customer profile helps to select various banking offers
  • Telecommunications – Use of data to assess network capacity to improve customer experience.

In general, big data analytics help organizations to build a competitive advantage through efficient productions, innovative solutions, and conscious business decisions. Organizations adopt big data analytics to get ahead of the competition by improving their existing services and products. With real-time big data analytics applications, businesses can make timely decisions to maximize revenue. They can improve productivity through workforce data analysis and monitor product performance over a specific period. As this large amount of data moves across systems daily, information security can be a persistent concern. A critical analysis of such data reflects trends and patterns which can be judiciously used to detect and address chances of fraud.

Big Data Solutions

Big data solutions are necessary measures that involve capturing all of the organization's data, utilizing it for analytics and making strategic decisions to get solutions. Analytics should start from the leadership stage and be embedded into entire business processes. It provides the solution to different levels of management operations which leads to effective utilization of capacity and resources. Some pertinent solutions that big data analytics provide are given as follows: 

  • Risk Management: Big data analytics is used to identify discrepancies in the system, analyze root causes of problems to take corrective actions, and provide solutions for the mitigation of risks.
  • Product Development and Innovations: Manufacturing sectors across the globe uses big data analytics to analyze the efficiency of product performance and plan accordingly for improvements.
  • Quick and Better Decision Making: Big data analytics provides relevant recommendations through analysis of data, which helps to make effective and timely decisions.
  • Improve Customer Experience: The data regarding customers’ feedback is analyzed for both positive and negative aspects. By addressing the issues and offering solutions, it helps the organization to build a good customer base.

Big Data Analytics Challenges

It’s a challenge for enterprises to store, manage, utilize, and analyze such an enormous amount of data. Large business organizations are constantly looking for ways to make this huge amount of data useful for their business purpose. Some of the big data analytics challenges being faced are described as below:

  • As data sources are becoming bigger and more diverse, there is a big challenge to synchronize them.
  • It is important for business organizations to effectively use the data skills of professionals who are experts in data analytics. So a major challenge faced by businesses is the shortage of professionals who can handle big data analytics.
  • A major challenge faced by the companies in big data analytics is that only the relevant department should have access to this information, to get better insights for making decisions. It is considered vital to making data access easy and convenient for brand owners and stakeholders.
  • Another big challenge in big data analytics is to find the best-suited technology to handle a variety of data and address new problems and potential risks.
  • A real challenge is the storage of a massive amount of data, both structured and unstructured. Inconsistent data, logic conflicts, and duplication are some of the data quality challenges.
  • Big data analytics offers a wide range of possibilities and opportunities, but also involves the potential risks associated in terms of privacy and security of data.

Big Data Analytics uses advanced analytical techniques to a diverse, large data set that consists of unstructured, semi-structured and structured data, collected from different sources. Analysis of this big data by analysts and researchers helps business users to make better and quicker decisions using data in comparison to the traditional way of decision making using data that is inaccessible or unusable.

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