6 Big Data Analytic Techniques

What is Big Data?

Big Data may be a collection of knowledge that’s huge in volume, yet growing exponentially with time. It’s a knowledge with so large size and complexity that none of traditional data management tools can store it or process it efficiently. Big data is additionally a knowledge, but with huge size.


Components of Big Data:

Big data processing and storage systems have become a standard part of data management architecture in businesses. The 3Vs are often used to describe big data:


Data is gathered from a multitude of outlets, including corporate purchases, Internet of Things (IoT) computers, industrial machines, recordings, social media, and more. Previously, storing it would have been a challenge, but the cost of storage has decreased thanks to platforms like data lakes and Hadoop.


With the rise of the Internet of Things, companies are receiving data at an alarming rate that must be processed quickly. The need to deal with these torrents of data in near-real time is being driven by RFID tags, cameras, and smart meters.


From hierarchical, numeric data in conventional databases to unstructured text records, notes, images, audios, stock ticker results, and financial transactions, data come in a variety of formats.



6 Big Data Analysis Techniques

The practice of analyzing data sets in the context of text, audio, and video and drawing conclusions regarding the knowledge they hold, most generally by particular processes, tools, and methods, is known as data analysis or analytics (DA). Data analytics solutions are used on a large scale in a variety of commercial business sectors because they enable companies to make measured, informed decisions.



Data Analysis Techniques:

Data drives the globe, and it’s analyzed every second, whether it’s Google Maps on your computer, Netflix viewing preferences, or what you’ve reserved in your online shopping cart. Since these approaches draw from a variety of fields, the analytic techniques can be used to analyze both large and small data sets:

1: Data Mining

Data mining is a popular technique used in big data analytics to derive patterns from massive data sets using a combination of statistics and machine learning techniques within database management. When consumer data are mined to determine the segments are most likely to respond to a bid, this is an example.

2: Data Fusion and Data Integration:

The observations are more effective and theoretically more reliable than if they were generated from a single source of data by combining a series of approaches that analyze and apply data from different sources and solutions.

3: Machine Learning

Machine learning, which is well-known in the world of artificial intelligence, is mostly used for data processing. It is a branch of computer science that uses computer algorithms to generate data-driven conclusions. It makes forecasts that human observers will be unable to make.

4: Natural Language Processing (NLP)

This data analysis technique, which is a subspecialty of computer science, artificial intelligence, and linguistics, uses algorithms to analyze human (natural) language.

5: Statistics

Inside surveys and studies, this technique is used to gather, organize, and analyze data.

6: A/B Testing

This data analysis approach comparing a control group to a series of comparison groups to see which interventions or improvements can boost a given objective indicator. Analysis of what copy, documentation, photographs, or architecture would increase conversion rates on an e-commerce site. Big data fall into this paradigm once again so it can measure large numbers; however, this can only be done if the classes are large enough to obtain significant variations.

Types of Big Data

Here are three types of Big Data, which are following.

  • Structured Big Data

Structured data is any data that can be collected, retrieved, and interpreted in a predetermined format. Over time, computer science expertise has been more effective in designing methods for dealing with such data where the format is well understood in advance and extracting value from it.

  • Unstructured Big Data

Unstructured data is any data that has an undefined type or structure. Unstructured data presents many problems in terms of retrieval in order to derive meaning from it, in addition to its enormous scale. A heterogeneous database containing a mix of the basic text files, photographs, videos, and other types of unstructured data is a good example.

  • Semi-structured Big Data

Both types of data can be found in semi-structured data. Semi-structured data appears to be structured, but it is not determined by a table description in a relational database management system. A data set described in an XML file is an example of semi-structured data.



How Big Data is Stored and Processed

Before putting big data to work for them, companies must understand how it moves between a variety of sites, channels, processes, owners, and customers. To take control of this large “data fabric,” which includes standard, structured data as well as unstructured and semi-structured data, there are five main steps to follow:

  1. Set a big data strategy.
  2. Identify big data sources.
  3. Access, manage and store the data.
  4. Analyze the data.
  5. Make data-driven decisions.

1) Set a big data strategy

A big data approach, at the most basic level, is a plan that aims to help you handle and enhance the way you collect, store, manage, distribute, and use data both within and outside the business. In the face of an explosion of data, a big data approach sets the tone for market growth. It’s important to think about current – and potential – industry and technology priorities and strategies while planning a plan.

2) Identify big data sources

Here are some sources mentioned, by which we can identify the basic source.

a) Streaming Data:

Wearables, smart vehicles, medical devices, industrial equipment, and other connected devices send data to IT systems via the Internet of Things (IoT) and other connected devices. You will review this big data as it comes in, determining which data to hold and which to discard, as well as which data requires further investigation.

b) Social media:

Interactions on Facebook, YouTube, Instagram, and other social media platforms provide data. This involves massive volumes of large data in the form of photographs, videos, speech, text, and sound – all of which can be useful for marketing, sales, and customer service. Since this data is often in unstructured or semi-structured format, it presents a particular processing and interpretation challenge.

c) Public data:

It comes from a slew of open data outlets, including the government’s data.

d) Other sources:

Data lakes, cloud data sources, manufacturers, and consumers are also possible sources.

3) Access, Manage and Store the data

Modern computer systems have the speed, resources, and versatility needed to access large volumes and forms of big data easily. Companies often include methods for integrating data, maintaining data integrity, supplying data control and storage, and planning data for analytics, in addition to secure access. Some data can be stored on-premises in a conventional data warehouse, but cloud solutions, data leaks, and Hadoop have a versatile, low-cost alternatives for storing and managing big data.

4) Analyze Big Data

Organizations may use any of their big data for analyses using high-performance technology like grid computing or in-memory analytics. Another method is to decide which data is important ahead of time and then analyze it. Big data analytics, in any case, is how businesses extract value and knowledge from data. Big data are increasingly being used to fuel sophisticated analytics projects including artificial intelligence.

5) Make intelligent, data driven solution

Data that is well-managed and trustworthy leads to trustworthy analytics and decisions. To remain competitive, companies must harness the full potential of big data and act on a data-driven manner, relying on facts provided by big data rather than gut judgement to make decisions. The advantages of data-driven decision-making are obvious. Organizations that are data-driven to perform well, are more predictable in their operations, and are more innovative.