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What Is Big Data Analytics? Why Is It Important? A Detailed Look

What Is Big Data Analytics? Why Is It Important? A Detailed Look

Big data and analytics are revolutionising the way we view the world. They are transforming businesses across sectors and innovating processes, operations, tactics, & strategies at an unnaturally rapid pace. Big data has been around for decades, but the last few years witnessed a sudden boom in data-driven research and operations. Resultantly, big data analytics is now a crucial business component across all sectors, from e-commerce ventures to social media networks, essay & assignment help services & beyond.

There are prominent reasons behind such a sudden surge in data science & big data analytics, and businesses all over are embracing big data analytics more than ever.

What has made big data analytics so indispensable? What benefits does it offer, and how does it work? This article takes an in-depth look.

What IS Big Data? How Does Big Data Analytics Help?

Big data refers to a complex set of structured, unstructured, and hybrid data from myriad sources. According to Oracle, big data boasts enormous variety and is generated in huge volumes & at high velocities. These are the three Vs of big data, which is so voluminous that traditional data processing procedures become inefficient. But, within such a vast volume of varied data lies valuable information that can elevate a business to a new level.

Despite what many might think, there’s nothing new about big data or the implementation of big data analytics in business. On the contrary, many have been doing it for years and have big data analytics systems embedded deep within their infrastructure.

  • Finance and fintech companies have been leading the way in handling massive amounts of customer data and processing billions of transactions. MasterCardis one of the most prominent examples of a well-established finance company that was a pioneer in using data analytics to enhance business performance.
  • Data is their primary product for social media, search engines, online video hosting, and streaming websites. Facebook,Twitter, LinkedIn, YouTube, and Netflix- these tech companies use analytics to analyse varied data & make informed decisions.
  • Companies like Googledeal with massive volumes of data of many different types and from various sources. Structured data from relational database management systems, unstructured image and video data, data from myriad sensors & IoT devices, social media & clickstreams- the list goes on.
  • Currently, modern MOOC platforms & academic service providers are using powerful technologies to understand their data pools better. This enables them to determine the best course content for an individual or a particular demographic, develop effective learning management systems, and offer personalised assignment help.
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Significant Brands That Use Big Data Analytics Source: www.analyticssteps.com 

With so many application areas and different kinds of data to work with, analytics platforms must be built to adapt & scale to almost any level.

Ever wondered how they are designed in such a manner? Well, the flexible and intricate underlying architecture makes it possible.

The Big Data Analytic Process

Three key aspects or stages are involved in any big data analytics operation.

1. Data Integration

Everything is data, and every piece of data contains some information or other. Data is all around us, with the Internet itself a massive ocean of structured & unstructured data. In addition, there are numerous sources, making it necessary for systems to integrate different types of data from an array of sources.

New tactics, tech, and ecosystems are necessary to normalise and standardise data formats for effective management and insightful analysis.

2. Management

Proper storage and cleaning are incredibly crucial for any data set. Be it in the cloud or on-premises; systems must ensure safe, secure, and reliable storage and make data available as necessary.

3. Analysis

In the climactic stage, analytics involves processing the data, and extracting valuable nuggets of knowledge. Discoveries thus made can be used to understand the data itself better and analyse it better.

Big data analytics models form the basis of AI & machine learning systems, albeit with different objectives and applicational areas.

The above three stages comprise 6 key steps that encompass all the procedural aspects of the big data analytics operational pipeline.

5 Steps To Effective Data Analysis

Before we analyse, we first need to find data & accrue data that are worth examining. So, naturally, data mining is the initial step of them all.

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1. Data Mining

Data extraction and mining are central to identifying valuable insights from any data. For example, if you own an online clothing store, you may be able to use specific research techniques to gleam that 60% of people buy to wear a particular brand—this is data extraction. Now, when you take that data and look into it further to determine the age & income brackets, that’s data mining right there.

Kaggle, RapidMiner, and Teradata are some significant examples of data mining tools.

2. Data Collection

As mentioned, data deluges are happening almost every single day. Therefore, you need systems that can capture and collect appropriate data accurately & consistently.

3. Data Storing

With massive amounts of data comes the need for massive data storage systems. Storage infrastructures must be developed in alignment with the company’s business needs and, must come packaged with potent analytics tools and be agile, scalable, & versatile.

Cloud storage providers such as Hadoop, Cloudera, and Talend are major names in the business.

4. Data Cleaning

There are humongous volumes of data and loads of information all around. One must be able to narrow down and focus on what is necessary & essential. Then, any extracted needs to be cleaned and filtered for practical analysis. This includes removing duplicate & incomplete entries, wrong & unwanted information, etc.

DataCleaner and OpenRefine are two exceptional tools for cleaning datasets.

5. Data Analysis

The climax, the main event! Data analysis involves using potent mathematical and statistical techniques to reveal helpful information from cleaned data. Three key analytical processes are carried out on data sets, namely, descriptive, predictive, and prescriptive.

Descriptive analytics aggregates and analyses data and offers descriptions & summaries that may become helpful in identifying patterns & further mining. Predictive analytics uses statistical algorithms and machine learning techniques to note trends & make predictions. Finally, prescriptive analytics is used to generate recommendations and support decision-making in a business. Business intelligence is a combination of descriptive & prescriptive analytics, while machine learning is an example of predictive analytics.

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Mathematics and statistics are central to almost every stage of the big data analytic process. If you wish to pursue a career as a data analyst or scientist, know that you will need a solid foundation in mathematics & statistics. Look for professional do my assignment help services if you lack skills or confidence.

So, before we round up, here’s a look at the vital numerical techniques powering the best big data analytic platform.

The Layers Of A Generic Data Analytics Process

The Mathematics & Statistics Behind Big Data Analytics

Linear algebra, number theory & set theory, probability, Bayes Theorem, calculus, optimisation, graph theory, and analytical geometry are some of the foundational domains of analytics algorithms.

Here are some key mathematical concepts used extensively in Big Data Analytics.

  • Quadratic Equations
  • Mean
  • Probability & Random Variables
  • Variance
  • Set theory
  • Random variables
  • Linear algebra
  • Coordinate Geometry
  • Logarithms & Exponents
  • Trigonometric Principles

And here’s a look at some prominent statistical techniquesemployed.

  • Discrete Probability Distributions
  • Continuous Probability Distributions
  • Distribution Parameters
  • Testing & Estimation
  • Random Variables

Well, that’s all the space we have for today! Hope it was an informative read for one and all. Big data analytics is challenging to master, so that you may need professional assistance at one time or the other. Enrol in a course or take professional assignment help from a reputed service only.

All the best!

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