What is Big Data Analytics ? Definition with Video


What is Big Data Analytics ? Definition with Video

What is Big Data

Data which are very large in size is called Big Data. Normally we work on data of size MB (Word doc, Excel) or maximum GB (Movies, Codes) but data in Peta bytes is called Big Data. It is stated that almost 90% of today’s data has been generated in the past 3 years.

Sources of Big Data

These data come from many sources like ,

Social networking sites Facebook, Google, LinkedIn all these sites generates huge amount of data on a day to day basis as they have billions of users worldwide.

E-commerce site Sites like Amazon, Flipkart, Alibaba generates huge amount of logs from which users buying trends can be traced.

Weather Station All the weather station and satellite gives very huge data which are stored and manipulated to forecast weather.

Telecom company Telecom giants like Airtel, Vodafone study the user trends and accordingly publish their plans and for this they store the data of its million users.

Share Market Stock exchange across the world generates huge amount of data through its daily transaction.

3V’s of Big Data

These are mainly 3 V’s of Big Data. Here is Follows –

  • Velocity
  • Variety
  • Volume
  1. Velocity The data is increasing at a very fast rate. It is estimated that the volume of data will double in every 2 years.
  2. Variety Now a day’s data are not stored in rows and column. Data is structured as well as unstructured. Log file, CCTV footage is unstructured data. Data which can be saved in tables are structured data like the transaction data of the bank.
  3. Volume The amount of data which we deal with is of very large size of Peta bytes.

What is Big Data Analytics ?

The term ” Big Data ” refers to digital stores of information that have a high volume, velocity and variety. Big Data Analytics is the process of using software to uncover trends, patterns, correlations or other useful insights in those large stores of data.

Data analytics is not new. It has been around for decades in the form of business intelligence and data mining software. Over the years, that software has improved dramatically so that it can handle much larger data volumes, run queries more quickly and perform more advanced algorithms.

Types of Big Data Analytics

The market research firm Gartner categories Big Data Analytics tools into four different categories:

  • Descriptive Analytics
  • Diagnostic Analytics
  • Predictive Analytics
  • Prescriptive Analytics
  1. Descriptive Analytics

These tools tell companies what happened. They create simple reports and visualizations that show what occurred at a particular point in time or over a period of time. These are the least advanced analytics tools.

  1. Diagnostic Analytics

Diagnostic tools explain why something happened. More advanced than descriptive reporting tools, they allow analysts to dive deep into the data and determine root causes for a given situation.

  1. Predictive Analytics

Among the most popular big data analytics tools available today, predictive analytics tools use highly advanced algorithms to forecast what might happen next. Often these tools make use of artificial intelligence and machine learning technology.

  1. Prescriptive Analytics

A step above predictive analytics, prescriptive analytics tells organizations what they should do in order to achieve a desired result. These tools require very advanced machine learning capabilities, and few solutions on the market today offer true prescriptive capabilities.

Advantages of Big Data Analytics

These are various advantages of Big Data Analytics. Here is Follows –

  1. Timely It can save plenty of time since on every working day 60% knowledge workers are spending time attempting to find and manage data.
  2. Accessible Half of the senior executives report that accessing the right data is difficult. So this helps to access the data more vulnerable.
  3. Trustworthy Due to poor data quality in the average of 29% companies are measuring the monetary cost. Even the simple things like customer contact information updates monitoring in multiple systems will help the company to save millions of dollars.
  4. Relevant Keeping irrelevant data is a curse for the database since it will make the filtering process complicated. But the statistics say, around 43% of companies are having tools which are unable to filter the junk data. A simple thing like filtering the customers from web analytics will be able to provide an insight for the efforts of your acquisition.
  5. Secure With data hosting and technology, companies can secure their infrastructures since an average of the security breach in any company costs $214. So with this technology, the company can save up to 1.6% of their revenue per year.

Importance of Big Data Analytics

The Big Data analytics is indeed a revolution in the field of Information Technology. The use of Data analytics by the companies is enhancing every year. The primary focus of the companies is on customers. Hence the field is flourishing in Business to Consumer (B2C) applications. We divide the analytics into different types as per the nature of the environment. We have three divisions of Big Data analytics: Prescriptive Analytics, Predictive Analytics, and Descriptive Analytics.

  • Data Science Perspective
  • Business Perspective
  • Real-time Usability Perspective
  • Job Market Perspective

Real-time Benefits of Big Data Analytics

There has been an enormous growth in the field of Big Data analytics with the benefits of the technology. This has led to the use of big data in multiple industries ranging from:

  1. Banking
  2. Healthcare
  3. Energy
  4. Technology
  5. Consumer
  6. Manufacturing

There are many other industries which use Big Data Analytics. Banking is seen as the field making the maximum use of Big Data Analytics.

Big Data Analytics Challenges

These are various challenges of Big Data Analytics. Here is Follows –

1.) Implementing a Big Data Analytics solution is not always as straight forward as companies’ hope it will be. In fact, most surveys find that the number of organizations experiencing a measurable financial benefit from their big data analytics lags behind the number of organizations implementing big data analytics. Several different obstacles can make it difficult to achieve the benefits promised by big data analytics vendors:

2.) Data Growth one of the biggest challenges of Big Data Analytics is the explosive rate of data growth. According to IDC, the amount of data in the world servers is roughly doubling every two years. By 2020, those servers will likely hold 44 zettabytes of digital information. To put that in perspective, that is enough data to fill a stack of iPads stretching from the earth to the moon 6.6 times. Big Data Analytics solutions must be able to perform well at scale if they are going to be useful to enterprises.

3.) Unstructured Data Must of the data stored in an enterprise’s systems does not reside in structured databases. Instead, it is unstructured data, such as email messages, images, reports, audio files, videos and other types of files. This unstructured data can be very difficult to search unless you have advanced artificial intelligence capabilities. Vendors are constantly updating their Big Data Analytics tools to make them better at examining and extracting insights from unstructured data.

4.) Data Enterprise data is created by a wide variety of different applications, such as enterprise resource planning (ERP) solutions, customer relationship management (CRM) solutions, supply chain management software, ecommerce solutions, office productivity programs, etc. Integrating the data from all these different sources is one of the most difficult challenges in any Big Data Analytics project.

5.) Cultural Challenges although it is becoming common place, it has not infiltrated the corporate culture everywhere yet. In the New Vantage Partners Survey, 52.5 percent of executives said that organizational hurdles like lack of alignment, internal resistance or lack of coherent strategy were preventing them from using big data as widely as they would have liked.

Open Source Big Data Analytics Tools

These are some open source Big Data Analytics tools. Here is Follows –

  1. Tableau Public

It is one of the intuitive and simple to use tool which democratizes visualization. This data analytics tool communicates insights through data visualization. Although there are great alternatives to data visualization, Tableau Public’s million row limit acts as a great playground for personal use.

  1. OpenRefine

Formerly known as Google Refine, this is a data cleaning software that helps you get everything ready for analysis. It operates on a row of data which have cells under columns, which is very similar to relational database tables. This is one of the Data Analytics tools for business.

  1. KNIME

It is one of the Best Data Analytics tools that allow you to manipulate, analyze, and modeling data in an intuitive way via visual programming. KNIME is used to integrate various components for data mining and machine learning via its modular data pipelining concept.

  1. Rapid miner

It is also one of the top data analytics tools is Rapid Miner provides machine learning procedures and data mining including data visualization, processing, statistical modeling, deployment, evaluation, and predictive analytics.

Rapid Miner is also considered top in the list of Big Data Analytics tools. This data analytics software is written in the Java programming language.

  1. NodeXL

It is a visualization and analysis software of relationships and networks. NodeXL provides exact calculations. It is a free and open-source network analysis and visualization software. NodeXL is one of the best statistical tools for data analysis which includes advanced network metrics, access to social media network data importers, and automation.

So , it was all about What is Big Data Analytics ?  , We hope you understand everything well. If you have still any questions or doubts related with What is Big Data Analytics ? then you can freely ask us in the comment box below.

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