big data analytics
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What is Big data Analytics?

Big data is also data but it is large in size. It is the combo of structured, semi-structured and unstructured figures from distinct sources. Organisations also use these stats and mines out the useful information from that and use in their predictive modelling, ML (machine learning) projects and other analytics application. It contains the immense variety which arrives in rising volumes and higher velocity. It is bigger, intricate documents sets from new data sources. Traditional stats processing software can’t manage them. 

In organisations, the systems which manage and processes big data become generic components of data management architectures. The three important Vs of Big Data are: The first one is volume. The extensive volume of data. The second v is variety. The large variety of data types stored in a big data system. And the Final v is the velocity at which it generates process and collects the data. There are other Vs also which has been added to distinct narrations of big data like value, variability and veracity. Big data do not equal to a particular volume of data rather it captures stats in petabytes, exabytes, zettabytes and terabytes from time to time.

Organisations also use big data to sort out the issues. For example, it helps the analyst and business users to take immediate decisions which are not possible before this technology.

Examples of Big Data

A jet engine can generate more than 9 terabytes of data after 30 minutes of flying time. As many jets fly per day, the generation of stats reaches up to petabytes.

Social Media Listening: 

According to some facts and figures, social media sites like Facebook generates more than 500 terabytes of stats every day. It generates facts and figures in terms of message exchanges, comments and photo and video uploads etc.

The stock exchange like the New York Stock Exchange generates more than one terabyte of trade data every day.

Marketing scrutiny: It consists of the information that has been used for the promotion of new products and services.

Consumer thoughts and pride analysis:

Customer reviews play a vital role in running any business. These reviews reveal the feeling of the customers towards the company or the brand. For example, if any company receives too many negative reviews about their products or services then they have to sort out this problem. They may conduct a survey to find how they can improve customer services etc.

Comparative Scrutiny:

It consists of the observation of consumer demeanour metrics and real-time consumer commitment so that we can compare products and services with those of its rivalry.

Why Big Data is important?

It provides many benefits to organisations. It helps them to improve functioning, provides better customer support, creating a marketing campaign based on the customer desires which consequently increases their profits. Companies that make effective use of it can make better and faster decisions.

For example, it can provide relevant information to the companies so that they can analyse it. After analysis, they can perform modifications in their strategies like they can refine their marketing methods and campaigns to increase revenue and fulfils customer’s desire.

Also, to make use of big data enable the enterprises to improve the quality of products. By using current and historical figures they can examine the taste of the customer. Consequently which enables the business to upgrade and reform their marketing strategies and become more accountable to consumer’s requirements.

Medical Professionals researchers also use big data to recognize the factors of disease risk. Doctors also use it to diagnose a malady in each patient. Social media, web and EHRs( electronic health records) provides government authorities and healthcare departments with latest updates on contagious disease menace or threats.

It also helps in the power industry. Companies like gas and oil to find out the drilling locations and monitor the pipeline activities. Financial Services companies use it for peril management and real-time scrutiny of market stats. Transportation departments depend on the big data to govern the supply chains and optimize distribution paths. The government also uses this for delinquency prevention, steps to make smart city etc.

How Big data works?

It gives us a new vision and bringing us new opportunities and business portraits. Five vital steps include structured, unstructured and semi-structured data.

Setting Up Strategy

The Big data strategy enables us to supervise and reform the ways we maintain, share, and store and use the info outside and within the enterprise. This strategy sets the platform for business achievement amid the sufficiency of statistics. When we create a strategy we need to examine current and future business goals.

Identify the Sources of Big Data
  1. Social media websites like Facebook, YouTube, Instagram etc. comprises of the huge amount of figures in the form of text, images, videos etc. This stats is helpful for sales, marketing etc. But this data is in the unstructured or semi-structured form and it brings a challenge for utilization and scrutiny of figures.
  1. Internet of things (IoT) provides streaming data. Organisations can use it and decides which records they have to keep or not to keep and which records needs analysis.
  1. It is also available from other sources like customers, suppliers and cloud sources etc.
Maintains and handling the Big data

Currently, manufacturers are making systems which provide good speed and power. Consequently, it enables to access huge amount and types of big data. Organisations need to adopt best practices for files integration, preparing reports for analytics and ensures the quality of info along with the files access. Currently, we are using the traditional method of storing data in a warehouse. But there are also some distinct options like Hadoop, cloud solutions etc to store and handle the big data.

Big data Analysis

With the development of latest technologies like grid computing helps the organisations to analyse all their big data. Another thing we need to keep in mind that which content is important and needs analysis. Through analytics, organisations achieve values and perception.

Better Decision Making

Firstly, setting up a good strategy. Secondly, identifying the sources of info. Lastly, performing deeper and richer analysis and storing and managing the data. These crucial steps are necessary for every organisation to make a better decision. To stay emulative in dynamic environment enterprises need to gain the full value of facts and figures. The organisations which take better decisions functions better and are more predictable and profitable as well.

3Vs

Volume

Every organisation maintains the database and the amount of information in the database also matters and we have to do the processing of a great volume of unstructured data. This type of content has an unfamiliar value such as Mobile applications, sensor-enabled types of equipment etc. For some companies, this might be hundreds of petabytes or tens of terabytes of data.

Variety

Variety means different types of material are available for example, structured, unstructured or semi-structured. The conventional data types were structured and suitable for the relational database. Files such as text, audio, video, images etc. are unstructured and semi-structured data types. We have to preprocess these figures so that we can obtain useful information and support metadata as well.

Velocity

Velocity enables us to receive the info at rapid rates. The supreme velocity of data streams directly into memory. Most of the internet-enabled device’s functions in real-time. These devices require real-time assessment and action from time to time.

The Types of Big Data

There are three types of big data:

  • Structured
  • Semi-Structured
  • Unstructured

Structured

The content which we stores, access and process in a particular format are known as structured data. We know that technology has given us much more than we require and it helps us by developing techniques for working with such kind of info (a format which we already know) and extracting values out of it. Nowadays we are facing many issues as data is growing day by day. These figures are in petabytes, zettabytes etc.

For Example:

Employee IdEmployee NameGenderDepartment  Salary   
001DushyantMaleSales  3000  
 002  DusshashanMaleAccounts  4000  
 003Duryodhan MaleFinance  5000  

Unstructured

The figure which is not in a particular or appropriate form is known as unstructured data. It’s become a challenging task to extract useful information from the huge size of unstructured data. For example, a heterogeneous data source contains the combo of text, images and video files etc. Currently, organisations have a huge amount of content in their databases but unfortunately, they are not able to extract useful information from it. This is because they contain information in the raw form or unstructured format.

For example:

Social platform content: This type of info generates from social media platforms like youtube, Facebook, LinkedIn, twitter etc.

Mobile content: It includes info such as messages and location information.

Website content: This usually comes from that website which delivers facts in an unclear format like youtube, Instagram etc.

Technical content: It consists such as high energy physics and atmospheric data etc.

Sonar data: It consists such as meteorological department, vehicular etc.

Semi-Structured

It contains both the structure of data i.e structure and unstructured data. Data cannot stores in forms of row and columns as in databases. It contains tags and elements. Homogenous entities are placed together. Entities in a similar group don’t need to have similar properties. It is difficult to perform the automation and maintain the info as it does not contain plentiful metadata. Systems programs don’t use them readily as it does not has a particular format.

Advantages and Disadvantages

  • Everything has two aspects positive and negative. With the increase in the volume of information, it also presents the merits and demerits of big data. 
  • Organisations having more information about customer’s tastes and feelings help them to continuously improve their services and marketing strategies to create the highest level of satisfaction. If Companies gathers a huge amount of info than they can conduct good and deeper analysis.
  • It provides better analysis which is a good thing but it can also create overburden and noise. Organisations that maintain the huge quantity of info also need to determine which stats symbolizes indications compared to noise.
  • The presentation and type of figure require peculiar management before we can act upon it and can easily arrange and store structured info which contains numeric values. To make the unstructured data (for example, images, videos, text documents) useful, we have to apply refined methods.

 Use Cases

  • Improves consumers unification

If the company has overall data (Structured, Unstructured and Semi-Structured) then they can check out the customer’s behaviour and feelings so they can improve marketing campaigns. Sources of this info can be from social media, websites and mobile devices etc

  • Find out and alleviate frauds

Security is a crucial thing. We have to prepare ourselves against hackers. This is because it’s not about just a few hackers. We are against the whole expert team. Security adherence is developing gradually. It also helps to find out the info which points out fraud and collects a massive volume of information to make administrative reports much faster.

  • Supply chain competence

Big data collection and analysis also helps to figure out how goods are reaching their destinations. We can discover inabilities and it helps to save time and costs. For example, sensors can help to trace the vital information from godowns to destinations.

  • Warehouses Unload

This is the clear and the cost emphatic way for companies to use big data instruments to remove the load from the warehouse. But the saddest thing is that warehouse technology is very costly to buy and transact. As business administrators demand complete reports from business intelligence teams. But the warehouse solutions not able to provide good performance.

To sort out this problem, many enterprises start using big data solution like Hadoop to change their warehouses. These solutions provide much better performance which consequently reduces licenses fees and other costs as well.