Structured data examples in big data?

What is Big Data?

Big data means a large amount of data. Big data typically refers to data storage amount in excess of one terabyte (TB) however it is not possible to specifically define big data. Big data is complex and therefore it is difficult to store, collect, maintain, analyse and visualise. Companies do processing on Structured data in big data to extract useful information from it and eventually use it for making money and thereafter to use it for decision making.

I tell you how? From big data, companies find out the interest of people and repeatedly show them the things of their interest. For example, you made an online search for microwave oven even though you haven’t bought it but still, you encounter the same model of microwave oven you searched for on every website you visit.

How is this happening? Well, there is no miracle or coincidence happening here, the companies do it intentionally by using techniques of big data such as data mining, data visualisation, etc.

Companies use your data to show you things of your interest and that is how big data is used and processed. Using big data companies also do decision making by accumulating all its data at the end of the year and analyse what has benefited the company and decide which actions company should take and that?s how it helps in decision making. There are three types of big data, namely:

What is Structured Data?

Structured data is any data which has a high level of organisation with definite length and format, data model or ?schema? (structural representation of what is in the database) and this can be easily retrieved. Structured data is stored in databases, in excel sheet, or in tabular form in CSV file (Comma Separated Values), having a fixed number of rows and columns that clearly define its attributes.

Structured data is highly organised and perspicuous for a machine language. Structured data is straightforward and is simple to handle and easy to store, format, execute, process, query and analyse.

Structured data can be easily accessed and used by a computer program or by any person (user). Structured data is a Metadata that is hidden to the user by readable by search engines.

Structured data is often managed by SQL (Structured Query Language) for managing, querying and analysing the data stored in RDBMS (Relational Database Management System) and Spreadsheets. Structured data contributes around 20 per cent to big data.

Example to understand better, as under the attribute name all names are mentioned and under the attribute email id all email ids are mentioned, it is kind of an employee table. In structured data, there is no need to put much effort as things are presented in a well-defined manner.

Structured Data examples

The examples of structured data are spreadsheets, the traditional Relational Database Management System (RDBMS) with well-defined and organised rows and columns with definite attributes such as names, dates, addresses, email ids, mobile numbers, stock information, marks obtained, etc.

We are used to dealing with structured data and we frequently encounter structured data in our practice and use. Structured data is very reliant and the evolution of technology new sourced of structured data is being produced. The sources of structured data are:

  1. Machine-Generated data ? Machine-generated data refers to the data that is created by machine without involving even an iota of human intervention.
  2. Human-Generated data ? Human-generated data is produced by humans in interaction with computers.

Examples of Structured Machine-Generated data include the following:

Financial data ? Many financial systems are based on a set of predefined rules that has automated the processes. A good example of financial data can be Stock market trading data as it contains structured data such as the name of the company, company symbol, and dollar value. Here, some of the data is human-generated and some of it is machine-generated.

  • Point of sale data ? When you go to a shopping complex to buy a product and go to the payment counter, where the cashier scans the bar code of the product you are purchasing, all the details regarding the product is generated on the computer screen.
  • Sensor data ? Sensor data include smart meters, ID tags of radio-frequency, medical equipment. Companies use sensory data for inventory (stock) control and for the management of supply chain.
  • Web log data ? The servers, networks, and so on, they capture all the data about our activity and repeatedly show the data of our interest to us.
  • Such data amounts to huge volumes of data and from the huge volumes of data useful data is extracted to deal with product marketing and selling, to predict security breaches, to predict the winner of political elections, etc.

Examples of Structured Human-Generated data include the following:

  • Click-Stream data ? Whenever we search for any information on search engines, a number of links related to our search appear in front of us on the screen, and as we click on the links data is generated every time and it generates the data at a good rate of speed.
  • This type of data is used to analyse the buying patterns and the behaviour of the customers.
  • Input data ? Input data is any data that a person (user) feed or input in a computer, such as, name, address, age, mobile number, income, number of family members, survey response forms and so on.
  • This kind of input data is very useful for companies to understand the behaviour of the customers, the change in the markets and the change in needs and preferences of the customers and for various other needs.
  • Gaming data ? While playing a game you can record your each and every move and later on you can carefully analyse your recorded moves and you can eventually improve your game.
  • This gaming data can prove to be useful in understanding how the users move through in a gaming portfolio.

The structured data both human-generated and machine-generated, are very powerful, reliable and useful and structured data play a key role in generating huge profits for the companies. Structured data hold importance as it is very user friendly, easy to deal with and most importantly it is human readable and therefore companies rely on structured data.?