What is structured data in big analytics?

What is structured data in big analytics?

What is structured data in Big Data

A large number of business organisation are spending more and more revenue on collecting and analysing Structured data in big. Before analysing and optimising those data for further use, it is more important to look at what kind of data is being processed by the organisations.

Structured data in big is the essence of every organisation. Every business organisation today, are optimising and analysing a massive amount of data to gain a competitive advantage, reduce the operation costs and optimise their products or services more efficiently.

The era of data analysis is huge and companies are seeking more and more resources to utilise them. The organisation understands the importance and data and they are also aware that all data are not of the same type and same priority. This means the data generated from social media and different apps are completely different from the data generated by operation chain.

Structured data in big

Structured data are the standard form of data which is stored in the relational database (RDBMS). Files stored under structured data re phone number, Social security number, ZIP codes etc.

It can also store strings data types like names and other characters, making it simple to search. Data associated with structured data can be human-generated or machine-generated as long as data are created within an RDBMS structure.

This format of data can be easily accessible with human-generated queries and via an algorithm using the type of data and field names such as numeric or character or alphabetic, currency or dates.

The common relational database has large application such as airline reservation system, sales transaction, operation control, ATM activity etc. Some relational database do stores and points to unstructured data such as Customer relationship management (CRM). Structured data is highly organised and easy to process, which makes analytics possible using data mining.

Structured data is stored inside a data warehouse which is highly useful for data analytics and data analysis. Structured data is organised by using high analytics solution which is in an organised manner. With the rapid rise of new data source, every organisation shift their focus on that new data source and or point-of-sale system.

Most experts believe that structured data are responsible for 20 per cent of the available information in the market. Structured data are the data which you are probably dealing within the day to day life. Structured data are usually stored in a database.

Sources of structured data in big

Structured big data is taking the business organisation to the new era. The availability of advanced technology provides new sources to structured data in time and in large volume. This sources of data can be classified into two main categories:

  • Computer or machine-generated– This includes data created by machine without human intervention.
  • Human-generated- This includes data processed by computer with human intervention.

Machine or computer-generated data include the following categories:?

  • Weblog data– when servers, applications and system interact, they store all types of data that are generated throughout the activity. Thus, this ample amount of data can be used for different purposes by a business organisation.
  • Sensor data- Sensors like a smart meter, medical devices, Global positioning system and radio frequency data are some examples of sensor data. Business organisation are interested in such types of data for inventory and supply chain operation control.
  • Point-of-sale data– This type of data is processed and stored when the cashier swipes the bar code of any product while purchasing any item from that particular shop.
  • Financial data- Today, almost every financial system are programmatic in nature. They are operated based on predefined sets and conditions. One of the best examples of financial data is stock trading data. Stock trading data consist of company name, company symbol and dollar value.? Financial data can either of machine-generated or human-generated.

Human-generated data include the following category:?

  • Input data– This can be any piece of data entered by the human as an input into a computer. These data include name, age, contact number, email address, survey response, address etc. This data is important as it is required to understand the behaviour of the customer.
  • Click-stream data– This data is generated whenever you click on a website. This data is useful to analyse the behaviour and buying pattern of the customers.
  • Gaming related data– Gaming related structured data in big capture every move you made when you play the game and record it to their database. This pattern is useful as it states the behaviour of the customer by predicting their moves.