Contemporary Applications of Big Data in Science

Applications of Big Data in Science

The main application of big data in science is its use in healthcare. Big data is being used more and more across the healthcare industry with increasing amounts of data being collected in relation to healthcare. Whether it be for the hospital staff managing medical records, doctors evaluating a patient, a pharmaceutical company developing new medicines or helping an individual stay healthy, big data always has an important role to play. 

Key Applications of Big Data in Healthcare include:

Prevention/Detection - Big data and analytics are used along with specialised IoT monitoring devices to help spot when a person has or is likely to get a certain disease or medical condition, drastically improving their outlook.

Personalisation - Using personal data on an individual like their genetics, lifestyle and physical traits to give doctors more informed decision making.

Improved Diagnosis - Big data can be used along with highly trained AI algorithms to help spot anything that could have been missed by doctors in a scan or x-ray, leading to a more informed diagnosis. 

Drug Research - Drug companies utilise big data when analysing large data sets during clinical trial periods of testing new drugs. 

Contemporary Applications of Big Data in businesses

Big data applications for businesses

Big data is one of the most important parts of a business. It provides them with the opportunity to analyse vast data sets which can provide key statistics, patterns and trends that can be used to improve almost all aspects of the business. 

Key Applications Include: 

Marketing/Advertising - Big data is used to find ideal pricing and market niches, as well as targeted personalised ads base on customer behaviours and interests. 

Logistics - optimises transport routes as well as inventory management by using predictive analysis based on large data sets.

Innovation -  data driven product development by targeting consumer needs and optimising production costs.

Intelligence - spotting market trends early and gaining and advantage over competitors.

 

Big Data Analytics

Big Data Analytics refers to the processes, tools and methods used when analysing large complex data sets. This is very important because we know that companies collect more data than ever before partly due the increased use of social media and IoT devices. But without adequate analysis, this data doesn't hold the same value. The cost of not properly utilising this data is that a business can fail to keep up with competitors that are making the most out of big data and using it to analyse consumer behaviour and market trends. 

During the data analysis process data all different types of data are consolidated in one place, usually a large data centre. From there it is then sorted through, filtered and verified. Then AI, machine learning and data processing tools such as Adobe Spark, an open-source engine for data analysis are used to analyse the data to find any patterns or information that could be useful.

There are four main types of data analytics that each provide unique insight and benefits. 

Descriptive - which focuses on looking at historical data to form concrete statistical information.

Diagnostic - looks at why something has happened or is the way it is, based on past trends and patterns in data.

Predictive - aims to provide insight into what could happen in the future based on statistics and AI predictions. 

Prescriptive - uses evidence from the other three to determine an ideal approach for the future.