Technological Requirements of Big Data

Technological Requirements of Big Data

We've already discussed how big data has continues to grow and develop including what is likely to happen in the future. But it is important to understand the this growth is only possible due to the technological advancement in all aspects of hardware computing systems.

In my opinion the most important of these is storage. Big data requires a huge amount of storage to keep up with current demand which has lead to a significant rise of the amount of dedicated data centres recently. These data centres are vast facilities dedicated to the storage and management of huge, complex datasets. Large data centres can easily store up to hundreds of petabytes of data, which of course require thousands of high capacity hard drives as well as powerful processors capable of handling this much data. As technology improves you would naturally expect storage prices to continue decreasing as we have seen in the past with hard drives and SSDs becoming more affordable. However, with the recent surge in AI and big data this has not been the case. Prices of storage and memory have more than doubled over the last year as supply has not been able to meet the ever increasing demand of big data.

When it comes to processing power, things aren't as bad. Faster and more powerful processors are constantly being developed and improved to keep up with demand. AI algorithms also continue to be developed in line with big data improving efficiency. 

For companies using big data, data integration is important. This determines how data from different locations is compiled into a single source. From this the correct analytics should be used to get the most out of the data to improve aspects of the business. 

Future Applications of Big Data

Future Applications of Big Data

In todays world big data is already impacting practically all aspects of daily life from personal things like the type of content we consume on social media, scientific/health aspects like our health being monitored and in businesses with personalised adverts and marketing. In many ways we have already entered the future with big data since we started using AI with machine learning to optimise how we collect and analyse big data. This is only going to grow in the future as we continue to improve these  algorithms. One of the main areas of this are predictive analytics which will be used more and more in many different areas like healthcare to predict potential diseases and medical conditions before they occur, and in business to predict future market trends and consumer choices. In society surveillance utilising big data is expected to increase with the help of AI to identify patterns which could indicate a high likelihood of potential crimes. All of these developments involving big data are going to keep growing as we continue to collect more and more data and improve AI. 

Contemporary Applications of Big Data in Society

Contemporary Applications of Big Data in Society

Big data affects many different aspects of society in ways. This includes planning and infrastructure, policing/security, and education. Big sets of data are used along with AI and analytics models to help improve overall quality of life and better society as a whole. 

Key applications of big data being used in society:

Transport - Big data can be used to to create better public transport systems based on people's commuting habits, as well as analysing road traffic flow to lower congestion in busy areas.

Environmental and Energy Sustainability - Managing resource efficiency by analysing areas of higher energy consumption to know where to setup power grids. Predicts changes in weather that can be used to harvest energy using solar panels and wind turbines. 

Security/Crime prevention - Predictive analytics are used to identify individuals who may be a high risk of committing a crime, whether that be due to past offences or recent behaviours and activity. Modern security systems capture vast amounts of images/video evidence aiding investigations. Publicly gathered information is also collected and analysed by big data algorithms. 

Education - Large amounts of student data is gathered and analysed using big data algorithms to help identify patterns in learning to find what tools work best and why some students might be struggling or need help. 

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. 

Big Data vs Traditional Data Analysis

Traditional Data Analysis focuses on using spreadsheets and databases to analyse small, easy to manage structured data sets. Programs like SQL and spreadsheet and commonly used in traditional data analysis to provide statistics and identify trends in the data.

Big Data on the other hand analyses vast amounts of unstructured and semi-structured data by utilising powerful AI and machine learning tools to process far more data than traditional data analysis methods.

The advantage of traditional data analysis over other analysis methods is its simplicity. You know what your getting, it is reliable, easy to use and integrate and provides accurate, useful information and statistics that help businesses operate daily.

However compared to big data analysis it has a few limitations. It's only able to sort through limited small structured data sets whereas big data analysis can handle large amounts of unstructured data. It is only able to process data at a relatively slow speed so it struggles with larger volumes of data. And it's unable to reveal the complex patterns that Big data analysis can.

Despite its limitations traditional data analysis is still the most common type of data analysis used by businesses to find simple patterns, it is also very reliable and doesn't require the same level of AI hardware that big data analysis does.

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