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.