You’ve more than likely heard a lot about Big Data recently. It’s one of those buzzwords that, although everybody is talking about it, nobody really knows what it is. Simply put, Big Data is the name given to the huge amounts of data that most modern organisations collect and, more to the point, the actual application and use of this data. Big Data is usually collated from various sources and can produce effective solutions to otherwise difficult problems, once analysed by advanced computer algorithms.

How Does Big Data Work In Practice?

Big Data can be extremely effective for organisations looking for constant and reliable analysis of their data to gain greater insight and use this to advise future development. It often works in the same way that a national consensus works: by analysing a small percentage of data, Big Data analysis can then draw generalised conclusions about an entire market place or customer type that enables organisations to make effective decisions. Big Data can, however, be used for far more than just the analysis of data within a business.

Big Data Analysis Outside Of Businesses

The collection and analysis of a small percentage of data from online crowdsourcing to spawn generalized yet reliable conclusions that effectively advise the next steps for development can be applied to crisis situations to greatly improve relief efforts.

The crowdsourcing of data from sources such as mobile phones, satellites and social media has become a valuable asset for crisis relief over the last half decade. First employed in 2010, Big Data analytics helped greatly improve the recovery efforts for the earthquake in Haiti. The Japanese Tsunami the following year saw a turning point in using Big Data for crisis management, as a joint US-Japanese research programme was introduced in 2012 to allow the full potential of crowdsourcing to be realised.

Utilising Big Data Analysis For The Nepalese Crisis

Using data-driven solutions for disaster management has proven highly effective in wake of the earthquake in Nepal. Data sourced by a variety of means from people within the crisis zone can be interpreted to make better sense of the devastation and chaos that has been caused. Once a clearer understanding has been formed, it immediately makes change and solutions easier to employ – effectively saving more lives.

A specific result of using data-driven solutions in disaster management includes the use of crisis maps. Collated information can produce valuable results that can be used to gauge the best response to a situation. Crisis maps formed from Big Data analysis can direct relief organisations to the areas that require the most need – removing the difficulty often associated with having to interpret such large scale destruction before deciding the best way to proceed.

The 4 Steps Of Big Data Analysis

Prevention, preparation, response and recovery are the four key factors implemented in Big Data analysis. When it comes to crisis management, it is, of course, impossible to implement prevention. This merely places greater emphasis on the remaining three steps in combating the situation to prevent the most amount of damage from occurring.

The Importance Of Big Data

The reliability of collated data is crucial to its effectiveness. Having enough data to ensure that you can form an accurate projection allows otherwise unforeseeable results to be produced. Generating quick results by using as many sources as possible allows this to be successful, by taking a snapshot of a current situation and projecting the necessary response.

The sheer capability of data-driven solutions for generating future action can be seen through its versatile methods of use. Whether analysing crisis management from accurate crowdsourcing or the inner workings of a business to advise on progression for greater revenue – the potential for change that Big Data can produce is vast.