Why Is Big Data So Important?

Big Data is the term that explains the large volume of both stable and unstable data that inundates the business on a daily basis. In order to qualify as such, the data involved must exhibit categories known as the four V’s: By Matthew Meehan

VOLUME - The amount of created data is considered vast in relation to traditional data VARIETY - Data comes from all different types of sources and is therefore also created by machines and processes as well as people VELOCITY - Data gets produced extremely fast, with this process continuing even as we sleep VERACITY - Big Data is sourced from many different places and therefore the quality and veracity of such data must be tested.

This concept has existed for many years and it is understood by most organisations that capturing all data leads to significant potential value for the company. Even before Big Data became a concept in the 1950s, businesses were using basic analytics like spreadsheets and calculations to monitor developing trends and insights before developing the initial Big Data concept. While an increase in speed and efficiency was created by this concept, the time taken did not allow for anything more than future predictions. In comparison to now, trends are now instantly identifiable, allowing immediate strategic development that facilitates faster workstreams, staying agile to your environment and being constantly competitive. However, the main factor in utilising such data is also reliant on understanding why Big Data is actually so important for businesses in the first place, especially considering it is not actually the amount that matters, but instead what the organisations do with it. The importance of such can mainly be understood by the development of three core categories that have been decided over time to give the greatest overview of the inherent benefits of Big Data.

Cost Reduction

The reduction of cost is achieved by the new technological developments of Big Data, such as cloudbased analytics and the reduction in necessary hardware, meaning that there are significant cost advantages when it comes to both the ability and expense involved in storing large quantities of data. The ability to now take information into a more digital storage mechanism has allowed for large improvements into data driven processes such as quality standardisation and general testing. This is due to their needs for constant access to numerous complex sets of data that are especially important in industries where mistakes are critical, such as pharmacological investigation, technology and national defence. Big data is able to formulate in-depth insight that provides detailed feedback and the storage of identified issues that inform the process of assessing variables. This allows much quicker, clearer and well-informed decisions for all affected industries.

Improved Decision Making

Due to the greatly advanced speeds of the data mining processes and the memorisation of analytical data, this allows new sources of information to be speedily investigated, so that all businesses immediately understand both the state of competitors and the overall condition of their industries. This equips them with the tools to make constantly evolving decisions- due to the speed in which these companies can now learn and internalise this information. This also allows for much further future projection, as the accuracy and consistency allows businesses to create their own agile framework which is able to handle the involved risks, while constantly updating and re-evaluating responsible memory banks in order to effectively influence every necessary decision.

New Products and Services

With research methods such as polls, surveys and intelligent algorithms calculating and predicting future human behaviour, the data therefore has the ability to gauge all of the customers’ wants, needs and desires while combining these with resulting satisfaction levels that inform the analytics so that the customer receives exactly the right product. Thanks to Big Data, more companies are able to innovate and develop new products or variations on a consistent basis. This kind of data can also be informed by later situations such as product launches or long-term customer feedback to further the positive exchange, while also realising how the balance of demand then affects their own purchasing process so that they may save any wasted spend or attempted purchase misinformation from wholesalers. As a result, the focus then shifts through the data onto customer-centric marketing that allows accurate purchase prediction, where customer investment (that may rise due to satisfaction) will therefore require a more personalised and data-driven specialisation. While this is hard to predict, it can instead be informed through these subsequent buying behaviours. The issue behind this that contributes to the necessity of big data is the modern society in which we live and operate. Such developing needs in a technologically driven and advancing world means that everything from transport to personalised advertising, politics to weather and even health monitorisation of ourselves and the world around us requires constant data. This is even the case with oceanographical and geographical coverage for agencies such as NASA, which requires an unimaginable amount of constantly changing data processes. The issues involved with this stem from this are the miniscule details that are needed to create the individual DIY architecture of a specialised database just to handle simple processing, and to achieve this through a cloud storage access that is affordable to the individual organisations. There are also great challenges due to the mix of data that is needed by analysts and computer scientists, as the mix of platforms and data stores used can often make compatibility between them difficult. Therefore, the need for greater data catalogues occur which require subsequent governance and quality assurance. This issue shows one of the most crucial factors as to why big data is so important now lies in its own design: ethical practices and regulations. Collection practices and regulations must be clear and abided by in order to ensure that Big Data actually achieves what it needs to without impeding on personal freedoms. Increased usage, alongside few restrictions, leads to higher misuse, and the loss or theft of personal or sensitive consumer data becomes a significant possibility. This led to the creation of GDPR which limits and regulates Big Data. However, in order to ensure success and restriction are not opposing forces, there must be a balance struck that creates an environment that prevents the loss of efficiency and advancement. Much of this success is dependent on the human ability to handle small data, while making sure computers can effectively handle the data to ensure its ultimate success through the implementation of infrastructure that can allow such vital information as Big Data to continue to revolutionise our lives.