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The University of Southampton
Turing @ Southampton

About Data Science

The foundation to data science is the phenomenon of ‘Big Data’, representing the massive amounts of data rapidly being placed on the Web that are so large they require novel approaches to managing and organising compared to the traditional database solution. Data scientists can help to manage and process this data, producing the insights that would otherwise be lost or drowned out for organisations ranging from large corporations to government agencies.

When talking about Big Data, four "V's" are often used: volume, velocity, variety and veracity, to help descripe the key characteristics of 'big data'. These refer to the amount of data, the fact that is arriving quickly and often in real-time, it often contains different forms of data (variety), and contains a certain amount of uncertainty (veracity).

Smart Data is not necessarily 'big data'; instead it focuses in on the valuable bits of data without the noise commonly associated with big data. It therefore focuses on the data and information that makes sense, allowing actionable insights to be obtained. Often semantic technologies are adopted to allow linking with other key datasets, which therefore affords more insights to be gained from the data.

Open Data has become a popular idea within governments who wish to increase transparency by making their data available to the public. The Open Data paradigm provides the potential users with access to vast amount of information and evolves into a particular promising concept when the data are released according to Linked Data principles, as Linked Open Data.

Data scientists use their skills to produce analysis and visualisations on these large amounts of data that can tell a story to different stakeholders (digital storytelling). This opens up their findings so that they can be interpreted and understood by the many interested parties, and highlights the importance of the data scientist to an organisation. They act as a vital bridge between the complexity of the data and the policy- or decision-makers who can instigate real change with the knowledge that can be extracted from it.

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