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Big Data refers to large amounts of data that can no longer be processed with conventional software or hardware, and for which processing and analysis is performed for a specific purpose. In contrast to Big Data, Small Data refers to data that are accessible to humans due to their volume and format.

We encounter these large amounts of data in everyday situations, for example when surfing social media or searching on Google. To better define Big Data, analyst Doug Lane designed the 3-V model, which states that Big Data is characterised by volume, speed and diversity.

Large amounts of data can be used, among other things, to improve the strategic orientation of companies, for Deep-Learning-Systems, to fight crime and terrorism, for the scientific investigation of natural phenomena (e.g. earthquakes and climate change), but also for illegal evaluations that can lead to blackmail or fraud. The decisive factor is not so much the large volumes of data itself, but what happens to it.

Companies can use Big Data to increase their business success. Among other things, Big Data Analytics enables companies to make better business decisions and assess risks with greater accuracy. In addition, the efficiency of business processes can be increased when data is analysed, evaluated and linked together. Big Data helps companies in research and development to make predictions about trends, product characteristics, etc. Finally, the knowledge gained from Big Data can also be used to offer personalised customer service.

For a successful Big Data analysis, an appropriate Big Data strategy, a suitable corporate culture, personnel with the necessary know-how, efficient technology and, last but not least, a suitable data protection strategy are required. But analysing and processing big data not only offers opportunities and chances, but also poses challenges and risks.

A major challenge for companies is to ensure data security and to comply with the General Data Protection Regulation. In addition, it is often difficult for companies to find and retain suitable professionals who can handle the complex Big Data Technology. Big data projects are also associated with high costs and data quality is often poor. Finally, the right conclusions must be drawn from the results of data analysis and the right decisions must be made.

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