Big Data
Big Data are extremely large, fast-growing and complex data sets that traditional processing and analysis methods cannot handle or analyse efficiently.
Big Data refers to enormous volumes of structured, semi-structured and unstructured data arising from a range of sources — social media, sensors, mobile devices, transactions or internet searches. They are characterised by the so-called 5 Vs:
- volume,
- velocity,
- variety,
- veracity
- and value.
The goal is to collect, store, process and analyse this data so that it delivers valuable insight to companies or organisations and supports strategic decision-making.
Advantages
- More accurate decisions: data analysis reveals trends and customer behaviour and makes it possible to build predictive models.
- Better marketing targeting: companies can personalise offers and communication at the level of the individual.
- Process optimisation: data helps uncover weak spots and increase efficiency in areas from manufacturing to logistics.
- Innovation: it allows new products or services to be created based on user behaviour and reveals new market opportunities.
Disadvantages
- High costs: for technology, software and infrastructure (cloud storage, powerful servers).
- Privacy and security problems: working with huge volumes of sensitive data brings increased security risk.
- Demand for expertise: it takes qualified data analysts, data scientists and specialists, who are in short supply on the market.
- Risk of misinterpreting the data: shallow analysis, or analysis without the necessary business context, can lead to bad decisions.
Practical example
E-shops use Big Data for recommendation systems. Amazon, for example, recommends further products based on previous purchases, browsing and customer behaviour. Netflix similarly analyses what a user watches and offers personalised film and series suggestions. Transport companies, in turn, analyse smart-card data to optimise public transport routes and intervals.