Term
Big data
Very large, fast-growing and varied data sets that ordinary tools cannot handle, such as sensor, transaction or website behaviour data. Analysing them helps spot trends and fraud and optimise processes.
Updated
What it means
Big data are data sets so large, fast-growing or varied that ordinary tools such as spreadsheets or simple databases cannot handle them. They are often described by three Vs: volume, velocity and variety.
Sources include card transactions, factory sensors, user behaviour on websites, system logs and social networks. They are stored in distributed repositories such as a data lake and processed on powerful computing platforms.
The value of big data lies not in quantity but in what a company learns from it. Analysis uses statistical methods and machine learning to find patterns a human would not see.
How it works in practice
Banks analyse millions of transactions to detect fraud, online shops analyse customer behaviour to recommend products and manufacturers use machine data to predict breakdowns. Results are shown in business intelligence tools.
Most small and medium-sized companies do not have real big data. For them it matters more to connect and clean their own data from accounting, the CRM and the online shop and build a clear data warehouse.
Cloud data processing costs are operating expenses and can grow quickly with volume. It pays to monitor which data are actually used and delete what is not needed.
What to watch out for
A large quantity of data does not replace data quality. Wrong, duplicate or incomplete data lead to wrong conclusions, and the more data there is, the harder errors are to find.
Big data often contain personal data. The GDPR applies, including minimisation and purpose limitation, so data governance must be in place, with clarity on why and for how long each kind of data is kept.
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