Currently more and more people talk about Big Data, Data Science and Data Analytics. But what exactly do all these terms mean and what relevance do they have for companies? This article should bring clarity into the jungle of terms and clarify the basics of the individual disciplines.
Big Data describes, as the term already says, large and complex amounts of data, which are characterized by their fast-paced nature and little pronounced structuring. Big Data is often used when it comes to new technologies. Traditional manual analysis methods can hardly cope with the large amount of data and therefore require data analytics and data science methods.
Data Analytics deals with the analysis of data. It should help to convey patterns and conclusions within the existing data and information in order to optimize processes or strategies and gain new insights.
Data Analytics is used especially when companies want to optimize their services or want to optimize or refute decisions.
Data science is much more complex than data analytics. Data Scientists use mathematics, programming and statistics to read correlations from data and to optimally read data sets. They work with structured and unstructured data. Even though the two terms are very similar, they are different in terms of the complexity of the tasks and the processing possibilities of the data sets.
Particularly in recent years, the term data science has attracted a great deal of attention and is considered one of the most important professions of the future.
The combination with Big Data occurs in both areas when they have to analyse particularly large data sets. This Big Data Analytics is also used for the analysis of unstructured information such as images or videos, which goes beyond mathematical functions and often requires the use of supplementary instruments such as artificial intelligence.