Big Data and Data Science are real buzzwords at the present time. However, what are the differences between both terms and how are the fields related to each other? Can they even be considered as competitors?

Terms & Definitions

Big Data refers to large amounts of data from areas such as the internet, mobile telephony, the financial industry, the energy sector, healthcare etc… Big Data can also extract figure sets from sources such as intelligent agents, social media, smart metering systems, vehicles etc. which are stored, processed and evaluated by using special solutions [1].

Data Science is about to generate knowledge from data in order to optimize corporate management or support decision-making. Methods and knowledge from various fields such as mathematics, statistics, stochastics, computer science and industry know-how can be therefore used here [2].

Against each other or with each other?

Unlike other trends, these two areas are not in competition but empower, or enable each other. New big data technologies have made it possible to analyze large amounts of data with data science tools.

Some examples of this are:

• IOT: Only through Big Data, real-time systems can handle the flood of data and can both manage and prepare them for analysis.
• ML: Analyses based on artificial intelligence require a lot of computing power, which is also only possible with modern Big Data cloud architectures.
• Self Service BI: Hundreds of users building and sharing their own reports? In this case, a solid infrastructure is crucial here, to ensure a stable environment when working with large amounts of data.

So you can see that Big Data makes many of the Data Science trends possible. Of course, data analytics can also take place without modern, cloud-based Big Data technologies, but due to the rapidly growing data volumes, these are increasingly becoming a prerequisite. Once the solid architecture is implemented, there are no limits for the data scientist and analyst. They can then run their analyses without technical limitations and mostly on their own.

#data-science #data-analysis #big-data