Data Science versus Big Data: Key difference and similarities

For outsiders, it may be difficult to separate the terms 'data science' and 'big data', as they are seemingly pushing towards the same goals and certainly share a number of similarities.

However, there are also quite a few places in which these two diverge from one another, so here is a look at the distinctions and shared facets of each to bring you up to speed.

Data science as a professional field

First of all, it is worth establishing that data science is both an academic pursuit and a professional practice which melds together a number of different disciplines, including statistical analysis and mathematics.

With the help of an expert data science consultant, organizations of all sizes can drill down into the information available to them, extrapolate meaning from it and use this to assist in everything from product development and marketing to medical research and more.

So in essence, data science is the thing which formalizes the processes and tools used to deal with vast data sets. As a result there are professional data scientists who are specialists in this arena, researching new processes and applying their expertise to the various challenges they face.

Big data as a concept

Big data is an umbrella concept, and one which attracts data scientists and a cavalcade of other industry experts to it, as well as being a specialism in its own right, largely as a result of how rapidly its popularity has expanded in the last decade.

What makes big data particularly important is that it typically refers to huge pools of information harvested from a multitude of entirely distinct sources, as well as the steps taken to store, catalogue, collate and scrutinize this data in order to find patterns. From unstructured data drawn together via social media platforms and biometric sensors, to structure data from more traditional database solutions, big data is broad in its scope.

Where the two meet

As you might have guessed, the specific skills of data science are integral to the modern big data market, and without the former it would be impossible to explore the true potential of the latter.

Big data is an all-encompassing ecosystem of information, and data science provides the tools and expertise by which the ever-growing deluge of data being produced worldwide at the moment can actually be put to work for positive gain, rather than simply being ignored or misinterpreted.

This has especial relevance in the business world, where companies can easily be overwhelmed by just how much data they have access to, and it is the role of data scientists to help them make decisions based on the insights that their analysis can bring.

Big data is not limited to the commercial world alone, as hinted at earlier, and it is also applicable to everything from law enforcement to telecoms and beyond.

So as you can see, big data and data science have their differences, but are ultimately intertwined with one another as part of the same loosely defined industry.