What Actually Is Data Science? And Why Is It Important?

Data science is mostly about asking your data the right questions.

Getting insights from Big Data

Data science is a multidisciplinary approach that aims to obtain information and insight from large swathes of data. Back in 2009, in a McKinsey article, Google's chief economist Val Harian said:

"The ability to take data - to be able to understand it, to process it, to extract value from it, to visualise it, to communicate it - that's going to be a hugely important skill in the next decades."

He was right, of course. And in between the dashes is, essentially, a definition of data science.

Why do we need data science?


The reason why data science has become so important is first, of course, because of Big Data, i.e. the advent of extremely large amounts of digital data. No human could go through the data generated even by a small business to gather insights from it. That is also because data tends to be less and less structured: instead of coming from records and databases, it is made up of everything from emails to social media posts or mobile data, as well as formats that are not easily searchable, like audio or video files. By the end of this year, over 80% of the data will be unstructured. And the thing is, every organisation needs to be able to make sense of their data, in order to generate more business value, or to streamline some processes, or to understand what it is the users really need.

What does data science do?


This is where data scientists come into the picture. Data scientists are highly-skilled and trained individuals who work on the five stages of the data science lifecycle: they find ways to capture, maintain, process and analyse it, and to communicate their findings. An important part of data science is asking the right questions, i.e. knowing what you're looking for and what is the best approach to find it. When done well, data science can provide invaluable strategic guidance to stakeholders. It can also lay the groundwork for the development of data products, such as recommendation engines (think Netflix), spam filters or even machine learning algorithms for self-driving cars. The world is data science's oyster, really.

 

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