Is the title a bit of hype dreamed up to coincide with ‘Big Data’ or is there a real need for a ‘Scientist’?
If you're reading this then you likely work with data in your daily life, whether through a marketing or IT based function. So you may be familiar with the question I have posed above. Is the title a bit of hype dreamed up to coincide with ‘Big Data’ or is there a real need for a ‘Scientist’ when working with data in the world of the IoT (Internet of Things)? When this question was posed at the recent DataIQ conference, I felt compelled to sit and listen to everyone's views on this topic. And they were certainly unique.
When polled as to whether their business had a Data Scientist, around 30% of the data-focused audience said that their organisation did employ one. As the panel discussion got deeper into the subject it became clear that the complexity and types of data available within the bucket that is ‘Big Data' needs a more scientific approach to interpret and drive insights across an organisation.
Consequently, Big Data needs Data Scientists. Looking at business problems and more importantly innovation, Big Data is the raw ingredient, APIs are often the glue, and the Data Scientist is the key to drive a team to apply scientific methodology to validate the hypotheses. This is most definitely a different approach to the standard business intelligence team. The landscape has changed and although statisticians are still needed, technologies demand that they are also developers too.
It seems that the true value of a Data Scientist is to support business decisions, which a Business Intelligence team has always done, but Big Data has changed the game. In the Data Scientist’s team there is the need for a data engineer to consolidate and process data, then the analysts whom are able to review and find patterns in the data for the scientists to review.
However, part of the paradigm shift is that the Data Scientist needs to have broader skills than an academic qualification provides. It has been said that a true Data Scientist is like a unicorn (rather hard to come by). This is a classic problem in how to make sense of Big Data for the wider business; scientists need to translate their findings into a compelling story and define what success looks like. Equally, business leaders need to be more than numerate, if they are to innovate in this fast moving Big Data landscape.
In a B2B world Big Data in its unstructured form often needs to be enhanced or linked to a business entity. For more information on how the D&B D-U-N-S Number can help, click here.