It is quite likely you are wondering ‘what on earth’ am I on - for cooking up such a title when ‘data science’ is unequivocally one of the sexiest roles of the future (well, of the current future) as well as one of the most desired job functions. All those entering into the field of data, aspire ultimately towards the titles of Chief Data Scientist, Chief Analytics Officer, or any title that connotes some semblance of seniority with either the word ‘analytics’ or ‘data’ as a suffix or prefix to it (please see my new linked-in title). The title stands, notwithstanding the growing trend in companies pouring money in to hire anyone who professes to have a background in data. Is the ‘data scientist’ really the ‘unsung hero’, when all we see these days is the insourcing of analytical capabilities from previously outsourced centres and media articles which demonstrate the importance of data across a wide spectrum of industries?

So, in the face of growing recognition for what and how data can be utilised to reap benefits, lots of benefits – what is it that compelled me to come up with the statement that the data scientist is the unsung hero?

Most recently, a series of discussions had with some of my comrades in data illuminated the necessity of appreciating what a data scientist truly is. A data scientist is the sum total of all of these things – a scientist, a statistician and also an end-user – they are individuals who play a function that explores the successful merger of all three, more often than not, resulting in process improvement.

Yet with all that, the data scientist is not a magician that is able to single-handedly solve all of the challenges an organisation may face. Just as ‘analytics’ is not the be all and end all, and cannot be expected to be a panacea, so our humble ‘data scientist’ cannot be expected to be Harry Potter. It is through the ability to change culture, behaviour and expectation that data science truly achieves its potential. Yet all these cannot fall on the shoulders of the data scientist alone.

Data scientists – yes, we socialise (and to share with you a few snippets of our discussions):

• In one case, a data scientist shared with me an extremely successfully enhancement of operational procedures through demand forecasting and optimization. One of the key questions asked by the business during a review were - “Why did you not think of this earlier?”

• In another setting, a data scientist who demonstrated the value of analytics in an organisation that had previously been averse to the adoption of analytics in the first place, resulted in a feedback of - “Why are we not doing analytics faster?”

• And finally, my favourite snippet, a data scientist shared that at a review of the results achieved in an area that previously had no quantifiable benchmark, their manager asked - “Can you get better accuracy than 90%?”

So yes, it is extremely important to have a data strategy and just as important to insource the capabilities so as to assure an organisation retains its knowledge and is able to operationalise the value (i.e. adapt culture and behaviour). However, it is equally important to remember that the data scientist is only a catalyst and it remains the organisation’s responsibility to want to adopt change, to ask the right questions, and to support a move forwards rather than to question the past.

Understanding the environmental constraints within which a data scientist works will help you to assemble the right band of data scientists, keep them operating at their potential (just consider how many data science teams end up doing MIS reporting), and drive them to continuously seek further improvement with the end goal of achieving greater business potential for the organisation.