Common Misconceptions about Data Scientists
Established and experienced data scientists are often asked, “What is the role of a data scientist in an organisation?”
As a relatively new profession in what is quickly emerging as an exciting field, data scientists have naturally attracted curiosity from the Average Joe. The direct answer is that the role has evolved over time. Last decade’s data scientists were locked away in the back-office knee deep in data probably due to the limitations of technology.
However, with the proliferation of accessible technological advancements coupled with the maturing data scientist, the role ideally should have direct access to the organisation’s decision-makers. Data scientists are truly valuable in extrapolating, analysing and finding patterns in existing data using statistics and machine learning. But there is often a big gap between the expectations and reality of what data scientists can do for businesses. Some of these misconceptions that must be dispelled are as follows:
“That tech guy.” – Data scientists are not technical support. They don’t all necessarily know how to repair a laptop, fix a projector or know why the office broadband is slow. Data Science as it is commonly known now or data analytics in yesteryear is its own discipline. Data scientists should be likened to innovators, inventors or artists, constantly working towards making the present and future a better place for mankind.
“Data Scientists don’t understand the business.” – Data does not lie. So, if you put the two together – the person who deals with your data and your data is telling the truth, then what you have is a potent oracle of knowledge. Spend time listening to your data science team and you will be surprised at how much your data scientists can tell you about your business or even your competitor’s business. Interaction and accessibility between the business and the data science team should be encouraged to foster a collaborative environment.
“Now that we’ve tried everything, let’s see what data science can do for us,” – The immediate response to this particular quote is “too little, too late”. What should happen is that the data science team must be involved at every stage; product conceptualisation, product development, marketing initiatives, sales target setting and identifying the potential customers whose likelihood to buy is high. In fact, data science can even be utilised to decide whether any given product is worth producing at all. Data shows that once certain products saturate the market, any new similar product introduced to the market is usually destined to fail.
“Why do we need to include data science in this meeting?” – This final point is a culmination of the points above. Include the data science team lead in all business meetings, discussions, idea conceptualisation and business planning stages as their input will prove invaluable when it comes to data-driven decision-making.