Delving deeper in the role of a data scientist, Gupta said that they typically work with three to four people. They are usually paired with a subject matter expert; a data scientist who understands how to work with the data and how to manipulate and create the variables; a data engineer who can put together end-to-end solution; and lastly, someone to work on the visuals such as dashboards. In other words, a variety of people is needed to help create the solution for the customer.
Data scientists vs data analysts
Data scientists are often confused with other similar roles such as data analysts and Chief Data Officers (CDOs). Although these roles tend to be complementary to one another, they often span a wide variety of different skill sets and functional roles. The devil is in the details, so what exactly are the differences?
According to Gupta, a data scientist converts volume into value and provides a prediction of what is going to happen. In contrast, a data analyst checks for the viability and visualisation of the value by breaking them down into smaller topics, and provides a representation of what happened instead.
In a nutshell, data scientists are responsible for forecasting insights using past and current data, while data analysts are responsible for summarising current state using past and present data. However, the commonalities between both involve data aspects such as data governance, data quality, data preparation, data modeling and analytics skills. As such, both roles are very critical for information-based decision-making process.
On the other hand, the CDO manages data as a corporate asset. Similar to the way the Chief Financial Officer manages financial capital, the CDO manages 'data capital'. Data is increasingly viewed as the key asset for organisations and it's the CDO's responsibility to oversee data governance, quality and security.
Gupta highlights that the CDO role is more concerned with internal data policies procedures, standards and guidelines; often looking at security policies surrounding enterprise data and customer data. The data scientist and CDO roles could work independently of each other, unless the scope of the CDO extends to big data, which has unique data governance challenges of its own.
Important skillsets of a data scientist
As Gupta mentioned earlier, data scientists spend 80 percent of their time collecting, cleaning and organising data - which has got to be the least sexy bit of the job. Also referred to as 'data wrangling', data scientists usually work on cleaning data, connecting tools and transforming data into a usable format before deploying predictive analytics and modeling.
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