Professor Yudhvir Seetharam, Head of Analytics, Insights and Research for FNB Business, delves into the myriad skills and disciplines which are needed to explore a career in data.
The amount of data being generated on our planet every day has reached a staggering level. And it’s not about to slow down. If anything, the proliferation of smart devices, apps, websites and digital interfaces will exponentially add to the amount of data we generate – and which businesses need to store, process and analyse in order to keep on delivering what their customers want.
This data-rich world has also created a fertile environment for job creation. Data science is today one of the most in-demand professions globally, in fact, data professionals are in such high demand that The Harvard Business Review labelled data science as one of the ‘sexiest careers of the 21st century’.
Broadly, data science could be defined as working with, and analysing, data to deliver strategic business insights or create value for customers and companies. But data science is very much a catch-all term, and there is actually a plethora of roles and functions that fit within the label.
Within most companies or corporates, however, there are typically five key data roles; and having a broad understanding of what each of these entails is essential to choosing the data science career path that is the best fit for you. Perhaps the simplest way of understanding these roles, and the different ways in which they combine to deliver value to a business, is through the use of a house building analogy.
The design and construction of any house is a multi-faceted process that requires the specialised input and expertise of the following professionals:
The architect
The architect in a house-building project is responsible for transforming the vision of the prospective home owner or developer into a workable plan or blueprint. A data architect does much the same thing, designing the sound and stable systems infrastructure that will enable the business to collect, store, manage, process and use specific types of data with the purpose of achieving an envisaged outcome.
The engineer
In any building project, the engineer is the expert who turns the ‘what’ designed by the architect into the ‘how’ required to get to the outcome desired by the owner or developer. In the business context, data engineers have a similar role. They essentially execute the design and vision of the architect, building the systems needed to make data accessible to other business stakeholders, most often the data scientist or analyst, to optimise business performance and outcomes.
Is this you? The data architect and engineer roles are typically highly technical, which means you don’t need to be a people person to excel at them. Of course, it’s unlikely that you employer will simply put you in a quiet corner somewhere and tell everyone else in the business to leave you to your own devices, so an ability to engage with others is still a requirement; as is a willingness to learn about the business (and industry) for which you are designing and building data systems.
The scientist
Extending the building roles analogy, a step further, the data scientist can be seen as the interior decorator of the house. They are the people who move in once the engineer and building team have finished their work, and customise and arrange the home in a way that provides the optimal lifestyle that the home owner wants. In the business context, this ‘decorating’ involves using the systems designed and built by the architects and engineers to analyse, process and model the relevant data, and then interpret the results within the context of the business ‘problem’ that needs to be solved.
Is this you? Like any scientist, the data scientist needs to not only computational abilities, he or she also need to be a curious individual who is always on the lookout for patterns and anomalies that could be leveraged for business advantage. But unlike the more traditional sciences, the data scientist also needs to have an ability to tell the data ‘story’ in a way that makes sense to non-data people. So good people and communication skills are usually required.
The planning department
Every building project has to conform to a stringent set of standards and regulations. A data project is the same. However, unlike a building, most of the governance and compliance elements related to a data project are internally created and enforced. So, most data teams within a business need to include a professional whose knowledge straddles both the data science and business realms, so that he or she can keep an eye on the systems and processes being built and used and ensure that they align with the standards set by the business.
The estate agent
When the time comes to sell a property, the estate agent is an invaluable mediator between the parties involved in the transaction. He or she has the important role of ensuring that the interest of all parties align and that a satisfactory outcome is achieved. There’s a similar role to be played in the data science and business environment, were a person with strong knowledge of both disciplines can add significant shared value by translating business needs in a way that is well understood by the data team, and making technical concepts or challenges understandable to business people.
Is this you? Often, the roles of the data systems overseer and data ‘agent’ (or whatever they may be labelled in a given organisation) require less technical data expertise, and more general data science knowledge and understanding. They also require a keen understanding of business principles, and an ability to engage with technical and business people at all levels of the organisation.
While the five roles described in this building analogy are a very broad view of the various functions involved in data science, there are obviously many more specialised roles that fall within each of these broad job categories, particularly within different industries and organisations. So, it’s likely that the person setting out with a particular data science career in mind will end up performing very different duties down the line.
So, if you’re a first- or second year data science student who looked at these broad descriptions of the main data science roles and realised that you may have set off on the wrong path, there’s no need to panic. Studying to become a data scientist is a lot like studying to be a doctor. The first few years of study are aimed at giving you a general level of knowledge, after which you can choose your particular area of focus based on your more in-depth awareness of the various roles, and which one is the right fit for you. But whichever data science path you choose, you can be sure of an interesting, challenging and highly rewarding career.
