The concept of teamwork is fundamental to the success of any organisation. Sport fanatics understand all too well how this can help shape the success (or failure) of their favourite sporting teams. But when it comes to data science, many still think it is a solitary pursuit with people working in isolation from the rest of the organisation. The reality could not be further from the truth.
I recently came across an insightful article that explores what constitutes a strong data science team structure, providing a number of very relevant insights from various industry experts. While the piece does not bring in the sporting analogy, any team requires individuals who fulfil certain roles and responsibilities – much like the positions of a rugby, football, or cricket team. With this in mind, the piece does point out that companies should not just appoint several data scientists for the purposes of forming a data team. Rather, the business must understand the data science roles and consider the need for the data science team to understand the business challenges that needs to be overcome, in order for success.
Different roles
Much like sport, there are many different roles in any data science team. How effectively these individuals work together will greatly impact on the team’s ability to extract value and meaningful strategic insights from the data at hand. Examining what constitutes a data team, the article says that while data scientists will comprise the bulk of it, there are different types that must be considered. These can range from machine learning experts, statisticians, and developers to name a few – and all with varying knowledge and expertise.
In fact, the field is so vast that you can have an individual who has a PhD in data science but does not necessarily have much knowledge or experience in the applications needed for the business. Similarly, the individual might not understand the insights the company requires to grow in a competitive environment.
Building on from appointing the right mix of data scientists, any data team must consider data engineers who are responsible for setting up the data pipeline and managing it. So, while the scientists build analytical models, the engineers do the nuts and bolts of setting up the processes.
Strategic integration
Tying all this together is a data strategist that provides an invaluable link between the business and the data science team. It is a case of combining the productionising of analytical models with the data architecture and guiding it in such a way to meet the core business objectives. This function is often neglected as much of the focus falls on model development. Unfortunately, many organisations do not have the skills on board to put those models in use as part of the ecosystem of its production systems.
But it remains critical to combine the different skills of data scientists, data analysts, data engineers, and a data strategist. Of course, not all companies will be able to afford specialists in each of these roles. Instead, they should have the team share responsibilities to cover each of these touch points. The key is to have the roles fulfilled in a unified manner and deliver the best data value the organisation requires. Given how data will only become more important as businesses embrace digital transformation initiatives, the data science team becomes one of the most vital to help ensure the success of the organisation into the future.
