The importance of data scientists
Data science is increasingly becoming a core capability. After all, who or what is going to turn the increasing mass of data into something usable in every department, from marketing to product development? But there’s a real concern because of the shortage of data scientists and the low skill adoption rate compared to the growth in technology.
Our principal researcher Vidhya Karthikeyan shares her view on the importance of data scientists in organisations who want to be data-driven and future-orientated.
Data scientists enable evidence-based decision making in companies like BT. This helps to spot unintuitive trends we may not know to look for in the first place.
Everything is changing, constantly – from network infrastructure to the services we deliver over that infrastructure to the business processes that support both. People of all levels in a business have to answer different questions as their company evolves, but data scientists with a wider purview, such as those in research, are able to take a step back from the immediate question, identify common threads across a multitude of similar questions and call upon the right tools for the right question. There is no one size fits all.
There’s more to data science than simply finding patterns in existing data. It’s as much about tools and artificial intelligence (AI) as it is about domain knowledge, trust in stakeholders (and vice versa) and an understanding of what makes actionable insight in the business at that time vs. interesting but not necessarily actionable information.
Data scientists help the company define the way they collect and treat data to ensure that those efforts lend themselves to innovative solutions and insights for the business. Research data scientists help companies develop a tools roadmap. As pioneers of innovative proof of concepts, they are exposed to a number of tools that fit different purposes. As organisations evolve as a whole, a data scientist is well-placed to inform decisions on tooling for the wider community with experienced views on ease of use and capability with a potential target audience or infrastructure in mind.
Downstreaming data science is unique and differs from traditional software delivery. Data scientists might derive insights from data and present those insights to drive better decision making within the business. They might design futuristic learning algorithms that are then integrated into existing IT systems to enable automated and proactive action to improve customer experience. They might assemble a capability from a number of existing tools, using slivers of each of those tools and transforming data from one environment to another. Right from wrangling data to modelling to applying machine learning for discovery, the output and skillset of a data scientist can vary vastly to reflect the needs of the business.
In my opinion, good data science should be three things:
1. It must enable better and faster decision-making. The main purpose of data science is leveraging the vast volumes of data we have always collected and transforming an existing capability into one which is data-driven. Often this means changing existing processes as a result of the insight you’ve got from the data.
2. It should think about the common capability that would address a number of related questions rather than design spot solutions to analytical problems.
3. It must also be reproducible by others. Firstly this is to ensure quality control. Secondly, whilst a data scientist might design a data solution and help integrate it into the business, the maintenance of those solutions might reside in another part of the company entirely. Good data science is documented well enough for it to be understood by someone in the organisation with a different skillset entirely.
There are a number of other benefits.
