The data science discipline has been around for quite some time now, but you may have noticed the field gaining traction in recent years. This is due to a number of factors: technology is rapidly developing, digital adoption is consistently increasing, and the amount of data available is growing in volume and complexity. All these and more have built a foundation that has made decision intelligence vital for organizations today.
With the data science boom also comes an increasing demand for the manpower and expertise required. Because in order to extract value from data, you need the right roles and the right skills—and that means you need more than just the quintessential data scientist.
As a multidisciplinary field, data science combines several areas such as statistics, machine learning, computer science, and even business acumen. To build a machine learning solution or engage in analytics projects, a diversity of skills and perspectives is essential. In other words, it takes a team.
What does a data science team look like?
Let’s take a look at the different data science roles and responsibilities that powers a typical team
1. Data engineer
Before anything else, it’s important to have a solid foundation for data management—infrastructure such as databases or large-scale processing systems that keep data flowing and processing. Enters the data engineer, who is responsible for developing, constructing, testing, and maintaining that data architecture. In short, they refine the methods in which you bring in data, which often involves tricky engineering aspects.
2. Data architect
Next, you need to make sure that the data you are working with is useable. While the data engineer is responsible for building and maintaining the data architecture, the data architect takes care of conceptualization and visualization. By asking the question “what kind of data should be delivered?”, the data architect visualizes and creates the blueprints for a data management framework that can integrate, centralize, protect, and maintain data sources.
3. Database administrator
In order to ensure that the database and data is accessible to those who need it, you’ll need a database administrator. Not only do they take care of granting or revoking access for relevant users, but they’re also responsible for the security, proper functioning, and optimal performance of the database.
4. Data scientist
A number of roles in the data science world are responsible for turning data into actionable insights, and the data scientist is one of them. With responsibilities that often overlap with the data analyst, statistician, and even the machine learning engineer, the data scientist provides a solution to a business challenge by applying data processing techniques, machine learning algorithms, and artificial intelligence to structured and unstructured data—producing actionable insights in the process.
5. Business analyst / Citizen data scientist
The business analyst is usually a non-technical professional, and he or she is responsible for making sense of the insights produced in the context of the business. With strong business acumen and a specialized knowledge of the organization’s processes, this role often acts as the intermediary between the business and IT.
Building your data science team
It takes more than just computer science and statistics skills to be successful in this field. A careful confluence of diverse roles is key to succeeding in the data science space. Basically, you need a combination of business acumen, development / tech expertise, and creative skills.
Here at Sryas, we understand the diversity of roles needed to complete a data science project. This is why a multitenant architecture is built into our enterprise data science platform—to cater to diverse roles with diverse business needs.