#85 How to build data analytics team?
Nice that you have decided to become data-driven or/ and start monetizing data. After the decision organizations face the dilemma of how to get started? Often the practical start is to build your data team or analytics team. They are the frontline of data-driven operations in your company while others learn the data literacy skills a bit later. But what kind of roles do we have to have in the team? Let's have a look!
While team structure depends on an organization’s size and how it leverages data, most data teams consist of three primary roles: data scientists, data engineers, and data analysts. Other advanced positions, such as management, may also be involved. Here’s a look at these important roles.
1. Data Scientist
Data scientists play an integral role in the analytics team. These professionals leverage advanced mathematics, programming, and tools (such as statistical modeling, machine learning, and artificial intelligence) to perform large-scale analysis.
While their role and responsibilities vary from organization to organization, data scientists typically perform work designed to inform and shape data projects. They may, for example, identify challenges that can be addressed with a data project or data sources to collect for future use. Much of their time is spent designing algorithms and models to mine and organize data.
2. Data Engineer
Data engineers are responsible for designing, building, and maintaining datasets that can be leveraged in data projects. As such, they closely work with both data scientists and data analysts.
Much of the work data engineers perform is related to preparing the infrastructure and ecosystem that the data team and organization rely on. For example, data engineers collect and integrate data from various sources, build data platforms for use by other data team members, and optimize and maintain the data warehouse.
3. Data Analyst
Data analysts use data to perform reporting and direct analysis. Whereas data scientists and engineers typically interact with data in its raw or unrefined states, analysts work with data that’s already been cleaned and transformed into more user-friendly formats.
Depending on the challenge they’re trying to solve or address, their analysis may be descriptive, diagnostic, predictive, or prescriptive. Data analysts are often responsible for maintaining dashboards, generating reports, preparing data visualizations, and using data to forecast or guide business activity.
4. Data Product Owners
If your business strategy includes data products and data as a service, you will need product and service managers. This role is close to what more traditional product owners do, but focused on data which is the most valuable asset of our future. Data Product managers craft data products to match business needs (internal, partner and public) in cooperation with more technical members of the team.
5. Overhead aka management
In addition to the job titles above, data teams often include a management or leadership role, especially in larger organizations. These positions include data manager, data director, and chief data officer.
the larger your organization is and the more data-driven it becomes, the larger your data team needs to be.
Some Organizational aspects to keep in mind
Keep in mind that team building is a process and it consists of known phases. In addition, team composition changes over time and you might need more data engineers in the beginning.
Psychologist Bruce Tuckman offers a team development model in which a group of people come together and experience five phases of team formation:
Forming: People are just getting to know each other.
Storming: The group experiences initial conflict.
Norming: The team establishes shared understanding and expectations.
Performing: The team is working well together.
Mourning: The team experiences a sense of loss when someone leaves.
Another organizational thing to keep in mind is the acceptance of new data-focused approaches around the other teams in your organization. Management must make everyone aware of what is the purpose of the data team and how it functions. In some organizations, analytics initiatives are highly centralized, with a single data team serving the entire organization. Other organizations take a more decentralized approach, where each department or business unit has access to its own resources, processes, and employees. Some apply a hybrid model.