Data-driven companies deal with large amounts of complex data for processes such as research, machine learning (ML) backed services, analysis, and decision making. They put together custom data science teams for specific tasks geared to meet the company’s bottom lines. Some of these tasks include experimentation, which involves finding new ways to utilize data into new projects, democratization, which consists of scaling the entire company’s data science team. Lastly, the measurement of the impact involves evaluating the team’s findings to decide their effect on decision-making and accreditation. However, it is essential to have a clear understanding of the team, its members, and how they fit into the organization’s structures.
Data science teams and structures
The data science ecosystem requires several professionals for high performance. They can be categorized into two main groups. Firstly, type A data scientists whose primary role is to clean, forecast, model, visualize, and make sense of it. They do not necessarily have to possess a strong knowledge of programming. The second group is type B data scientists who work with the data during production. They build systems, personalize them, and are often knowledgeable software engineers. However, in most cases, the team members do not fall into just one category, and some members may have roles that combine multiple functions. Data science teams are responsible for delivering complex data projects that require software engineering, system analysis, and data engineering. As you put together your data science teams, it is critical to decide on a structure that best suits your organization. For example:
● IT-centric team structure- This is an option preferred by companies that do not have the resources or capabilities to create a data science team, and so they utilize the in-house IT department. They create user interfaces, train models, and deploy models within the IT infrastructure. This avenue is somewhat limiting, but with the help of MLaaS solutions can be adequate.
● Integrated team structure- This approach allows companies to have a data science team and IT specialists who work on model deployment interfaces and infrastructure. These types of teams have IT specialists and experienced data scientists within the team. This structure is more flexible and leverages a deeper understanding of machine learning libraries and tools.
● Specialized data science team- This structure uses an independent data science data department solely focused on machine learning applications and frameworks. This team performs processes such as data cleaning and model training. However, not all members need to have a data science background.
Data science team roles
Data science teams have several professionals, each driven to achieve various tasks. Some of the core members of high performing teams include:
- Data analyst- A data analyst is a generalist who can easily fit into any team and help the team make data-driven decisions. They add value to the team by analyzing data, using it to answer various questions, and communicating the findings to help make appropriate decisions.
- Data Scientists- This team member is a specialist who comes with expertise in machine learning and statistics, which is essential to making predictions and answering critical business decisions.
- Data Engineers- Data engineers work to build and optimize systems that allow the other team members to perform their duties. They work to ensure data is received, stored, transformed, and is readily available to other team members.
More members of the data science team include an analytics manager who manages the ETL solutions and data warehousing, director of analytics who manages the analytic and data science managers, and a business analyst who converts the business’s expectations into data analysis. Additionally, some data journalists help make sense of the data by putting it into context and a data architect who is a critical team member when dealing with big data.
A team’s high performance is easily measured using Key Performance Indicators (KPI), demonstrating how effectively your company is achieving its deliverables. High KPIs focus on an organization’s performance in its entirety while low KPIs focus on departmental processes such as marketing, sales, or call centers. Some of the KPIs that can help you recognize efficiency within your data science team include:
● Application delivery
● The breadth of application delivery
● A detailed delivery road map
● Governance of the entire analytics initiative
● Strategy alignment
A network of independent data and analytics specialists
Data science managers struggle with aligning their data science teams with business value through both the technical and managerial sides. Though it is hard, it is not impossible. By putting together skilled professionals in your data science team and using metrics to measure efficiency and contribution, you can achieve maximum productivity. Tracking your team’s KPIs to ensure they are met may be time-consuming, but it offers a clear projection of your team’s productivity, which improves management. At DataWrk, we house the best data scientists and analysts. Contact us today and let us help you put together the best data science team that suits your company’s needs.