Experts estimate that global data volumes will reach over 170 zettabytes by 2025. So, it is essential to have a data science team that interprets data, understands the importance of data to business performance, uses data to make informed decisions, and improves the overall data quality of the company.
However, data science teams can dissolve as quickly as they are created due to poor structuring and management, especially when companies hire data scientists without clear objectives for their work or a defined strategy to manage a data science team. Data science is a team sport, and several critical factors are required to build, grow, and retain an effective team.
To be fully effective, data scientists need to work with other roles as part of a team. As companies fully embrace data and build their data science departments, it is essential to establish the right processes and workflows first before proceeding to hire people with the right skills needed to implement these processes. Here are some important roles to consider when structuring a data science team.
Data scientists apply advanced analytics techniques, including (but not limited to) machine learning, modeling, statistics, and visualization, to discover patterns in data. They find and interpret rich data sets, merge them, create visualizations, and use machine learning to build models that help extract actionable insights from data. They know the entire process of data exploration and can present and communicate data findings and results to various team members. Their end goal is to turn data into actionable insights that improve future business decisions.
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Data engineers set up systems and processes data scientists and analysts use and rely on to work with data. Data engineers need to know how to optimize data flow to minimize latency and increase analytics flexibility. They also work with application engineers as they move data science projects into production. They create and test data pipelines for optimal performance.
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Data architects design and manage data systems, set policies for storing and accessing data, coordinate disparate data sources across an organization, and integrate new data technologies into existing IT infrastructures. Data architects can liaise between the IT side of an organization and other departments, aligning data collection and distribution policies with the organization's operational and strategic goals.
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A machine learning engineer takes AI models developed by data scientists and deep learning engineers and brings them into production. They review and sometimes have to rewrite a data scientists’ code before sending it off to production. They make the deployment of the model more straightforward. Machine learning engineers fill the gap between data scientists and data engineers.
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Data annotators enable the training of machine learning models. They enrich data by labeling it in a way that machines can recognize. Data annotators are vital because AI and machine learning models need constant training to become more efficient and effective and deliver the desired results.
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Like the title implies, data annotation managers are in charge of the data annotators and the entire data annotation phase of a project. They define what needs to be annotated and provide instructions to the annotators. They also set up a quality assurance procedure to ensure consistency in labels.
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Data analysts use standard business intelligence tools and other data analysis applications to derive insights from data. They mine data from primary and secondary sources and then reorganize it into a format that both humans and machines can easily read. They also use statistical tools to interpret data sets, paying particular attention to trends and patterns valuable for diagnostic and predictive analysis.
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Data science teams are structured differently depending on the maturity and size of a company's data science program, its objectives, and organizational structure. Despite these differences, there are standard models of data science teams—each with its strengths and weaknesses.
A centralized data science team structure integrates all the data scientists within the organization into a single team. In the fully centralized data team model, all data resources—people and technology—are owned by one central data team. If someone from product or finance has a data-related request, they submit it to the data team for prioritization.
Companies just starting with only two or three data science team members usually follow a centralized structure. As the team grows, with data, cloud, software, security, DevOps, and similar engineers embedded into or sitting close to the data scientists, the team has dedicated talent who leverage best practices to build effectively, scale and support AI-native products.
This centralized approach also facilitates the discovery and adoption of standard tools and infrastructure, which helps keep the organization running efficiently on cutting-edge technologies, provides bargaining power during vendor contract negotiations, and simplifies operational maintenance.
One disadvantage of the centralized structure is that some departments may feel their requests are not prioritized appropriately, or the data team is too slow to respond to time-sensitive issues. Also, by being somewhat removed from the business units, data scientists might need time to gain an in-depth understanding of the business unit's domain space.
In a decentralized data science team, data scientists are embedded with other departments (i.e., marketing, sales, etc.), and they cater to the data needs of that specific team. The decentralized structure allows data science team members to provide support and report to various business units within the organization. This structure is typical in larger organizations that have established the need for data science efforts in other business units.
A decentralized team structure improves accountability because other business units have greater flexibility to control their own data needs. A decentralized team structure ensures that data scientists will have the context needed to work effectively with their business partners and the opportunity to develop meaningful personal relationships to get buy-in for ideas and initiatives, promoting solid organizational alignment.
However, decentralization also creates some challenges. Having competent leaders to manage engineers and data scientists is essential for a decentralized structure to work successfully. A decentralized organization restricts the mobility of data scientists, often resulting in knowledge silos, fewer opportunities for peer mentoring, or a limited career growth path. In addition, decentralization makes enforcing uniform quality hiring standards, sharing analytical infrastructure, and pushing for standardized analytical practices more challenging.
Hybrid structures take advantage of the strengths of centralized and decentralized systems. The data science team has a centralized management structure in a hybrid unit, i.e., data scientists still report to a central leader. However, they are assigned to individual business units to help them meet their objectives of making data-driven decisions. A hybrid structure does not necessarily mean a perfect balance of centralized and decentralized structure; it merges both while leaning toward either of the two.
In a centralized hybrid structure, a single organizational data science leadership team sets the company's data science strategy like a centralized team. The data scientists are spread out among as many departments as necessary and work on the department’s data problems. Rotating data scientists among the various centralized sub-teams will broaden their knowledge base. As a result, the organization achieves a centralized infrastructure, a common data science strategy, and effective talent management. Each of the business units has a team of dedicated individuals who are knowledgeable about their individual needs.
The data scientists in a decentralized hybrid structure report to the respective business units. However, a central data scientist leader or team works with the data scientists in these units to facilitate sharing of information across departments. The central data science team might even be able to set the organization's data science strategy; however, implementing it might be a challenge with no dedicated team. Having a centralized team or an office managing the entire project life cycle can better manage the life cycle from development to productization.
To attract data science talent, your company must have a sound data strategy, a good data culture, and a data-driven environment where data scientists can make an impact. Ideally, data scientists would like to join an organization that understands the importance of data and utilizes it in its operations.
When a company has a solid data management plan and a data team with a vision, data scientists will know what's expected from them before they sign the contract. It also reinforces that management values their work and will support their role. After all, there is no use browsing terabytes of data if the executives ignore the insights.
A data team typically has primarily technical roles and requires some knowledge of coding languages, software applications, and platforms as prerequisites. When hiring for these types of roles, employers focus mainly on complex skills. However, when choosing suitable candidates to join your team, it's essential to consider all the skills that help make a data team effective.
You must understand the function and the needs of the role you are recruiting for to decide which attributes make an ideal candidate. In addition to technical knowledge, most roles demand problem-solving ability and competency in distilling business problems into technical terms. Communication, collaboration, and teamwork are crucial skills for many roles because data team members will work with business-side teams or clients at some point.
When recruiting for a new team, you can first hire experienced candidates before branching out to more junior candidates. Experienced first hires set and define the culture, define and execute the first deliverables, and create the structure to allow scaling.
Recent college graduates, people transitioning from other fields, and data science boot camp grads should make up the second wave of candidates. They will be much more likely to make the right decisions if a few experienced mentors can guide them.
A technology stack consists of the tools a team uses to accomplish projects and deliver products. You may not achieve your goals if the team does not have access to the right software to carry out their duties. Your team's future growth depends on choosing the right stack and tools, among other factors.
When team leaders are looking for the data science technology stack that best fits projects' needs, the many options initially seem overwhelming. However, there isn't the "best data science stack," but there is the most effective option for your goals. To determine which technologies will be necessary, always keep your objectives in mind and your team's project.
Irrespective of project-specific tools, there still are core tools needed in every data science team, and they include:
Technology choices should also take the team's skillset into account. For example, if your team is composed of Python experts, their tech choices will lean more toward tools that are compatible with their expertise. It's not possible to learn every technology on the market today (really, it's probably not even possible to count them).
Understanding what needs to be achieved, weighing the pros and cons of specific technologies, narrowing it down to a few mandatory tools, and knowing how to use each of those tools is what you should do when assembling a tech stack.
A project management framework consists of processes that prioritize, plan, execute, and deploy a project. Data science projects have unique requirements, so some management frameworks will work for one project and fail for another. Here are some project management practices that team leaders can apply to data science projects. Data science team leads can combine all these methodologies alongside other data science life cycle frameworks such as CRISP-DM and TDSP.
Agile is the best framework for IT projects with constantly changing requirements because stakeholders can review and make changes during the development process. Early in the development life cycle, the data science team and stakeholders agree on the requirements (the project backlog) and keep reviewing them as the project grows.
Unlike software development, data science projects cannot be utterly predefined because it is challenging to learn about the project's most effective techniques and methods beforehand. Each data science project requires trying different techniques.
Kanban is a three-column board method that signifies the position of a task/project with "To Do," "Doing," and "Done" labels. There are no time boxes in Kanban, so data scientists have more flexibility to execute their work. However, one might need a higher level of discipline to ensure tasks are not sitting in "Doing" for too long.
The processes involved in each data science project can vary, so it might be hard to label a Kanban board properly, so data science teams might have to stick with the generic labels or make Kanban boards with additional columns.
Scrum divides a project into mini-projects called "sprints" with a consistent and fixed duration ranging from one week to a month. Sprint planning and sprint review meetings are held at the beginning and end of a sprint, respectively. The team estimates their deliverables and meets every day to plan how they contribute to their end goal, then makes a sprint plan to develop these deliverables.
To properly implement Scrum in data science projects, implement action items in the sprint that allows research. For example, devote the first two sprints of a project to exploratory work to help the team get familiar with the data. The downside to Scrum is that it can be time-restrictive, considering that data science project timelines vary. Also, the daily standup meetings can take too much of the team's time.
Waterfall is a framework that divides a project into a series of predefined phrases. You can't begin a new phase without completing previous ones. It is the best framework for short projects with specific requirements and a stable product definition. However, it doesn't work quite well in data science because data science projects have dynamic requirements and a lot of experimenting.
Managing stakeholders for data science projects can be tricky. It is natural for executives to ask teams to commit to a clear timeline and hold them accountable, but this applies to data science teams differently. Most data science work has a strong element of research, which means a fair amount of time spent with less tangible results. Meet regularly with stakeholders to help them understand the ups and downs of data science work and the non-tangible progress the team is making.
Make sure all stakeholders understand the business goals of your project, not only the benefits of the project but also what the project cannot deliver. If the vision or idea of what should be achieved is too broad, narrow it down. Communicate with stakeholders when a project begins and provide an opportunity for stakeholders to ask questions with meetings, email threads, or phone calls.
Engage with stakeholders so you can help establish a better relationship between the data team and the stakeholders. It helps build trust and ensure both sides of the business's operations align with the same goals. Trust is the most important principle to establish with your stakeholders and maintain on an ongoing basis.
There is a lot of investment upfront that is hard to leverage immediately when building a data science team. Business leaders have difficulty understanding the value the data scientists provide, while data scientists also battle to demonstrate their value. However, the presence of a data science manager provides a bridge between data and business and connects both worlds.