Why in some cases a managed, heterogeneous SCRUM team may be preferable to body shopping?

When developing data and business intelligence (BI) solutions, not only technology, but also team building and operation contribute significantly to the success of a project. There are situations where body shopping can be an effective solution, for example for short, clear tasks or specific expert needs, but if the project requires a longer term, more complex, or more serious business risk and comprehensive expertise, a managed, heterogeneous SCRUM team is often the preferred choice.

Stable, multi-domain expertiseThe experts of a managed Scrum team initially know the basics of the common methodology and gradually become a real, mature team as the project progresses. At all stages of the project (design of data architecture, data cleaning, data loading, etc.) the appropriate competencies are achieved and they are constantly evolving within the team. In the case of body shopping, the customer has all the responsibility, the management of the experts, the management of the tasks, the management of individual problems, interpersonal problems, which is a task that requires complex coordination. In the case of body shopping, the risk is higher on the customer's side.

Continuous and coordinated feedback: both the managed team and the body shopping model may be able to operate on a sprint basis, but the essential difference is that in the case of a managed Scrum team, regular planning, feedback, and responsibility for team development are built-in elements of the service. On the client side, this means that the entire team reacts more efficiently and in a coordinated manner over time to new needs or emerging challenges, thus reducing implementation risks.

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Less management burden:In the case of the managed team service, the Scrum Master oversees the processes and the collaboration of the team members, while the Product Owner can focus on the business priorities of the project and the long-term use of the BI solution. This allows the client not to deal with coordination on a daily basis, but to focus on strategic issues, internal stakeholder management and interaction with business users. In body shopping, on the other hand, the customer has to coordinate the individuals, which can work well if you have the right internal resources and competence to manage, but can easily be overwhelming in the case of more complex projects.

Stability and knowledge sharing:A managed Scrum team typically thinks long term, so the knowledge gained stays within the project and is not lost by switching experts. In the field of data, it is particularly important, since a deep understanding of the relationships between systems and data greatly influences the success of the project. There is a greater chance of fluctuation in the body shopping model, and if a leading expert leaves, it can be a serious problem to transfer knowledge. In the case of a managed team, the team takes care of the proper sharing of knowledge.

Better risk management and more effective decision making: the mechanisms — such as retrospectives, shared backlog management and transparent communication — that are the default for the managed Scrum team, help to quickly identify and resolve risks. Since the team as a unified whole is responsible for the success of the development, less time goes by having to coordinate different actors. In the case of body shopping, similar procedures are also possible, but often the customer must initiate and maintain this design.

Although body shopping can be useful for specific, well-defined tasks or short-term projects, a managed, heterogeneous SCRUM team can prove to be a preferable choice in many situations. Especially when the length, complexity of the project and the BI needs of the company require deep expertise and continuous cooperation. It is worth considering the complexity of the project, the available internal resources and the long-term development goals. If the goal is a stable, efficient and long-term collaboration, the managed Scrum team is more likely to deliver the desired results.

Author: Zsolt Borbély (CEO - UC Big Data Zrt.)