How to monetize data by integrating data teams

How important is the structure of data teams in the organisation?

Picture an organisation that wants to become more data-driven. It implements an updated strategy by hiring data specialists in business intelligence, data architecture, data engineering & data science. The organisation does not yet have a clear vision on how to structure & manage this new field of specialists. Small data teams pop up within the various business departments & IT. They work in close collaboration with business experts to create an impact & an appetite for data-driven change. As the number of data specialists in the organisation grows, it creates a need for standardisation, quality control, knowledge sharing, monitoring and management.

Sounds familiar? Organisations worldwide are in the process of taking this next step. In this blog, we will discuss how to structure & integrate teams of data specialists into the organisation. We will base these discussions on Accenture’s classification and AltexSoft expansion on these.


Two key elements are essential when discussing the structure and management of data teams.

The element of control

Customers and the organisation need work to be delivered predictably with quality in control. In other words, tooling, methods & processes need to be standardised among data specialists. Output can be planned & communicated, delivery of output is recognisable & easy to use, and assurances can be given on the quality of work by adherence to standards. Adding to the control element are the practices of sharing knowledge & code base between specialists and centralised monitoring & management.

The element of relevance

Data specialists rely on domain expertise to deliver output that is relevant to the organisation and its customers. Domain expertise is gained by working closely with business experts within and outside the organisation. Expertise building is slow & specific to the domain. Relevance and speed in delivering go hand in hand. Data specialists create maximum value when working closely & continuously with business experts. Adding to the element of relevance are the practices of customer-centricity, value-driven developing adaptability to the given situation in tooling, methods & processes.

The elements of control & relevance determine the success of the next step in data-driven change. The structure & integration of data teams depends on the amount of control and relevance required by the organisation. We will discuss three common approaches for structuring teams.

Decentralised approach

This approach maximum leverages the relevance element. In the decentralised approach, specialists work in cross-functional teams (product, functional or business) within the business departments. This close collaboration allows for flexibility in the process & delivery of output. Communication lines within the cross-functional teams are short, business knowledge for the data specialist is generally high. Central coordination & control on tooling, methods & processes is minimal as expertise & people are scattered across the organisation. Organisations implementing this approach may have less need for control or, in many cases, are just starting data-driven work and therefore lack the need for elaborate control measures.

Centralised approach

As the name suggests, this approach centralises data expertise in one or more teams. This approach leverages the control element. Data specialists work closely together, enabling fast knowledge sharing. As the data specialist work in functional teams management, including resource management, prioritisation & funding is simplified. Centralisation encourages career growth within functional teams. Standardisation of delivery & results are common & monitoring on these principles is done more efficiently. Communication lines with business experts & clients are longer. Creating the danger of transforming the data teams into a support team. As team members work on a project by project base, business expertise is decentralised within functional data teams, adding lead time to projects. Organisations implementing this approach may have a high need for control & standardisation. Furthermore, as work is highly prioritised & resource management coordinated, the centralised approach supports organisations with strict budgets to take steps to data-driven work.

Center of Excellence (CoE) approach

The CoE is a balanced approach trying to optimise both the element of control & relevance. In this approach local pockets of data specialists work within businesses teams (cross-functional) while a Center of Excellence team enable knowledge sharing, coordination and standardisation. The data-savvy professionals — aligned with the business teams — build business expertise and have efficient communication lines within business departments. The CoE team provides a way for management to coordinate and prioritise (cross-) department tasks and developments by enabling CoE team members to support the local data specialist (commonly called SWAT technique). Furthermore, the CoE team is tasked with standardising delivery & results across the organisation. Organisations implementing the CoE approach need local expertise within units to support daily operations and a more coordinated team to support & standardise. As data specialists work in both the business department and the Center of Excellence, the organisation needs to support both groups financially. A higher level of maturity in working data-driven is required to justify the SWAT-like approach in high priority data projects.

Concluding control & relevance are two critical elements to consider when deciding how to integrate & structure data teams within an organisation. We elaborated on three common approaches, centralised, decentralised and the Center of Excellence. Each balancing control & relevance. Which structure will work for your organisation depends on the current level of maturity and the need for either control or relevance.