Why no two ‘data dream teams’ are built the same

Organisations generate vast amounts of data, from schedules and budgets to productivity metrics and workforce records. Their data teams help them to convert this mountain of raw material into information that supports better decisions. 

There is no single blueprint for building an effective data team. Each enterprise must make structural choices reflecting its own needs and attitudes.

Much of this task hinges on three recurring trade-offs. If you get the balance right in each case, your data team can become central to value creation. 

The Making of a Data Dream Team, a research report published by the Infrastructure Client Group (ICG) and the UK chapter of the Data Management Association, explores this subject in depth.

How to create a ‘data dream team’

There's no single blueprint for building an effective data team. Each organisation must make choices reflecting its own needs and attitudes.

Much of this task hinges on three recurring trade-offs. If an organisation gets the balance right in each case, its data team can become central to value creation.

For infrastructure owners, where data underpins processes ranging from asset management to regulatory compliance, such choices can be particularly impactful.

The three trade-offs that shape data teams

1. Defensive or offensive?

In heavily regulated sectors, data specialists tend to focus on protective basics such as data security and compliance. 

Elsewhere, they are more likely to be on the front foot, helping decision-makers to use data to improve and grow the business.

2. Technological or analytical?

Some teams are focused on systems, platforms and automation. Others prioritise data analysis, modelling and visualisation.

Most organisations require expertise in both areas, but the balance is rarely explicit.

3. Centralised or federated?

Operating a centralised team provides consistency, thorough governance and shared infrastructure. 

Meanwhile, a federated model embeds data expertise within business units, where it can be more responsive to their specific needs.

The right balance will depend on the organisation’s data maturity and how its departments collaborate.

How are these decisions made?

In practice, organisations rarely make these trade-offs explicitly. But such choices determine whether a data team and the business units it supports are set up for success.

They also influence where responsibility sits, how teams are resourced and how accountable they are for various outcomes.

Data teams must often build relationships and find new ways to contribute to decisions affecting key data assets.

Often, data functions are spread among IT, financial, strategic and operational teams, as shared many of the survey respondents that contributed to our report.

The benefits of deliberate choices

Organisations rarely start with a blank slate. Most are working within the limits of long-established systems and structures.

They often treat data as a by-product of their IT investment, instead of an asset in its own right.

As a result, they can make decisions about the funding and accountability of data teams indirectly, through technological and organisational design choices, rather than explicitly.

At the same time, the fact that data flows across functions that evolve at different speeds makes it hard to maintain a clear, consistent overview.

Failing to make deliberate choices about how data is managed and used will still produce an outcome in this context, but not necessarily the one intended.

What the best data teams understand

The research confirms the intuitive idea that high-performing data teams have a deep appreciation of the following:

• which data is most important to the organisation;
• how that data is used and re-used across various systems; and
• the cost and value attached to different data assets.

With this knowledge, they can move beyond managing systems and/or mitigating data risks and become integral to their organisations’ work.

The same applies across enterprises with differing structures and levels of data maturity: a data team that understands how its organisation actually uses data will add most value.

Keep chasing the dream

The report offers three key insights for data specialists and their employers:

• Data teams are shaped by the choices that organisations make about their role and scope – and the most effective teams are central to the enterprise.
• Data is still widely treated as a by-product of technological investment rather than a primary asset.
• Integrating information across organisational boundaries remains a challenge.

The evolution of data teams from backroom support functions to strategic business partners reflects a broader shift in how organisations understand and use information.

But building a “data dream team” isn't a one-off exercise. It's an ongoing adjustment of ambition, structure and capability. 

We at the ICG believe that data teams led by our members are ready to embrace this. Grounded in their organisational realities, they are ideally placed to create lasting value in an increasingly data-driven world.