Any large software effort, such as the software estate for a large
company, requires a lot of people – and whenever you have a lot of people
you have to figure out how to divide them into effective teams. Forming
Business Capability Centric teams helps software efforts to
be responsive to customers’ needs, but the range of skills required often
overwhelms such teams. Team Topologies is a model
for describing the organization of software development teams,
developed by Matthew Skelton and Manuel Pais. It defines four forms
of teams and three modes of team
interactions. The model encourages healthy interactions that allow
business-capability centric teams to flourish in their task of providing a
steady flow of valuable software.
The primary kind of team in this framework is the stream-aligned
team, a Business Capability Centric team that is
responsible for software for a single business capability. These are
long-running teams, thinking of their efforts as providing a software
product to enhance the business capability.
Each stream-aligned team is full-stack and full-lifecycle: responsible for
front-end, back-end, database,
business analysis, feature prioritization,
UX, testing, deployment, monitoring – the
whole enchilada of software development.
They are Outcome Oriented, focused on business outcomes rather than Activity Oriented teams focused on a function such as business
analysis, testing, or databases.
But they also shouldn’t be too
large, ideally each one is a Two Pizza Team. A large
organization will have many such teams, and while they have different
business capabilities to support, they have common needs such as data
storage, network communications, and observability.
A small team like this calls for ways to reduce their cognitive load, so they
can concentrate on supporting the business needs, not on (for example) data
storage issues. An important part of doing this is to build on a platform
that takes care of these non-focal concerns. For many teams a platform can
be a widely available third party platform, such as Ruby on Rails for a
database-backed web application. But for many products there is no
single off-the-shelf platform to use, a team is going to have to find and
integrate several platforms. In a larger organization they will have to
access a range of internal services and follow corporate standards.
What I Talk About When I Talk About Platforms
These days everyone is building a ‘platform’ to speed up delivery of
digital products at scale. But what makes an effective digital platform? Some
organisations stumble when they attempt to build on top of their existing
shared services without first addressing their organisational structure and
operation model.
This problem can be addressed by building an internal platform for the
organization. Such a platform can do that integration of third-party
services, near-complete platforms, and internal services. Team Topologies
classifies the team that builds this (unimaginatively-but-wisely) as a platform
team.
Smaller organizations can work with a single platform team, which
produces a thin layer over an externally provided set of products. Larger
platforms, however, require more people than can be fed with two-pizzas.
The authors are thus moving to describe a platform grouping
of many platform teams.
An important characteristic of a platform is that it’s designed to be used
in a mostly self-service fashion. The stream-aligned teams are still
responsible for the operation of their product, and direct their use of the
platform without expecting an elaborate collaboration with the platform team.
In the Team Topologies framework, this interaction mode is referred to as
X-as-a-Service mode, with the platform acting as a service to the
stream-aligned teams.
Platform teams, however, need to build their services as products
themselves, with a deep understanding of their customer’s needs. This often
requires that they use a different interaction mode, one of collaboration
mode, while they build that service. Collaboration mode is a more
intensive partnership form of interaction, and should be seen as a temporary
approach until the platform is mature enough to move to x-as-a service
mode.
So far, the model doesn’t represent anything particularly inventive.
Breaking organizations down between business-aligned and technology support
teams is an approach as old as enterprise software. In recent years, plenty
of writers have expressed the importance of making these business capability
teams be responsible for the full-stack and the full-lifecycle. For me, the
bright insight of Team Topologies is focusing on the problem that having
business-aligned teams that are full-stack and full-lifecycle means that
they are often faced with an excessive cognitive load, which works against
the desire for small, responsive teams. The key benefit of a
platform is that it reduces this cognitive load.
A crucial insight of Team Topologies is that the primary benefit of a
platform is to reduce the cognitive load on stream-aligned
teams
This insight has profound implications. For a start it alters how
platform teams should think about the platform. Reducing client teams’
cognitive load leads to different design decisions and product roadmap to
platforms intended primarily for standardization or cost-reduction.
Beyond the platform this insight leads Team Topologies to develop their model
further by identifying two more kinds of team.
Some capabilities require specialists who can put considerable time and
energy into mastering a topic important to many stream-aligned teams. A
security specialist may spend more time studying security issues and
interacting with the broader security community than would be possible as a
member of a stream-aligned team. Such people congregate in enabling
teams, whose role is to grow relevant skills inside other teams
so that those teams can remain independent and better own and evolve their
services.
To achieve this enabling teams primarily use the third and final interaction
mode in Team Topologies. Facilitating mode
involves a coaching role, where the enabling team isn’t there to write and
ensure conformance to standards, but instead to educate and coach their colleagues so
that the stream-aligned teams become more autonomous.
Stream-aligned teams are responsible for the whole stream of value for their
customers, but occasionally we find aspects of a stream-aligned team’s work
that is sufficiently demanding that it needs a dedicated group to focus on
it, leading to the fourth and final type of team:
complicated-subsystem team. The goal of a complicated-subsystem
team is to reduce the cognitive load of the stream-aligned teams that use
that complicated subsystem. That’s a worthwhile division even if there’s
only one client team for that subsystem. Mostly complicated-subsystem teams strive to interact
with their clients using x-as-a service mode, but will need to
use collaboration mode for short periods.
Team Topologies includes a set of graphical symbols to illustrate teams
and their relationships. These shown here are from the current standards, which differ from those used in
the book. A recent article elaborates on
how to use these diagrams.
Team Topologies is designed explicitly recognizing the influence of
Conways Law. The team organization that it encourages
takes into account the interplay between human and software organization.
Advocates of Team Topologies intend its team structure to shape the future
development of the software architecture into responsive and decoupled
components aligned to business needs.
George Box neatly quipped: “all models are wrong, some are useful”. Thus
Team Topologies is wrong: complex organizations cannot be
simply broken down into just four kinds of teams and three kinds of
interactions. But constraints like this are what makes a model useful. Team
Topologies is a tool that impels people to evolve their organization into a more effective
way of operating, one that allows stream-aligned teams to maximize their
flow by lightening their cognitive load.
Acknowledgements
Andrew Thal, Andy Birds, Chris Ford, Deepak
Paramasivam, Heiko Gerin, Kief Morris, Matteo
Vaccari, Matthew Foster, Pavlo Kerestey, Peter Gillard-Moss, Prashanth Ramakrishnan, and Sandeep Jagtap discussed drafts of this post on our internal mailing
list, providing valuable feedback.
Matthew Skelton and Manuel Pais kindly provided detailed comments on this post,
including sharing some of their recent thinking since the book.
Further Reading
The best treatment of the Team Topologies framework is the book of the same name, published in 2019. The authors
also maintain the Team Topologies website
and provide education and training services. Their recent article on team interaction modeling is a good intro to
how the Team Topologies (meta-)model can be used to build and evolve a
model of an organization.
Much of Team Topologies is based on the notion of Cognitive Load. The
authors explored cognitive load in Tech Beacon. Jo Pearce expanded on how
cognitive load may apply to software
development.
The model in Team Topologies resonates well with much of the thinking
on software team organization that I’ve published on this site. You can
find this collected together at the team
organization tag.
