4#16 - Audun Fauchald Strand - Alignment in the Age of Autonomy (Nor)

MetaDAMA - Data Management in the Nordics

«Det at det er en team sport; det tror jeg er i hvert fall noe data verden kan lære av (software development). / The fact that it’s a team sport; that’s definitely something the data world can learn from software development.»

Software development is ahead of the data world in many ways, but what can we learn from its methods? Audun Fauchald Strand, Director of Platform and Infrastructure at NAV, shares insights from building autonomous teams and balancing freedom with governance.

Here are my key learnings:

What is a platform?

• A platform is something you build on top of. Something that eases building applications, because if a common ground layer.
• A platform reduces the need for competency, since it predefines some choices.
• The capacity for change is higher when building based on a common platform, then when buying applications.
• It’s important to find a balance in how much you should standardize through a platform and how much room for innovation should be beyond that.
• A platform can provide an «easy choice» that is the best practice way, but it should also allow for alternative, innovative ways to get jobs done.

Software Development and Data

• Software development has been a role model for data work, but they are at different stages of maturity.
• What is happening now in data platform is something Audun has experiences in Software 10 years ago.
• Yet, the principles are largely the same.
• Data is not as mature as software. There is still something to understand when it comes to use of tools and open source, etc.
• «Verden har blitt god å lage software. /The world has become real good at making software.»
• There is a certain agreement on what good looks like in software.
• Software has embraced open source in a much larger degree then data.

Data Mesh and the need for scale

• Data Mesh was a description if the data world from the perspective of a software developer.
• Change in architecture of operational environments have led to more possibilities to scale and to speed up operational data processing.
• This changes the need for speedy analytical data environments that have traditionally been built to compensated for the reduced flexibility of operational environments.
• Scaling up in a Data Mesh fashion, requires more data and analytical competency in teams.

Teams

• Working in teams, and having team ownership of products creates a certain sustainability over time.
• In software, with open source utilization, only teams can take technological decisions. Who else could decide what library to use for what purpose, then the team working hands on?
• You have to believe in autonomous teams for them to work. If not you will create dependencies that minimize their authority as a team and thereby capabilities to deliver.
• To create alignment there are some steps to take, from communicating openly, creating a tech radar to ensure alignment between team, to describing deliberate choices, rather then general principles.

5 learnings from article on alignment and autonomy:

1. Insource data expertise to ensure ownership, security, and business alignment.
2. Embed data professionals in cross-functional teams to enhance collaboration and generate actionable insights.
3. Balance team autonomy with governance by allowing flexible tool use within standardized data policies.
4. Foster continuous learning through asynchronous knowledge-sharing tools like wikis and Slack.
5. Provide clear direction with self-service data platforms to support scalability, efficiency, and governance.

챕터

1. Intro (00:00:00)

2. Audun (00:02:37)

3. Insourcing and ownership (00:10:21)

4. Cross-functional teams and competencies (00:17:41)

5. Balancing autonomy and governance (00:28:23)

6. Team Topologies (00:40:30)