4#19 - Bjørn Broum - Data Strategy at Statens Veivesen (Nor)

MetaDAMA - Data Management in the Nordics

«Hvis folk tror at data faget er forskjellig mellom industrier så er det ikke det. Data er Data. Forretningskunnskapen er forskjellig. / If people think data work is different across industries, it isn’t. Data is data. Business knowledge is what changes.»

In this episode of MetaDAMA, we dive into the data strategy of Statens Vegvesen with Bjørn Broum. With 30 years of experience in the data field, Bjørn shares valuable insights.

Bjørn takes us on the journey from Statens Vegvesen’s first data strategy to today’s revised version and explains why a strategic approach to data is essential for success. The conversation also touches on critical questions around privacy, skills development, and organizational challenges. Bjørn shares his most important lesson: keep technical complexity low in the early stages, focus on demonstrating value, and remember that decisions are made with or without data, so make data available when it’s needed!

Here are some key takeaways:

• We need a mindset shift in organizations. Think about the following:
  • You need to keep focus on operational success, that is what you are measured by.
  • You need to understand how you can impact your architecture, before the actual impact happens.
  • New services needs to be build around data. Data cannot be an afterthought,
• You need to work actively with your executive management to ensure that data becomes a stable, not something that is hype-driven.
• Your data infrastructure is what you need consistent focus and buy-in on. This is the foundation that can support the «hype» on top of it.

Statens Veivesen

• Long standing tradition dating back to 1864, tasked to build and maintain road infrastructure in Norway.
• Maintaining 10.500 km of road infrastructure, including about 6000 bridges and 600 tunnels.
• Utilization of sensors to predict risks and impact of e.g. rockfall, snowdrift, avalanches that all could impact traffic and road security.
• Research on how Norway can prepare for autonomous vehicles and traffic safety.
• Modern cars can be used as sensors. You can e.g. get information on how many people are in a car.
• There is a focus on ensuring personal data protection while maximizing insights from data.
• Ensure that the organization develops in a direction that can address and handle new challenges (data-driven).
• Remember that decisions are made with or without data. If you want data-informed decision data needs to be available for decision making, when needed.

Data Strategy

• It’s a living strategy, that needs to stay updated.
• Data Strategy is always designed to accompany for a need, and that need can change over time.
• Data Strategy needs to hold a big-picture that people can relate their work to, that is recognizable.
• Digital value chain needs to be understood also towards the cross-functional needs across the organization.
• Conways law needs to be recognized.
• The value of data is realized in business. Therefore Data Strategy is build on business objectives.
• Be deliberate with your strategy: what do you need to focus on? For veivesenet this was e.g. to establish a common language and vocabulary.
• Statens Veivesen also makes data accessible for research. This needs to be reflected.
• AI is a topic as well, including data readiness for AI.
• Competency and skills are an important part of data strategy.
• Don’t focus on dat roles, but rather upskilling in your organization.
• Understanding boundaries, goals, and strategic priorities is key when working with data strategy.
• Scaling and dimensions need to be strategically understood.
• Focus on the value of data and the business value it potentially generates, not technology.

Bjørns' blog

فصول

1. Introduksjon til Metadama (00:00:00)

2. Bjørns bakgrunn og dataerfaring (00:07:00)

3. Datasentrisk arkitektur og mindset (00:13:35)

4. Veivesenets rolle og utfordringer (00:22:12)

5. Dataforvaltning og personvern (00:27:55)

6. Datastrategi og organisasjonsutvikling (00:32:50)

7. Komplekse datatyper og anvendelser (00:40:20)