Kenneth Schwartz, VP of Global Data and Governance at Genmab, joins The Tech Trek to talk about what happens when data teams start applying software engineering discipline to modern data work.

As AI raises expectations across the business, the challenge is no longer just building more dashboards or models. It is building data products, governance systems, and engineering cultures that can move from experiment to production in a repeatable way.

In this episode, Kenneth shares how data teams can reduce sprawl, create stronger stakeholder alignment, shift governance earlier in the process, and use AI agents to accelerate the data roadmap without simply creating more noise.

Key Takeaways

• Data sprawl often starts with good intentions. Teams want to move fast, but without alignment they can end up solving the same problem in multiple ways.

• Software engineering practices are becoming essential in data. Stable interfaces, data contracts, testing, modular design, and clear ownership help data teams scale with fewer downstream breaks.

• Governance works better when it is built into the process early. Kenneth explains why governance should not be treated as a cleanup project after the data already exists.

• AI can help data teams move faster, but speed alone is not the goal. The bigger opportunity is using automation to improve quality, reduce manual work, and give teams more time to think.

• The future of analytics may depend on better foundations. Catalogs, semantic layers, data marketplaces, and governed metrics can make data more usable across BI, apps, chat interfaces, and agents.

Timestamped Highlights

Why Data Teams Need Software Engineering Discipline