Why Data Teams Need Software Engineering Discipline

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 00:00 Kenneth Schwartz joins the show to discuss data engineering, governance, data products, and the growing role of AI in modern data teams. 01:17 Why data is still catching up to software engineering, and how low barriers to entry have created sprawl across dashboards, models, and experiments. 02:55 How stakeholder trust, honest conversations, and change management help reduce duplicated work without slowing the business down. 05:23 The software engineering ideas data teams should borrow, including stable interfaces, data contracts, tests, modularity, and repeatable frameworks. 09:21 Why infrastructure, data, and security teams need a more unified engineering culture as AI and data use cases become more complex. 14:43 What it means to shift governance left, and why governance has to become easier for the people expected to follow it. 20:35 How unstructured data, semantic layers, catalogs, metrics layers, and data marketplaces could change how analytics gets delivered. 24:38 Why faster delivery should not automatically mean more dashboards, more models, or more work products. Standout Line “More is not always better.” Pro Tips • Do not treat every new data request as a net new build. Look for overlap, reuse, and shared definitions before creating another dashboard or model. • Build trust before trying to reduce sprawl. People are more willing to standardize when they believe the data team is helping them win, not just saying no. • Move governance earlier in the lifecycle. Capture ownership, quality expectations, access needs, and context when data is ingested, not months later. • Use AI to accelerate the hard parts of the roadmap, but keep the focus on better decisions, not just faster output. Call to Action Subscribe to The Tech Trek for more conversations with technology leaders building the data, AI, and platform foundations behind modern companies. Follow Amir Bormand on LinkedIn for more clips, takeaways, and episode updates.