City of Virginia Beach

Bojan Duric

Civic Data Modernizer

Bojan Duric, chief data officer of the City of Virginia Beach, leads a data‑driven and citizen‑centric culture through a product‑oriented delivery model spanning data, analytics, software development and web platforms. He oversees five cross‑functional teams that turn information strategy into practical solutions, assisting citizens with information retrieval, optimizing city operations and supporting transparent, fact‑based decision‑making.

Notable achievements include establishing the City’s collaborative data and analytics platform and a practitioner certification framework known as “data governance in practice.” His vision is to empower employees, deepen resident engagement and increase transparency through strong governance and usable data. Under his leadership, the city has received multiple recognitions at national and state levels for digital service and innovation.

With a rich background in data science and business analytics, Duric has worked across industries government, transportation, healthcare and consumer packaged goods. He has held key roles in financial, operational, supply‑chain and marketing analytics and has provided management coaching, training and consulting to fortune 100 companies and government contractors.

Duric has served as an advisory board member at the Commonwealth of Virginia data council, Evanta, a Gartner company; Washington, DC CDO group and Old Dominion University’s computer science and engineering department. He holds a B.S. in computer science with a minor in mathematics from Rutgers University and an MBA from Old Dominion University.

Eight years ago, I stood in front of a room teaching our first data literacy class in person. Today that training lives online and is part of our standard process at the City of Virginia Beach. But the training itself wasn’t what changed the culture. What changed the culture was what grew out of it.

We created the DataSmart Collective as a community of practice for people who completed our data academy. It started small and intentional. You had to go through the program or get invited by someone already in it. That exclusivity wasn’t about gatekeeping. It was about building credibility. People saw others getting value, heard about it through word of mouth and wanted in. Today we have over 300 members across a 6,000-person organization spanning parks, planning, finance, public safety and everything in between.

Nothing about this was mandated. No one was told to join. That’s why it works. Research consistently shows that coalitions of the willing drive adoption faster than topdown mandates, especially in government where skepticism runs deep. When a planner shares how she used data to cut permit processing time or a dispatcher shows what analytics did for emergency response, that carries more weight than any directive from leadership. The collective became our innovation spark. A place to share, learn and normalize datadriven decision making across the organization.

But a community is only as powerful as the data it can access. And that’s where we hit the harder problem.

We are now building what I’d call a contextual layer on top of our data. This means asking business units to annotate their data in plain English. Not just technical metadata. Real context. What does this field mean. Why does it matter. What business logic sits behind it. What exceptions exist. This work captures knowledge that usually lives only in people’s heads and makes it explicit, searchable and usable.

This is not optional anymore. If you want to be AI ready, your data needs context. AI systems cannot guess intent. They need explicit meaning to operate reliably. When we document that a revenue calculation includes specific logic or that a service metric follows certain exception rules, we prevent errors and build trust in what AI produces. Plain English annotation is how you bridge the gap between technical infrastructure and real-world understanding.

The collaboration piece is the hardest part. Business units own the knowledge. They identify what data matters and why. Our job is to manage, certify and make it accessible. That partnership has to be genuine, not transactional. We learned that early.

Over the years our team grew from four DBAs and two BI analysts to over 35 engineers and analysts. We now cover data science, GIS, web services, software development and more. The title is still chief data officer but the scope kept expanding because the problems kept expanding.

What I’d tell other government data leaders is this. Culture and infrastructure go together. You cannot have one without the other. The DataSmart Collective built the community. Plain English annotation builds the foundation that community can trust. Start by finding people who are already curious. Give them a place to connect. Then do the harder work of making sure the data underneath actually serves the decisions they need to make.

That is the real work. And it never stops.