The Evolving Role of a CDO with Bojan Duric

Bojan Duric is the Chief Data Officer (CDO) of the City of Virginia Beach where he promotes a data-driven and citizen-centric culture at all levels of the organization. Bojan’s rich experience in data science and business analytics span multiple industries including government, transportation, healthcare, and consumer packaged goods (CPG). He shares with Apex how he has watched the role of a CDO evolve throughout his tenure and discusses the current data trends that can impact an organization.  


Q: What is the difference between a Chief Data Officer (CDO) and a Chief Analytics Officer (CAO)? Are they one in the same?

A: I personally wear both hats and view these roles as being one in the same. However, depending on the size of the organization, its culture, and individual skills and personalities, the roles might be different. Both roles often play change agent with the same end goal, utilizing data and people to support organizational growth, enhance operational efficiency, and deliver an exceptional and personalized customer experience. The CDO is often incorporating both roles, while the CAO might come from the business side, focusing on data utilization without data governance, data infrastructure and other more technical data-related responsibilities.


Q: How have you seen the role of CDO change? Have you encountered any challenges facing the CDO function?

A: If we look at any business capability from a technology, people and process framework perspective, we can see that data plays an integral part and sits in the middle, acting almost as a glue. Projecting this view onto the CDO role clearly indicates that the role is evolving as our customers, processes and technology evolve, especially regarding overall responsibilities and organizational expectations. The role has become more mature and better-defined over the last few years, but the major leadership traits of possessing a well-balanced approach to technology and process while being an overall good negotiator and conversational leader to empower and inspire an entire organization to embrace data-driven practices remains a challenge. As an organization matures in its data and analytics journey, the role is growing by fine-tuning and expanding certain responsibilities. When I assumed the CDO position with the City of Virginia Beach, we defined our purpose as “to promote a data-driven culture at all levels of the decision-making process by supporting and enabling business capabilities with relevant and right information accessible securely anytime, anywhere, on any platform.” We were early in our data adoption journey, and our main goals were to address challenges such as breaking data silos, building internal data and analytics human capital, implementing an enterprise analytics platform and becoming cloud ready. By focusing on these challenges for two years and successfully closing the identified gaps, we enhanced our purpose to include digital transformation and innovation which changed the CDO role and responsibilities. It requires 360-degree support from leadership, peers, customers and fellow data and analytics practitioners. To secure buy-in from all stakeholders, it is very important to define an agreeable and achievable customer-centric purpose statement and start delivering on the promise. I have been able to get the necessary buy-in and continuously grow my team by frequently engaging customers and taking on new responsibilities to deliver actionable insights and relevant analytics solutions.


Q: How is your Organization leveraging Big Data and AI and machine learning to transform their businesses and what opportunities does it present to the business? What are the challenges, and how can these be best overcome?

A: Both Big Data and AI have been occasionally used as “buzzwords.” Big Data almost started to fade after failing to deliver on high expectations from all the hype a few years ago. Thanks to AI, Big Data is getting its second wind. AI, particularly narrow AI (NAI) seems to be able to deliver quick wins by automating processes and integrating chatbots, paving a good foundation for wider more sophisticated AI-backed solutions. So Big Data as a backbone of AI is getting attention again more from variety and veracity with way better outcomes than a few years ago when most business could not comprehend its applicability. Bots, RPAs and virtual assistants make AI applicability tangible and relevant to the business users. We have seen this transformation and its direct, positive and measurable impact on our organization with simple bot integration to handle basic, repetitive yet frequent tasks such as password resets and knowledge base searches. After one successful implementation, a floodgate of other use cases opened. Just one case, demonstrating seeing makes believing, has inspired great demand while cloud services along with human capital skills has proved to be able to scale appropriately and meet the increasing demand. Further automation and NLP adoption have huge potential, not only as a new solution but as an extension of existing business capabilities, almost AI as a service and product enhancement. For example, we all have access to personal assistants not only senior management as was the rule in past decades, but we do not utilize it in our everyday tasks to be more productive. The key to marginal improvements and adoption on a larger scale to gain huge organizational impact and operational efficiency involves freeing the creative mind to deliver new values. It requires unlearning old habits, relearning existing ones and learning new approaches. 


Q: What are the current data trends and how will it impact your organization?

A: Data is growing exponentially and new trends are emerging almost frequently but I would focus on a few that can make a huge impact on our lives as data consumers as well as on data practitioners such as data sharing and data privacy. It seems these are on opposite sides but not mutually exclusive rather data ethics inclusive. It does not mean that private data cannot be shared or that sharing means opening up all data. There is governance in place to ensure appropriate levels of privacy and security. It requires a good understanding of existing data compliance as well as your role to support and enforce data governance processes. I found that “data owners” are most reluctant to open up and share their data even in instances where there are no legal, compliance or business restrictions. I always use the analogy of home ownership when trying to explain data governance and especially, the term “data owner.” I ask the group to raise their hands if they are homeowners. You will notice most people in the room raising their hands. Second, I ask them if they would still be homeowners after failing to pay their mortgages for 12 months to raise their hands? Only a few hands would stay up (those who owned their homes outright and no longer had a mortgage). It is the same with the data; we own certain data and it is protected and regulated depending on industry and compliance, but in the most cases we as data practitioners are data trustees. We take good care of our homes, we follow regulations, do home improvements to enjoy our homes, improve quality of life, and build equity. We certainly do not mind keeping our neighbors accountable if we see that their neglect can jeopardize our living conditions and diminish equity potential. Why should it be different with data? If your home is one of your biggest assets, and we continue promoting ‘data as an asset’, then we should manage it as an asset. Data sharing is one way of improving and enriching your data. It also promotes data reusability, significantly reducing the number of requests for new datasets which force highly-skilled data engineers to perform unnecessary and redundant ETL processes. I have to admit that the data sharing implementation might be painfully slow, but we will see enormous efficiency among our customers even with small improvements around data sharing. Streamlining the process and annotating data on small samples eliminates not only silos but unnecessary errors and increases trust in existing data. Thus again showing the importance of being a data trustee.   


Q: How has DevOps and cloud services changed the way you design, build, deploy, and operate online systems and secure infrastructure?

A: My decades of professional experience as a data practitioner and a leader have taught me that information is valuable and actionable only if received when needed—one day or even one hour late could easily make it irrelevant. A day-old newspaper is viewed as useless, almost like garbage to be recycled. My latest hire to lead data engineering efforts came from a strong DevOps and cloud background. I see strong, agile, and infrastructure scalable data engineering is a prerequisite for successful data science and data analytics practices. For those going to the gym regularly, data would be your legs and you never want to forget your leg day, while analytics is your upper body, the most visible thus getting the most attention. Data engineering is your core, abs and back. A weak core compromises your overall health and fitness. So strong data science without strong, agile data engineering is questionable too. I must be clear that DevOps is not a simple copy/paste to data engineering, but there are many similarities. The data engineer role is often used interchangeably to define data architect which requires a solid cloud understanding. It also requires good scripting skills where I pull parallel with software developers, and as every code, it requires versioning and collaboration. In previous years, we have managed to retool part of our DBAs practice and develop a data engineering team that is fully cloud-certified adopting DevOps principles with an ultimate goal to manage data via code repos rather than maintaining multiple data tables and views. On the analytics side, in addition to computing power and scale, the cloud offers production-ready, data science services which require borrowing DevOps methods. Both cloud and DevOps hugely accelerated a long-term need for data analytics and quick turnaround resulting in DataOps as not only a set of best practices but as its own methodology in data analytics.


Bojan Duric is the Chief Data Officer (CDO) of the City of Virginia Beach where he promotes a data-driven and citizen-centric culture at all levels of the organization. As CDO, Bojan is responsible for implementing data and information strategies across the enterprise with wide impact not only on Virginia Beach residents but whole Hampton Road region. Shortly after joining the city, he successfully implemented the highly demanded Data Academy Program, a data and analytics literacy initiative which enriches employees with data and analytics skills to support factual based decision-making process. Some key advances for the City of Virginia Beach in his short tenure include the implementation of the first data and analytics platform for collaboration and a framework for certifying both data and practitioners, as he likes to call “Data Governance in Practice”. He views data as an asset to empower employees, boost citizen engagement, and increase transparency.

Bojan’s rich experience in data science and business analytics span multiple industries including government, transportation, healthcare, and consumer packaged goods (CPG). He has held key roles in financial, operational, supply chain, and sales and marketing analytics. His vast business background includes providing management coaching, training, and consulting to Fortune 100 companies and government contractors, such as Norfolk Southern, Carlsberg A/S, and ADS Inc. He is proficient in several open source and proprietary technologies and has developed a range of data solutions and analytics products recognized by influential data communities, and both private and public organizations.

Bojan is a guest lecturer at the Old Dominion University (ODU). He is the advisory board member with ODU’s Computer Science and Engineering, and Storme College of Business. Bojan holds a Bachelor of Science degree in Computer Science with a minor in Mathematics from Rutgers University and a Master of Business Administration (MBA) from Old Dominion University.