Why Data Isn’t the New Oil

Are we in an AI hype cycle? I’m not here to answer that but most of us in the technology space feel that rapid advancements, writ-large, are building up to a massive tipping point. We certainly have to be careful about over-rotating before the full impact of innovations can be seen. However, the possibilities are pointing out very clear and present gaps.

At a time when many public sector organizations are in the throes of modernization, we are yet again presented with the next change.

Are we ready to adjust fire, again? I speak often with public sector leaders, CDOs, CIOs, CTOs, and top technologists around the world. Everyone is feeling the push and pull of digital transformation, but this time a third wave. What is different this time? It’s all about data. (Why this article was specifically written for CDO magazine).

In this three-part series, I will explore the circumstances we feel today (part I), a framing from which to understand the third wave (part II), and the importance of a viewpoint to navigate/ride the third wave (part III).

Push and pull

If we think about push and pull, there are systemic factors underlying each. Pushing us to change are the legacy technologies, the technical debt, and the budgetary landscape. Much of the technology we run on today literally has its entire generation of knowledge workers retiring. While the human generation has aged out, the hardware and software are likely 7+ generations behind. Take the US government as an example. The oldest systems a GAO report identified are all over 60 years old today.

We’ve done everything to keep these systems running despite exponentially growing costs to maintain. We’re out of runway. Add to that the technical debt incurred over the years between cost tradeoffs, bolt-ons, and changing requirements. Organizations often realize greenfield strategies make a compelling business case, despite the huge lift. I’m not advocating starting from scratch, only pointing out that the tradeoff has grown to the extreme where the equivalent of a natural disaster reset is now a viable option.

On the flip side, preparing for the future draws us into change. Citizens demand change in their services, especially when contrasted against their commercial experiences. New capabilities to deliver missions, like AI, simulation, and quantum, often assume modern technologies underlying their support structures. And great power competition globally recognizes the need for advanced capabilities, setting a purpose and pace for modernization.

Tech and operations

Let’s unpack these drivers from a technical and operational perspective and extract some key principles — those that every CDO will appreciate. The tech blockers for developing a data ecosystem start with infrastructure, both the age of the technology and the enterprise architecture and design. We cannot leverage, at scale, capabilities like AI if the design of our infrastructure is not purpose-built for the data ecosystem. While individual designs will vary, the first principle is:

1. Architectural design and technology choices must be based on data, and the value of data, as the central organizational principle

The next blocker is around the network effect of data. Collaboration is a key supporting pillar to increasing the value of data. The easiest example of a common blocker to collaboration are data silos. But successful collaboration goes beyond the removal of silos.

To build a healthy data ecosystem, data needs to be interconnected and people need to be able to collaborate with ease across data sets. This requires technology to not only provide ease of connectivity but additionally, fine-grained security. There is no collaboration to manage the risk. These two elements are critical to developing a culture of collaboration. For those hyper-concerned about security, even the myth of security via data silos is being challenged. This brings us to the next principle:

2. Security needs to be at the data level

Say your organization does all this and builds a tremendous multi-model data cloud architecture. Success will depend on the data ecosystem, which goes beyond the data to include users. Technology needs to be democratized to include as many stakeholders as possible.

Technology choices affect democratization, but critically the upskilling of the workforce and data literacy are key investments. Managing budgets across business units, sub-units, programs, and projects is as critical as managing data across this highly interdependent ecosystem.

Properly managing resource constraints ensures a vibrant data ecosystem full of collaboration and optimized value. Finally, the pace of innovation requires adaptability to allow technology to flow in and out as tools and capabilities change. Active readers will notice I speak of data in terms of ecosystems because, in nature, they are complex adaptive systems. This leads to our final principle:

3. Treat data as part of a holistic ecosystem

In this article (part I) we explored this driving sense of a third digital transformation happening today. This third wave seems to be coming right on top of the second, but ideally unpacking some of the drivers into principles sets us up for success. The next article will provide a framing and context to understand the third wave (part II), and the final article (part III) will provide ideas on how to navigate/ride the third wave.

To wrap up, I like to tell folks that there is a shift in mindset necessary moving forward. We need to change our view of data as oil (the old analogy) to seeing data as infrastructure. That shift in thinking will make the logical next steps apparent.

About the Author:

Winston Chang is CTO, Global Public Sector at Snowflake Inc. He is an expert in data-driven organizational transformation, AI/ML, and innovation in public sector ecosystems. His over two decades of work encompasses startups, IT modernizations, fashion branding, AI/ML/Blockchain prototyping, structured finance, military service, and more.

Chang volunteers his time with the NIST MEP Advisory Board and the Eisenhower Fellowship network. His engagement in both organizations supports global bridge building and strengthening US economic drivers. Winston graduated from the United State Military Academy and holds a personal mission to help government and educational institutions leverage data for maximum societal impact.