Riding the Third Digital Wave — A Comprehensive Guide for Data-centric Transformation

I’m not a beach person, but when I was in Hawaii, I went surfing. While I got up a few times, I mostly missed catching wave after wave and instead watched expert surfers crush their rides.

Waves provide a great analogy for change cycles in the technology market. Once waves are generated, they move to the shore, picking up energy until they break.

They layer and crash into each other, creating unique patterns, and keep coming, one after another, across great distances. And, like the expert surfers, those who have trained to see these cycles developing and prepare for them are able to harness the energy, propelling themselves forward and through. 

In part I of this series, we covered what is propelling public sector entities toward another wave of modernization and the first principles to consider. In part II, we present a model for understanding the third digital wave, a maturity model for the data-centric organization, and a start on how to navigate it.

A brief history: The waves of digital transformation

The multi-decade digital transformation we are in didn’t happen all at once — even with progressive introductions of more powerful compute, storage, networks, and software capabilities. It happened in three distinct waves that represent transformations within enterprises defined by organizational adoption.

As each wave brought new advancements into the market, organizations had to change their design to integrate new capabilities.

The enterprise-centric wave came first and was distinguished by application development in tightly coupled software and hardware. Systems tended to be closed and proprietary in the first decade (e.g., IBM), until the market moved to multi-vendor and finally open-sourced environments as this wave matured.

While enterprise resource planning systems (ERPs) were the mainstays during this phase, the enterprise-centric model required the organization to adapt to the available technology. Certainly, there were customizations of ERPs, but on the whole, companies modified their processes and adapted their skills to align with the off-the-shelf technology of the day, with limited ability to change what the market was offering.

This all changed as hardware and software became decoupled. Then the internet, mobile, and eventually the cloud drove incredible market growth creating new incentives for specialized software and further reducing the cost of capital-intensive IT systems. At the same time, the number of IT human capital skyrocketed.

All these factors ushered in the application-centric wave starting in the early 2000s. This wave flipped the technology-business paradigm. Now, software could be designed to align with the enterprise, informed by strategy, portfolio, and processes attuned to business and customers. This wave democratized software development for enterprise B2B and consumer (B2C) applications and put high-quality software development tools into the hands of end customers. 

Enterprises reorganized to value and support the creation of applications. Critical leadership positions like CIOs and CTOs became commonplace. Resources and technical skills were consolidated into shared services and efficient development methodologies like Agile and DevSecOps became the norm.

The peak of the application-centric wave was the advent of the low-code/no-code movement, massively democratizing the ability to develop websites.

Today’s Third Wave

This leads us to the third wave of digital transformation we see building: The data-centric wave. The data-centric wave shifts the center of value from applications to data. It recognizes that data is an asset class in itself, not specifically tied to the collection or creation mechanism. Data’s value is the organizing principle of the business model, core processes, and enterprise architectures. Getting the data component correct is critical to best deliver services and accomplish the mission.

This does not mean that all data is literally centralized, however as the enterprise architecture is abstracted to higher levels, most organizations will see a consistent, connected, and secure data layer emerge as the foundational infrastructure. This data layer incorporates the three principles we presented in Part I of this series.

The evidence of this data-centric wave forming can be found across various indicators. First, there are the capital markets. Enterprises and even country valuations are increasingly affected by data ownership instead of relying on revenue, cash flow, EBITDA, or other financial metrics. Many data-reliant investments continue to hit the markets. Goldman Sachs estimates AI/ML investments to be $200B by 2023.

Second, is the reliance of business capabilities directly tied to data. Besides AI/ML, dashboards, data-driven decision-making, IoT, simulation, quantum, and numerous other advances only work, or work as well as their data. I’d challenge commenters to see how many business functions we can list that do not use any data.

And third, organizational changes are already shifting. Our institutions around the world are putting critical leadership in place – notably, the creation of Chief Data Officer, Chief Analytics Officer, Chief Data and AI Officer, and other position variants – designed around data.

A 2021 study from PwC found that 21% of the top 2,500 largest publicly listed companies in the world had a CDO — and almost half of those CDOs were appointed in 2019 and 2020 alone. The existence of CDO Magazine is evidence of the growing momentum of this wave.

Are we ready to ride the wave?

Where does your institution stand as a data-centric org? Are you organized correctly or positioned to utilize your data? Are the right people and policies in place to surf this wave for the next decade or two?

Below is a maturity model we developed to help customers understand their pathway and help guide strategic growth decisions. The key questions in the data maturity model are: Where is my data? How is it organized? And who manages the data (both control and governance)?

There is a less apparent nuance baked into this model. The devil in the details will be the resource balance across those with high domain expertise and high technical expertise. Getting that organizational design correct across people, processes, and technology is paramount.

First step of the journey

So where does a humble CDO start as the sponsoring leadership in this third wave of digital transformation? The first two steps are getting off legacy technology and developing incentives that drive collaboration across a pervasive data layer. 

The first step is self-explanatory. An EY survey of government executives showed that 67% said their infrastructure could not support emerging technologies (think AI). The solution to this step is to adopt a modern data system that bridges existing systems and mobilizes data across a wide range of sources (including multi-cloud) so that data can be joined with data, and applications or workloads can be efficiently moved to data.

The second step is wholly focused on nurturing the value of the data ecosystem. I like to use the analogy of gravity when visualizing data value. To increase gravity (and build value) we have to increase mass and that is done one of two ways. The first is to increase more volume, meaning get more data. The second is to increase density. Density in data comes from the interconnectivity between your data sets. In both instances, the more you can do to increase collaborations and sharing across your data sources, the more valuable your data becomes.

Both of these steps can be done incrementally and progressively. And like any good organizational transformation, it is as much cultural as it is technological.

In this article (part II), we provide a framing and context to understand the third wave and some practical models and initial steps. In the prior article (part I), we talked about the drivers and first principles of this digital transformation. In part III, we’ll discuss the importance of having a point of view on change and the north star by which we navigate our way forward and through the wave.

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.