Chief Data Officers are ultimately judged on the business outcomes they deliver. But with the shortest average tenure of any role in the C-suite, it’s crucial for CDOs to understand and put into action concrete measures that deliver value to the business.
So, how can CDOs actually deliver against this fairly open-ended objective? What are the general areas worth prioritizing regardless of how value will be realized? How much time, money, and effort should be spent on different areas in pursuit of delivering value? Where should a CDO focus if they are to survive in their role?
The answers to these questions are highly dependent on the context of the environment in which the CDO is operating. Consider the impact that any of the following would have on the needs and priorities of a CDO:
This white paper provides a view on what are likely to be the common truths for many CDOs and practical guidance on how to prioritize capabilities that are strategically important — while also addressing tactical needs.
Large organizations have the most to gain from getting it right. They tend to have very large data footprints, extensive commercial ecosystems, and expansive data investments that need to demonstrate a financial return.
CDOs will already understand the nuances of those six areas in the context of their business. So rather than delve into them in detail, let’s instead acknowledge that any of these in isolation generate complexity through their diversity.
CDOs must contend with diversity of the data itself, the users they need to serve, the technologies involved, and the organizational boundaries they need to cross:
Much of the focus for solving complexity has been placed on technology. The reality is that no single technology can service the diversity of data and users at play or effectively transcend organizational boundaries. Perversely, efforts to remove complexity have resulted in further compounding the issue. The primary example of this has been the push towards data centralization. This approach has been criticized for offering a solution aligned to — and dependent on — a specific technology provider. It’s also incredibly expensive.
More recently, federated and decentralized data architectures like data fabric and data mesh have gained support in trying to address complexity rather than remove it. However, these approaches have been criticized for lacking tangible recommendations, leaving much to the would-be implementer to navigate and solve. Additionally, much of the technology that would be required to make that a reality at scale, and across complex environments, is nascent and continues to evolve. Consequently, most organizations already have a hybrid architecture and will continue to have one regardless of their current preference.
Let’s start by exploring the challenges created by the diversity of data, users, technology, and organizational boundaries and considering some practical solutions…
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