In its early days, analytics was very much the secret ingredient behind the offerings from leading technology companies. As firms like Netflix and Amazon experienced explosive growth, however, analytics went from a niche need to an imperative for transformative leaders. In a very short time, what was once an outlier practice has found itself under an intense spotlight for the practitioners themselves– the CDAO.
Rapid advancement in AI has created more demanding conditions under which CDAOs must operate. It is easy to assume that this function demands an analyst or technologist. However, as we can see in the DataIQ 100, the most effective data leaders today excel in change management, influence, and driving business value more than any technical mandate. Unfortunately, when we zoom out from the industry leaders, we see that 62% of all CDAOs are reporting great difficulty in building a data-driven culture.
CDAOs are under immense pressure to prove their value sooner rather than later. What can emerging leaders learn from DataIQ’s Top 100? We have analysed the key traits and characteristics of these leaders to understand what makes them tick. Keep reading for a roadmap to becoming an effective CDAO.
Want to dive into the data yourself? Check out Blend’s US and EMEA Dashboards for DataIQ’s Top 100 here.
Shifting from a quantitative to a qualitative approach
Gartner stresses the importance of CDAOs hitting the ground running with a plan for their first 100 days in the office. This urgency is due to the brevity of the CDAO role. Historically, with a tenure of only two years, this is the most volatile title in the C-Suite.
Analysts have differing opinions on whether this is just a temporary role or one that will endure long-term. Senior leaders are also impatiently seeking to deploy artificial intelligence across their business, with nearly three quarters of them expecting genAI to have a large impact on their organisation. The scrutiny is intense – you need to rapidly prove the value of your position before it potentially gets eliminated.
Yet, at the same time, you are battling imposter syndrome. AI capabilities, like large language models, are evolving at a blistering pace and what seemed cutting-edge just months ago can feel outdated. How can you position yourself as the transformation leader when the ground is constantly shifting beneath you?
The data from our analysis shows us that even the Top 100 skew towards being much more analytical and data driven. The majority of the leaders on DataIQ’s list find themselves among the Questioner archetype (a combination of the C&D DiSC Style). These folks are usually keen on ensuring success prior to even beginning. This is primarily a result of the D Style traits that data leaders have a tendency to over-index on. However, it is when these leaders lean into the other side of the Questioner archetype, the C Styles, that they are able to balance out the analytical side with a more qualitative approach.
From this analysis of the Top 100, we observed that a CDAOs strength lies in understanding where on the personality spectrum they naturally land, and then adjusting their tact based on; their natural leadership style, who they are speaking with, and what stage of maturity their D&A programmes are at.
Click through the personality wheel on the Blend dashboards to learn how you can build better business relationships with the different archetypes in your organisation.
Take your DiSC Personality Assessment here to learn how you lead
This focus on cracking quantitative problems may have been an asset in the past, but this is no longer the primary mandate. The CDAO must be driving cultural changes. Doing so successfully requires an entirely different skillset of influence, collaboration and change management that may not come naturally.
You find yourself at the intersection of warring mindsets – the impatient C-suite executives demanding rapid impact, and the risk-averse business teams deeply sceptical of disruptive technologies. Your analytical persona made you trusted advisors before. But now you need to become motivational leaders, operating with empathy and inspiration.
Establish yourself as the essential catalyst to unlock productivity, top-line growth, and bottom-line efficiencies through data-driven transformation. Failure is not an option – it is time to evolve.
Designing a new operating model for D&A
One of your first major decisions as CDAO is how to structure your function’s operating model. There are two primary paths to consider – establishing a Center of Excellence (CoE) or positioning yourself as a dedicated delivery arm.
The CoE approach focuses on building the foundational platforms, establishing governance frameworks, and driving change management. This provides strategic focus but limits your direct influence within the business units executing use cases. Making how you track and measure impact imperative as you will not own the business case execution.
Alternatively, you could embed your team as the execution arm, responsible for developing and implementing AI use cases across the enterprise. This guarantees relevance but brings its own challenges around prioritisation across different teams’ competing needs.
For example, if the HR team wants to implement AI tooling in its processes, how should you prioritize servicing them versus a sales team with similar ambitions? Once either team has their program implemented, how will you ensure each team gets enough attention amidst shifting priorities like system upgrades or legal issues?
A potential middle ground is a hybrid model – use your central CoE to build reusable technical assets like model governance frameworks and no-code AI platforms. Simultaneously, though, embed “fractional CDAOs” within priority business verticals, co-developing and driving adoption of use cases from the inside.
Using the marketing team as an example, the CDAO would partner with the CMO. Work with the CMO’s funding to create a data enablement team within the marketing function. Marketing would own the business case, the CDAO would own the technical enablers within.
Plan your roadmap for change
Forty-three percent of CDAOs report difficulty navigating competing priorities. This inevitably impacts the focus on data-related projects while hampering your ability to rise to influence. The development of a strong roadmap is easy to overlook but proves itself in value once built.
As you look to make an impact, be wary of trying to boil the ocean. Carefully select your first few use cases, as these will establish credibility and buy-in. Your primary metric here is earning business buy-in and launching production efforts successfully. There are two critical boxes your initial targets must check:
Low risk, but high value: The first use cases should be relatively low-risk from an ethical/governance perspective to secure buy-in. For example, automating contract data extraction carries far less bias risks than something like recruitment screening.
However, they must also deliver tangible value to build credibility. Here are the criteria to consider:
- Align to Business Priorities
Don’t lead with a fascinating but low-impact use case. Identify the current top priorities and pain points for each business line. Map your initial AI deployments to directly address those burning issues. This ensures strong executive-level sponsorship. - Start Lean, Then Expand
Your first use case should be relatively contained in scope. Get it successfully deployed into production, gather user feedback, and quantify the benefits. This builds trust. Then you can expand into adjacent processes and use cases. - Showcase Responsible AI
Embed ethical AI governance and transparency from day one. Don’t just treat it as check-box compliance. Actively showcase these responsible practices as a core capability that instils confidence in your AI systems.
Follow this measured approach. Once you have a few successful implementations under your belt you’ll have the momentum and credibility to scale your AI transformation across the enterprise. Blazing the new trail you can set expectations on building to ROI, but ensure you have clearly defined value goals you deliver against to ensure ongoing faith in the plan.
Conclusion: Establishing your data-driven future
The path for new CDAOs is rife with challenges – proving the value of an unproven role with a potentially brief tenure. Your greatest asset of analytical prowess may now be a liability as you try to inspire organizational change. And imposter syndrome looms as AI capabilities outpace your knowledge.
But within this crucible lies immense opportunity. By designing a hybrid operating model, mapping an actionable 100-day plan, bridging cultural divides, and embedding ethical AI practices – you can cement yourself as the catalyst for enterprise-wide data transformation.
Stand up a CoE to build technical foundations, while embedding “part-time CDAOs” within business units to co-develop and drive use case adoption. Prioritize initial deployments that are low-risk yet high-impact to rapidly build credibility. But most importantly, complement technical expertise with influencing skills to map AI to tangible business outcomes valued by diverse stakeholders.
When you showcase responsible AI as a core competency rather than an impediment, you will instil confidence. The road is daunting but exciting. It is now on CDAOs to architect this future – translating impatience into action, scepticism into confidence, and vision into transformational reality.
Authored in Partnership with Blend
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