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5 Tenets for Leveraging Chief Data Officers in the Era of AI

January 3, 2024

Data remains a company’s competitive advantage, especially in the era of artificial intelligence. So why do so many startups struggle with leveraging the skills and knowledge of their Chief Data Officers?

I recently caught up with Yao Morin, Chief Technology Officer at JLL and my fellow co-founder at InvestInData, an angel collective of 50-plus leading data executives. As the former Chief Data Officer at JLL and StubHub, Yao has the unique vantage point of a CDO-turned-CTO.

Looking at AI’s sweep of business and the conversation as a whole, “a lot of the effectiveness of AI needs to come from the data that you have,” Yao says. “How good are your data assets? Do you have a team or individual gathering those assets and working on data integrity so you can fully leverage the power of AI? What do you need to do to actually enable that person or team to be effective?”

Defining the Chief Data Officer role in the age of AI

After emerging as a C-suite role in 2002, CDOs have steadily gained influence—and yet the role remains ambiguous at many companies. According to a January 2023 survey of Fortune 1000 companies, more than 80% reported having a CDO or CDAO title and more than 60% reportedly described the role as “poorly understood.”  

Based on Yao’s experience, a CDO should be empowered to not only set a company’s data strategy but also ensure that milestones are met—if not exceeded.

For leaders grappling to understand AI, a CDO can act as that trusted advisor, or better yet, have “the power to actually execute on your vision,” Yao says. “In order to be effective and for a company to actually get the benefit of data and AI, the CDO needs to be an operator role.” 

5 ways to get the most value from your Chief Data Officer 

Once you’ve defined the role and scope of a CDO, following these five tenets can help set that person up for success: 

  1. Report directly to the CEO: Where the CDO reports into often reflects how much a company values data. If a CDO doesn’t report straight to the CEO, Yao recommends being a CTO’s direct report (versus the CIO or COO). Whereas the CIO tends to be infrastructure-focused, “the CDO needs to provide a foundational layer where everyone can leverage the data. The CDO also needs to be very integrated with the entire product and engineering organization.” After all, “the data strategy is your technology strategy; your data and technology strategy is your business strategy,” Yao remarks. “That's why we see CDOs start to move toward the technology officer role.”
  2. Centralize data orgs: According to Yao, “you can't have a decentralized organization for data because data needs to be seen as a product.” If you wouldn't have engineers and product managers be distributed, why would the data team be? 
  3. Prioritize the right data: Given that your data sets your company apart in the age of AI, “I believe that the Chief Data Officer should have the accountability—and the skill set—to  advise leadership and also make AI a reality by providing the right data assets,” Yao adds. It’s not just about the plumbing and application building done by the engineering org; data should be leveraged across the organization to drive business outcomes.
  4. Attract top talent: A CDO must also recruit the “right talent in data, science, and AI to make sure the data is leveraged correctly,” Yao says. From data engineers, scientists, and analysts to machine learning ops engineers, the ideal data org includes governance as well as quality assurance to continually monitor and “have preemptive, proactive correction of data quality,” per Yao.
  5. Influence business and product go-to-market strategy: The CDO can be a “neutral party” providing insights and analytics for making critical business decisions, including strategies for marketing, sales enablement, FP&A, and more. 

As more companies turn to proprietary data to stand out in this era of AI, those that learn how to define and best leverage the Chief Data Officer role also stand to reap the most value from their data talent.

After all, “leveraging data and becoming a truly data-driven organization are not short-term endeavors,” Yao points out. “Once you’ve put a lot of initial investment in building the infrastructure and the team by hiring the right talent, you have to maintain it. It’s a long-term journey that requires discipline.”