Modern chief data officers (CDOs) – and, more recently, chief data and analytics officers (CDAOs) – have an unenviable task.
They are the gatekeepers of their organisation’s chief asset; its digital information. In the age of artificial intelligence, the importance of their role has been elevated and made more complex as the risks posed by the quality, neglect or misuse of that data has multiplied.
Fortunately, CDOs are in a strong position. Over recent years they have overseen a transformation within their departments that began even before AI became indispensable. They acquired exceptional data management skills as financial institutions rapidly digitalised, enabling them to prise value from data that would drive cost-saving automation programmes and revenue-generating investment and analytical packages.
These are talents that are essential for ensuring not only that the data they deploy in highly sophisticated new technologies is accurate and complete, but also that the applications to which they deploy it are safe and their outputs trusted.
Naturally, such a transformational period in the evolution of the CDO is not happening without challenges, according to Julia Bardmesser, of the New York University Stern School of Business and founder and chief executive of Data4Real.
“Across my conversations with data and analytics leaders, two problems keep surfacing: shadow AI development that outpaces governance structures and real ambiguity about who in the organisation owns accountability and what constitutes value once an AI initiative is underway,” Bardmesser told Data Management Insight.
“Business leaders want AI everywhere, and that appetite is generating a lot of activity – pilots, tools, experiments – without a proportional amount of results. Meanwhile, CDOs are often getting viewed again as a purely regulatory and risk management function, a defensive position that data leaders spent the last decade trying to overcome.”
Expert Panel
The resulting “gap, between enthusiasm and execution, and between where accountability and value should actually sit” will form the core of the Data Leaders Panel session at this year’s A-Team Group Data Management Summit New York City later this month.
Now in its 16th year, DMS NYC returns on September 17 to explore how to use data and AI to drive measurable business outcomes – reliably, repeatedly and at scale.
The opening panel discussion, entitled The Evolving Data and AI Operating Model – Synergy, Governance and Accountability, will be moderated by Bardmesser.
Offering their thoughts will be Yinghua Michelle Zhou, head of finance data and AI at State Street; Richard Chudley, CDO for SG Americas at Société Générale; Andrew Foster, CDO at M&T Bank; Amy Kabia, head of wholesale client data strategy at Wells Fargo; and, David Zimmerman, CDAO, Americas Division at SMBC Corporation.
Data View
With the roles of the CDO and CDAO having become pivotal to the deployment and safe management of AI and AI agents, focus has turned to the principles by which they work as well as the practicalities.
Their offices are responsible for maintaining a view of data that ensures it is dependable and reliable enough to be used in AI. Ensuring a robust governance structure is an essential ingredient in this.
“AI is only as trustworthy as the data beneath it – without a governed foundation, it stalls in pilots; with one, fragmented data becomes fuel for scale,” State Street’s Zhou told Data Management Insight.
“The real shift is treating data and AI governance as one conversation, not two. This means designing shared accountability for inputs and outputs before an agent goes live, keeping data-quality work funded as a first-class priority, and repositioning data leaders from gatekeepers to enablers who set enterprise standards while empowering business lines to innovate safely.
“Synergy, governance and accountability aren’t competing goals; they’re three legs of the same stool,” Zhou added.
Future Operations
The rapidly changing nature of data management and AI means, also, that the CDO must operate with an eye on the organisation’s future data-led operations. Companies cannot stand still technologically but driving innovation tomorrow will require investment in expertise and skills today.
With many organisations professing difficulty in finding suitable new talent to drive their AI ambitions, CDOs are having to address a lack of data literacy within their organisations.
But the challenge isn’t simply one of overcoming a lack of understanding of how to use data effectively – it’s also about preventing erroneous use or deliberate misuse, said Société Générale’s Chudley.
“Data literacy has always been a focus for the CDO and AI literacy is playing an increasingly important role,” Chudley told Data Management Insight. “The most effective programme is role-based; as senior executives will focus on strategic opportunities, business users need to understand responsible AI use, and citizen developers need a deeper technical understanding.
“With both data and AI, the biggest challenge is to highlight what is expected in a professional corporate environment, especially in a regulated industry such as financial services. Many individuals use AI tools in their personal lives and freely share personal and confidential data that would not be allowed in a professional environment.
“Reminding users of the boundaries that need to be adhered to is a critical aspect of both data and AI literacy.”
- Data Management Summit New York City will be held on September 17 at @Ease, 7th Floor, 605 Third Avenue, New York. Attendance can be reserved by clicking here.
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