On September 15, 2008, Lehman Brothers, the 4th largest investment bank in the United States, filed for bankruptcy. The bankruptcy triggered a 4.5% drop in the Dow Jones Industrial Average, the largest drop since the attacks of September 11, 2001. In the days and weeks ahead, the Federal Reserve assumed control of American International Group (AIG), and Merrill Lynch, then the 3rd largest U.S. investment bank, was acquired by Bank of America to avoid bankruptcy.
In the wake of the financial crisis of 2008-2009, banks and other large companies undertook measures to decrease their risk and ensure better financial controls. One resulting action was the establishment of the role of Chief Data Officer (CDO), or Chief Data and Analytics Officer (CDAO) as the role has come to be known at many organizations. Although some early innovators had dabbled with the role prior to 2008, the CDO/CDAO role in its present form was rooted in response to the need by financial services and other leading companies to better manage and understand their data, initially to comply with regulatory and risk management requirements. Today, fifteen years after the financial crisis, 82.6% of leading companies have appointed a CDO/CDAO.
The CDO/CDAO role has evolved over time as companies have progressively transitioned from defensive functions related to risk and regulatory oversight to offense-driven efforts like business growth, customer acquisition and retention, cross-selling, and online servicing. Activities requiring advanced analytics, machine learning, and artificial intelligence (AI) have been incorporated into the CDO/CDAO mandate. The CDO/CDAO function has been transformed at many companies from being a technology role which reported to the Chief Information Officer (CIO) to a business function reporting to senior business leaders. Currently, 43.3% of CDO/CDAOs report to the COO or CEO.
Though the CDO/CDAO role is now ubiquitous, CDO/CDAOs continue to face challenges. Many CDO/CDAOs have been remarkably successful and happy during their tenures. However, turnover in the CDO/CDAO role is high, and progress has been slow to materialize for most companies – only 39.5% are managing data as a business asset, 23.9% have created a data-driven organization, and 20.6% have established a data culture. Just 35.5% believe that the CDO/CDAO role is successful and well-established, and 40.5% say the role is well understood in their company. Fifteen years after its establishment, the CDO/CDAO role is a work in progress.
Why haven’t data and AI transformation efforts progressed faster for many leading companies? Business transformation of any kind is never easy, and this is especially true for legacy companies, which constitute the core of the Fortune 500. In a recent article, The Economist presents a hypothesis, painting a legacy perspective of the mainstream business economy. It notes that only 52 Fortune 500 companies were founded after 1990, so that nearly 90% of the Fortune 500 is comprised of legacy firms. Since 1990, the average age of a Fortune 500 company increased from 75 to 90 years. The average age of Fortune 500 banks is 138 years old.
While innovation leaders Alphabet (Google), Amazon, Apple, Meta (Facebook), and Microsoft invested a combined $200B in R&D last year, this represents 30% of the total of R&D business investment, suggesting that investment in innovation remains heavily concentrated. The Economist concludes, “Inertia has slowed the pace of competitive upheaval in many industries, buying time for incumbents to adapt to digital technologies. The digital revolution has not been all that revolutionary in some parts of the economy”. For legacy companies, the gradual pace of transformation and change is reflected in the slow progress in building data-driven organizations and data culture.
There is a belief that the emergence of Generative AI will unleash a wave of disruption comparable to the arrival of the Internet and will accelerate data and AI transformation efforts. The Economist is less enthusiastic, noting, “Recent breakthroughs in artificial intelligence (AI) have left many corporate Goliath’s nervous. Yet America Inc has experienced surprisingly little competitive disruption during the Internet age. Incumbents appear to have become more secure, not less”. A recent IDC/Teradata industry survey reports that 57% of executives believe that interest in Generative AI will fade.
Adoption of Generative AI, when it comes, will likely result in renewed attention to AI ethics and data privacy. Just 23.8% believe that industry has done enough to address data and AI ethical concerns, and only 40.2% of firms have well-established AI ethics and data responsibility policies and practices in place. Cameron Kerry, The Ann R. and Andrew H. Tisch Distinguished Fellow at The Brookings Institution, is a leading advocate for privacy legislation that can help address AI expansion, a topic that he has written extensively on. Kerry notes, “As artificial intelligence evolves, it magnifies the ability to use personal information in ways that can intrude on privacy interests by raising analysis of personal information to new levels of power and speed.”
Current economic uncertainty has renewed pressure on companies to deliver measurable business value from their data and AI investments. Some companies are engaged in reevaluating and reassessing the CDO/CDAO role – the need, its structure, its reporting relationship, and its mandate. These companies recognize that the CDO/CDAO role remains a challenge for both the company as well as for the incumbents who occupy the role — difficult to be successful in, stretched across multiple constituencies, often misunderstood, a periodic target of resentment from peer executives wary of the new kid on the block, new to the C-suite, and too often set up to fail with expectations nearly impossible to meet.
Where do we go from here? Are companies ready to name an analytics leader or an AI visionary to lead the CDO/CDAO function? Do companies want to see responsibility for AI sit within the CDO/CDAO organization? Some companies are turning to accomplished and savvy business leaders to fill the CDO/CDAO role, seeking someone who is highly conversant in communications, storytelling, building alliances, muscling support, selling ideas, operating in the C-suite, and driving results? These companies are asking themselves whether they want to continue to elevate subject matter experts in disciplines such as data governance and data management, as has been the blueprint for many organizations in the past, or they now looking for something different.
I am often asked what the future of the CDO/CDAO role will look like. Will the role continue to exist? Innovation leaders like Apple, Amazon, Google, and Facebook do not have a CDO/CDAO role within their companies? In 2020, Tom Davenport and I wrote about the 7 roles of the CDO/CDAO, ranging from data architect to data ethicist, and in 2021 about the short tenures of CDO/CDAOs. In 2023, we have a new variation, Chief Data, Analytics, and AI Officer (CDAIO). Still, these roles remain tenuous, with the average tenure appearing to get shorter.
Some leading companies have put the CDO/CDAO role on pause as they rethink their data and AI leadership needs. Seasoned Fortune 1000 CDO/CDAOs are choosing to opt out, focusing on new roles and new ways through which they can help organizations navigate the complexities and opportunities created by data and AI. Accomplished former CDO/CDAOs like Allison Sagraves (in co-authorship with me) and Heidi Lanford have offered fresh perspectives based on their respective tenures in the role.
While the progress of data and AI adoption has been slow, the need for data and AI leadership is greater than ever. The volume and variety of data available for capture and analysis continues to grow. Opportunities to use data and AI to differentiate companies and personalize the customer experience are increasing. Generative AI poses opportunities and challenges. Will the gradual pace of business transformation and change continue to be a barrier to data and AI progress, as The Economist says?
History teaches us that change is rarely easy. Transformation plays out over a period of years, and decades. While progress in data and AI has been slower than might have been hoped for, business understanding and technical proficiency in data and AI will only grow. Inevitably, the demand for data and AI leadership will increase. Progress will require fresh thinking, new perspectives, determined patience, and sustained commitment. Fifteen years after the 2008 financial crisis, we are still learning how to manage data and AI.
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