Action and Inaction on Data, Analytics, and AI

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AI in Action

This column series looks at the biggest data and analytics challenges facing modern companies and dives deep into successful use cases that can help other organizations accelerate their AI progress.
More in this series

The title of this column series is “AI in Action,” and there has indeed been a lot of action over the past year. Judging from the 11th annual NewVantage Partners survey of senior data and analytics executives, some trends are moving in the right direction. For example, more companies are creating senior roles to focus on data and analytics. The chief data officer role has quickly become much more common over time and across more industries; in the survey, 83% of companies have appointed a CDO or chief data and analytics officer (CDAO).

An increasing number of companies (69% in this year’s survey) are officially incorporating analytics and AI into the CDO role, and we think that’s a good idea. It’s simply too hard to demonstrate value through data management alone.

These executives do feel that they are demonstrating value. This year, 92% of CDO/CDAO and data leaders agreed that their companies had delivered measurable business value from data and analytics investments. That figure was about the same as last year’s and up dramatically from five years ago, when only 48% of respondents reported seeing a measurable return for their organizations. And there is pronounced optimism about further improvement: 98% of these data leaders said they think their companies will see a return on their investments in 2023.

Organizations continue to invest in data, with 88% reporting an increase in data investments during 2022. Data modernization was identified as the top data and analytics investment focus by 41% of organizations, and 82% plan to invest in that objective. Eighty percent of organizations will be increasing their investment in data products in 2023 (as we described at Regions Bank in a recent article), which we feel is conducive to effective deployment of analytics and AI systems. Looking ahead to 2023, even with a high degree of potential economic uncertainty, 94% of organizations are planning to increase their investments in data. These are all good things.

Inaction on the Human Front

But these improvements in the importance of data stand in contrast to slow changes — even retreats in some cases — in other areas. The human side of data continues to challenge companies, and data leaders appear reluctant to change their paradigms toward more emphasis on these issues.

Every year in recent surveys, the great majority (80% this year) of respondents report that the principal challenges to becoming a data-driven organization are human — culture, people, process, or organization — rather than technological. Not surprisingly, respondents report making little progress toward that goal. Just 24% of respondents characterize their companies as data-driven, and only 21% say that they have developed a data culture within their organizations.

Yet the focus of data executives in the survey is overwhelmingly on nonhuman issues — data modernization, data products, AI and machine learning, data quality, and various data architectures. Less than 2% of respondents ranked “data literacy” as an investment priority.

Only 21% of surveyed data executives say they have developed a data culture within their organizations.

Outside the survey, in the companies we encounter through research and consulting, we see few efforts to create different cultures or behaviors relative to data. Even data literacy programs — which are typically quite generic and often transmitted through boring online courses — aren’t getting the message across. Few companies have any specific roles devoted to culture or behavior issues involved in data, analytics, or AI. Our searches on LinkedIn for “data-driven culture czar” or the more prosaic “director of data-driven culture” yielded no jobs or incumbents.

Not All In on Culture

One of us (Tom) was recently doing research for a new book (All In on AI) about companies that are aggressively using AI in their businesses. In his research, he came across a university-based research institute that had recently been given many millions of dollars to investigate how machine learning could facilitate the areas of science it was exploring. When Tom asked the institute’s leaders what issues might prevent them from achieving this goal, they all mentioned culture first. They said that AI people — typically computer scientists — and scientists from other domains often don’t speak the same language, goals, or success criteria.

When asked what they planned to do about these cultural issues, the leaders were uncertain. When Tom suggested that a cultural anthropologist or other type of social scientist might help them to diagnose the cultural issues and recommend solutions, they seemed to like the idea, but only in a limited fashion. One of them suggested that it might be a good idea to turn a Ph.D. student in that field loose on the organization to uncover some issues. Others seconded that approach.

This institute does world-class research — and they wouldn’t dream of putting a computer science or chemistry graduate student in charge of research in those areas. The institute would only employ world-class scientists in such roles. And we guess that they didn’t even pursue the graduate student suggestion; Tom volunteered to help them in the search and never heard back.

That’s just one example, but it illustrates what we’re witnessing more broadly and what the impediments are. Until we take active steps to manage these human issues, we are unlikely to make substantial progress on them. This is probably the reason why many companies — even large businesses with enormous technology budgets — do not seem to become more data-driven over time.

One large bank with which we are familiar, for example, spent several billion dollars on information technology in 2022. About half of that was spent on implementing new digital products and services and the technology required to run daily operations. A few billion went to technology for specific business units. The rest was spent on infrastructure modernization, digital transformation of business processes, and scaling up existing platforms. These are not unreasonable areas for expenditure, but culture change was not a budget line item.

One might think that this bank and other large companies could find a little dough to spend on addressing the “principal challenge to becoming a data-driven organization” — the executives who make decisions on technology and the human employees and customers who have to use the technology, data, analytics, and AI in order for it to be useful. Until these issues are explicitly addressed, we are unlikely to find that the money spent on information and technology is producing the desired returns.


AI in Action

This column series looks at the biggest data and analytics challenges facing modern companies and dives deep into successful use cases that can help other organizations accelerate their AI progress.
More in this series

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