There’s little disagreement over the vital role played by data management in getting the most value of artificial intelligence (AI) applications. Without good data, there will be no good outcomes; or, as the popular saying goes: if you put garbage in, you’ll get garbage out.
It’s a principle to which Peter Ku, vice-president and chief industry strategist for banking, capital markets and financial services at automation technology provider Informatica subscribes.
There is a catch, though, Ku argues. Training large language models (LLMs) and machine learning (ML) processes requires human expertise. But what happens when the gifted minds behind the data management processes that enable that are no longer in the industry? From where will organisations source the necessary expertise at a time when few colleges or companies are investing in teaching the skills that today’s experts will take with them when they retire?“The demand for proper data management and governance of the data to build and train and deploy these models is growing exponentially,” Ku tells Data Management Insight. “But the supply of talent, meaning people to fulfil that demand, is not.”
New York Summit
The answer, he says, is technology. And that means, of course, AI.
While data management and data governance has traditionally been very labour intensive – “smart people trained over years learning how to use special tools” – much of that can now be automated using the very same technology it’s intended to harness.
“The downside of not looking at solutions that are powered by generative AI (GenAI) for data management and data governance really is that you won’t be able to fulfil the demand for fit-for-AI or fit-for-business-use data quickly enough or efficiently enough – everyone’s competing for those really smart people in the industry, half of whom are going to retire in five year.”
This gap between capabilities demand and enablement supply will form the core of Ku’s keynote address at A-Team Group’s Data Management Summit New York City on 26th September. The full-day event will gather the leading vendors and consumers of data and data management services in A-Team Group’s annual look of the state of the financial data industry.
In a speech entitled “Transforming data management and data governance with GenAI and LLMs”, Ku will also take a deep dive into the use cases for what he terms “this exciting thing called GenAI” and examine the challenges to, and business value of, its implementation by financial institutions.
Recruitment Woes
The brewing data skills shortage within the financial sector is a topic that is occupying the minds of chief executives and chief technology officers alike. It’s particularly acute in the sustainability data space, which suffers also from being a novel domain lacking even the long-term expertise that can be found within the conventional data sector.
A recent report from Encompass, a provider of automated corporate know your customer solutions, illuminated the challenge, revealing that two-thirds of decision makers surveyed said they lacked digital skills and maturity. A similar proportion said their organisations lack the skilled technical resources to integrate external data into core platforms, while 57% indicated they have only the most basic digital maturity. Just 16% rated their digital maturity as advanced and 26% rated it moderate.
To help companies plug the gap, Informatica has built a suite of LLMs, ML models and natural language processing (NLP) technology.
“At Informatica we are taking models with GPT capabilities to ask the Informatica solution to do the things that traditionally human beings would have to click-and-drag and learn how to use our software,” he said. “That’s a gamechanger.”
In the Loop
The question of retaining human expertise also touches on the longer-term question of what role people will play once AI is fully implemented throughout the economy. Despite dire warnings of “machines taking our jobs”, as Ku put it, the Informatica executive still anticipates a crucial role for human talent in data security functions such as validation and testing AI-generated outputs.
He expects there will be an uncomfortable transition period as the industry tests the potential of new technologies and jobs are shed. He’s confident, however, that AI will create new employment opportunities as companies find ever more innovative ways to harness the power of AI technology.
He also predicts that as regulators grapple with how to put guardrails around AI, they will continue to insist on human input in the data loop.
He argues, however, that a greater threat to jobs would be posed by companies’ reluctance to adopt AI. Already, he says, he sees organisations that are over-cautiously holding back on AI implementation for fear of its impact on operations. If too few are bold enough to take the plunge the opportunities presented by the technology may be missed, he says.
“We’re at this point of no return,” Ku says. “Either these technologies are really going to help, or organisations will not adopt them and they will be at risk of not meeting that skills demand curve.”
- A-Team Group’s 14 annual Data Management Summit New York City will be held on 26 Click here to find out more or sign up for attendance below.
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