AI-Ready Data Strategies: Key Takeaways from Fortune Brainstorm AI Singapore 2024

For ambitious enterprises, AI has thrown down a gauntlet. The leaders are racing to leverage this hot new capability, while the laggards risk being left behind. Whether you’re implementing AI to personalize customer experiences, optimize inventory, or simply write emails faster, leaders everywhere face the same challenge: trusted AI demands trusted data. How can organizations rise to this challenge and get their data AI-ready?

In a recent thought leadership panel, Satyen Sangani, CEO of Alation, and Geraldine Wong, Chief Data Officer at GXS Bank, addressed this question and more. This conversation highlighted the critical role of data quality, data intelligence, and data governance in leveraging emerging technologies like AI. Here are some key takeaways from their discussion.

Video and image credit: FORTUNE

The role of data quality in AI

Sangani kicked off the conversation by addressing a fundamental issue in AI and data management: trusted data needs trusted AI. As he noted, generative AI introduces a new paradigm for data analysis. Unlike traditional data processing, which involves structuring unstructured data, generative AI feeds unstructured data directly into models, which then produce outputs in a largely opaque manner. Checking that “black box” of the model is impossible. “If you give it garbage, you're going to get garbage,” he said. “Even if you give it good data, you might get garbage.” 

Data challenges in financial services

Geraldine Wong provided a practical perspective on these data challenges, drawing from her experience as the CDO at GXS Bank. As a digital bank with a focus on financial inclusion, GXS faces unique data hurdles. “[We need to] leverage data to do things differently in terms of pricing, credit risk scoring, as well as enabling financial inclusion and financial mobility to customers who today might not be served by local banks,” she clarified.

Wong explained how the bank leverages alternative data sources, including financial behavior data from Grab and Singtel, to build credit risk models where traditional data might be sparse. She shared, “When we started off as a new bank, we had to work closely with Singtel and Grab to use them as proxies.” This creative approach allows GXS Bank to offer financial services to underserved populations by creating reliable credit profiles from unconventional data sources.

Learn more: Data Governance in Banking: Benefits and Use Cases

Building a strong data culture

As one of the first employees to join GXS, Wong had the chance to build a data culture from scratch. She shared her experience of establishing that culture at GXS Bank during the challenges of remote work. “As new people came on board, I think what I experienced was their understanding of what the data team does for them, what analytics does for them, what data science is for them. It's all varying levels of maturity.”

“It was a lot of educating, meeting with them to understand what their needs were, the expectations were, but also creating an operating model to say, hey, what does the data team actually do? How can it benefit you?... And also being able to assess their level of maturity was very important for us.”

Wong focused on teaching new employees the value of data and democratizing access through tools like Alation. Wong remarked, “Tools like Alation help users search for data definitions and build trust in the data they see.” This accessibility helps embed data-driven decision-making across the organization.

When asked what advice he’d give a business leader seeking to build a data culture, Sangani emphasized the importance of aligning data initiatives with business goals. He advised data leaders to tie their work to clear, measurable outcomes, noting, “The best companies tie their outcomes to value very quickly.” This approach ensures that data initiatives impact key business problems, building trust in the data team across the organization.

The potentials and pitfalls of synthetic data

Synthetic data, which mimics the patterns of real-world data, offers a solution to the challenge of insufficient data. Sangani discussed its potential benefits, noting that synthetic data can help overcome data shortages by creating data that mirrors the structure of real data without revealing confidential or private information. “In a world where we are looking for more and more data and these models are so voracious and their appetite is so big, it is a helpful thing to have,” he noted.

However, he cautioned that the effectiveness of synthetic data depends on how it is generated and used. “Synthetic data certainly has a place... but it depends on how the transformations occur,” he explained. 

Data governance in emerging markets

Wong also addressed data governance challenges in emerging markets. In regions where traditional data infrastructures are less developed, establishing robust data-sharing agreements and ensuring data privacy are crucial. “Having proper data sharing agreements, including consent and audit mechanisms, is essential,” Wong said.

“Who collects consent as part of this whole customer journey is really important as well,” she added. “I recall days and nights poring over that document, but operationalizing it is equally tough because each organization has their own mechanism or channel for which data is being shipped,”

Looking Ahead

As the conversation wrapped up, both Sangani and Wong expressed optimism about the future of data intelligence. Sangani acknowledged the rapid pace of technological advancements and the challenges of keeping up. “There’s just tons of excitement in front of us,” he said, noting that the journey of integrating and utilizing new technologies like AI is just beginning.

Wong, too, sees potential for further transformation through AI, particularly in enhancing workflow efficiencies. “There’s a lot of GenAI being done in a very task-level manner that should be expanded into a more enterprise workforce impact type of flow,” she noted, highlighting the ongoing evolution in how organizations can leverage data and AI.

In summary, the discussion underscored the critical importance of data quality and governance in the age of AI. As organizations navigate these challenges, aligning data initiatives with business goals, leveraging alternative and synthetic data, and fostering a strong data culture will be key to harnessing the full potential of data intelligence.

Curious to learn how Alation can help you up-level your own AI and governance strategies? Book a demo with us today.