When a company operates across 88 markets, diversity can be both an advantage and a burden. For the 126-year-old Suntory Beverage and Food International, that tension surfaced in the gradual build-up of data silos.
Its 11 business units had developed a patchwork of systems, each holding its own view of the world. Sales data sat in one place, weather information in another and macroeconomic indicators elsewhere. Instead of a single view of the market, teams were piecing together fragments, often with limited visibility into shifting consumer preferences.
“The tipping point came as consumer expectations began evolving faster than our ability to respond,” Bharathi Viswanathan, Suntory Beverage and Food International’s chief digital and information officer, tells The Edge Singapore. What the company needed wasn’t more data but a way to make sense of what it already possessed.
The infrastructure pivot
Unlike many companies that rushed into generative AI pilots, Suntory chose to fix its data foundations before deploying advanced AI tools. It unified information on a single platform, selecting Databricks as its core data and AI partner and adopting the Databricks Data Intelligence Platform on Microsoft Azure.
The choice reflects broader market confidence in Databricks, which recently raised more than US$4 billion ($5.16 billion) at a US$134 billion valuation and surpassed a US$4.8 billion revenue run-rate, growing more than 55% y-o-y. The appeal for Suntory, however, was not just scale but alignment on how enterprises should approach AI transformation.
See also: Thailand's Bitkub turns to Tencent Cloud to strengthen digital asset services
“Unifying our data and AI environment is a strategic move to eliminate barriers and accelerate decision-making. It’s about creating a boundaryless foundation for intelligence that would help local teams act quickly,” says Viswanathan.
The approach aligns with Databricks’ view of how large enterprises should begin their AI journey. “The first step is centralising all enterprise data on a unified platform with strong governance and security. Global organisations should prioritise data sources where they possess unique information or exclusive customer relationships, giving them a strategic advantage as they build AI solutions,” says Cecily Ng, Databricks’ vice-president for Asean and Greater China, in the same interview.
For Suntory, that meant integrating sell-in and sell-out transactions with weather, market, and macroeconomic indicators to gain a comprehensive view of what was happening across the portfolio. Within weeks of deployment, insights that previously required months of manual compilation began surfacing automatically.
See also: Why connectivity, not just AI, will power Singapore’s next leap in competitiveness
The governance labyrinth
As insight velocity increased, another challenge quickly surfaced. Operating across 88 markets means navigating varying regulatory requirements while maintaining data sovereignty and compliance. Missteps risk fines, reputational damage or even market exclusion.
Viswanathan says Suntory addresses this by standardising only what is essential while remaining “Gemba-centric” and adapting to local operational realities. Core platforms, data models and security protocols are consistent across the group, while regional teams retain flexibility in execution. Compliance is built into the platform’s architecture from the outset, allowing the company to scale responsibly rather than retrofitting controls market by market.
Ng adds that effective governance must treat data and AI as a single ecosystem. Companies need systems that deliver consistent access control, audit logging and lineage tracking across regions, without constraining innovation. That is the role of Unity Catalog, Databricks’ access-control and lineage engine, which enables granular permissions while supporting scale.
Moreover, governance cannot sit solely within IT or compliance. “Instead, it should be a cross-functional discipline, with ownership shared across data owners, stewards, and business domain experts. Regular reviews of data access, usage, and quality — tied to clearly defined KPIs — help ensure governance remains actionable and aligned with business outcomes,” advises Ng.
The cultural equation
Despite two years of work, Viswanathan says Suntory still considers itself early in its transformation. Even so, the shift has already changed how teams operate. By bringing internal and external data together, Suntory was able to analyse information more quickly and free teams to focus on growing the business. A parallel platform overhaul is now consolidating financial data into a single view, giving leaders a stronger foundation for decision-making.
To stay ahead of the latest tech trends, click here for DigitalEdge Section
“What’s accelerated this journey is a highly capable internal data and analytics team who are deeply embedded in the business and focused on outcomes, not just algorithms,” says Viswanathan. Paired with targeted technology investments, the team is helping Suntory evolve from fragmented insights toward what she calls a connected, intelligent ecosystem.
Yet talent and technology alone cannot upend old habits. Through an internal learning programme called Manabi no Michi (or “the path of learning”), Suntory is working to build trust in AI and help employees see it as “a partner in creating meaningful, consumer-centric experiences.” “The real shift is cultural,” Viswanathan says, describing efforts to help employees see AI as a partner in creating consumer-centric experiences rather than a threat or a mystifying black box.
Ng echoes this sentiment from a different angle. “Building AI isn’t the hard part anymore; trusting AI’s output is.” This is where many deployments fail. Databricks attempts to address this by ensuring customers can evaluate model quality with transparent, repeatable benchmarks aligned to their use case. “Trusted AI is foundational,” Ng says, because companies need to prove accuracy to compliance and legal teams rather than rely on faith in a black box.
The foresight pivot
Today, Suntory’s AI applications focus on three areas: personal productivity, enterprise automation and faster insight generation. AI is used to eliminate repetitive tasks, streamline complex workflows such as new product development, and surface real-time cross-functional intelligence.
Both organisations have aligned on a single north star metric: speed to insight. “If we can reduce the time from data signal to business decision, we win. Everything else — be it forecast accuracy, ROI or productivity — flows from that,” explains Viswanathan.
That emphasis is driving what Databricks calls “data intelligent applications”, a category that helped push the company’s AI products past US$1 billion in revenue run-rate. Databricks recently launched Agent Bricks to help organisations build multi-agent systems on their own data, alongside Lakebase for transactional data and Databricks Apps for deployment — all designed to accelerate the path from insight to action.
The urgency is well-founded. Consumer markets shift quickly and often unpredictably. Companies that spot those movements early and act promptly capture outsized value, even when their data is imperfect.
The next step, and Suntory’s most ambitious goal for the year ahead, is shifting from hindsight to foresight. “Much of our AI work to date has focused on understanding what happened in the past [but the ambition now is to use AI to] assess how ready we are for the month, the year, even the decade ahead,” says Viswanathan.
She continues: “This isn’t just about automation; it’s about fundamentally transforming how we approach business. With the right talent, the right platform, and a relentless focus on creating value, we believe we can move from hindsight to foresight, making data-driven decisions that shape the future.”
If Suntory achieves that, it will move closer to Databricks’ objective measure of AI maturity: a culture of data confidence. “This means employees trust the outputs they receive from AI agents, know the insights are grounded in enterprise context, and feel empowered to use AI as a strategic advantage,” says Ng.
