Bottling Data Trust: Inside Swire Coca-Cola’s Data Product Strategy

In the fast-paced world of beverage manufacturing and distribution, data isn't just a nice-to-have—it's the lifeblood that keeps operations flowing smoothly. For Swire Coca-Cola USA, one of the largest Coca-Cola bottlers in North America, the challenge wasn't a lack of data, but rather building trust in that data across their massive enterprise. During Snowflake Summit 2025, the organization revealed how it restored trust in data with help from Alation.

Swire Coca-Cola USA serves 31 million consumers across the American West, operating a footprint that includes 50+ brands, 250+ products, six manufacturing plants, and 63 distribution centers. With production lines capable of manufacturing 2,000 cans per minute and operations running seven days a week, the complexity of their data ecosystem matches the scale of their operations.

"We have all of the data, from HR data to supply chains and logistics to sales data. We bring them all together, we consolidate it, and we make sure our end users, who are my stakeholders, who are my team members, we use that data in a way that we can do better," explains Bharathi Rajan, Vice President of Enterprise Data, Insights & Applications at Swire Coca-Cola.

However, when Rajan joined the organization three and a half years ago, she discovered a significant gap between what business partners wanted from their data and what the data teams could reliably deliver. This gap wasn't just about technology—it was fundamentally about trust.

Key challenges: Data abundance meets a trust gap

Swire Coca-Cola faced several critical challenges that are common across large enterprises dealing with complex data ecosystems:

Fragmented data foundation. The organization had data consolidated in various places, but lacked a unified foundational data model that could serve every business function. Different departments were operating with their own versions of truth, creating silos that hindered cross-functional collaboration and decision-making.

Data quality and trust issues. Perhaps the most significant challenge was the erosion of trust in data accuracy and reliability. "There were data trust issues. If my end users, my stakeholders don't trust the data, I'm really not doing a great job of giving them what they want so that they can make better decisions," Rajan notes. This lack of trust was manifesting in daily operational challenges where teams spent hours trying to validate data rather than using it for strategic decisions.

Inconsistent metrics and definitions. A prime example of this challenge was their On-Time In-Full (OTIF) metric—a critical manufacturing KPI. "When I started, there were different versions of OTIF, the way OTIF was calculated going around. The manufacturing had one or supply chain had one, OTIF is being calculated in a different way by our sales team and so there was no consistent way of calculating it," Rajan explains. This inconsistency led to confusion and undermined confidence in reporting across the organization.

Reactive data management. The team found themselves constantly in reactive mode, chasing down perceived data issues rather than proactively ensuring data quality. This approach was not only inefficient but also prevented the data team from focusing on strategic initiatives that could drive business value.

These challenges created a vicious cycle: Poor data trust led to more questioning of results, which consumed more time and resources, which in turn prevented the team from implementing improvements that could build trust.

Objectives: Build a foundation of data trust

Recognizing these challenges, Swire Coca-Cola established clear objectives for its data transformation journey. Their primary goal was to shift from a reactive, siloed approach to a proactive, unified data strategy centered around building trust.

The team aimed to create a foundational data model that could serve as a single source of truth across all business functions. This wasn't just about technical consolidation—it was about establishing standardized definitions, metrics, and processes that everyone could rely on.

They also wanted to implement a cultural shift from thinking about individual data sets to thinking about data products. Rather than creating thousands of separate reports and data sets, they envisioned reusable, trusted data products that could serve multiple business needs while maintaining consistency and quality.

Finally, they sought to establish measurable ways to demonstrate data quality and reliability, using language and metrics that resonated with their manufacturing-focused business culture.

Implementation: Clarity through strategic partnerships

Swire Coca-Cola's implementation journey began with a critical decision: choosing the right partner and platform to support their data transformation goals. After evaluating multiple vendors, they selected Alation as their data catalog and governance platform, alongside Snowflake as their data cloud foundation.

Technology stack and architecture. The implementation centered around a robust technology stack with Snowflake serving as the primary data warehouse, housing multiple layers of data models. Alation was positioned as the discovery and governance layer, providing a unified interface for data consumers across the organization. The architecture also incorporated various source systems, including SAP for enterprise data, manufacturing systems, and business intelligence tools like Power BI.

Rapid reployment strategy. What made their implementation particularly impressive was the speed of deployment. The team made the decision to proceed with Alation in January and had released the platform to their IT teams by April. By June, they were rolling out to business users, with most business units represented by August.

"I didn't expect it to go that fast. It was really nice," reflects Gordon Bonzo, Manager of Data Enterprise Master Data at Swire Coca-Cola. This rapid deployment was possible because the team had significant existing data documentation scattered across various systems, which they consolidated into Alation during the implementation.

Data products approach. Rather than simply cataloging existing data sets, the team focused on creating certified data products that solved specific business problems. Their approach to the OTIF metric exemplifies this strategy. Instead of allowing multiple versions of the calculation to exist, they brought stakeholders together to agree on a single, standardized definition.

"We were able to bring everyone together in a room. We said, OK, what's the right calculation? What's the standard that needs to be across all functions? And then we took that metric and put that into Alation. And now there's one standard calculation," Rajan explains.

Governance integration. A key success factor was integrating data governance into existing processes rather than treating it as a separate initiative. The team embedded data certification and documentation into their CI/CD pipelines, making governance part of the natural development workflow rather than an additional burden.

Results: Quality data delivered on time

The transformation at Swire Coca-Cola has delivered both quantitative and qualitative results that demonstrate the power of building trust through data products and proper governance.

Operational efficiency gains. One of the most significant improvements has been the dramatic reduction in time spent chasing data issues. Previously, the team would spend hours daily investigating perceived data problems and validating results. This reactive cycle has been largely eliminated as trust in the data has grown.

Standardized metrics and definitions. The implementation of standardized data products, exemplified by their unified OTIF calculation, has eliminated confusion and inconsistency across departments. Now, when stakeholders reference OTIF, everyone is working from the same definition and calculation method.

Enhanced data discovery and self-service. Business users can now easily find and understand the data they need through Alation's intuitive interface. The platform provides not just access to data, but context about what the data means, how it's calculated, and how it should be used.

Improved data quality via "Data OTIF." Perhaps most innovatively, the team created a "Data OTIF" metric that applies manufacturing principles to data delivery. This metric ensures stakeholders receive quality data, available at the right time and in the right place, when they need it. "Data OTIF or data on-time in full... is to make sure that we are giving our stakeholders, our end users, our data in full, as in with quality data. They have access to quality data, the data is available at the right time, in the right place, when they want it, how they want to use it," Rajan explains.

Cultural transformation. The results extend beyond technical improvements to include a fundamental shift in how the organization approaches data. "Before they come to me and ask me if there's something wrong with the data, they actually make sure they go and they check other sources and make sure that, yes, there is really something wrong with the data before they come to me," Rajan notes. This represents a significant cultural shift from distrust to trust in data systems borne of self-service confidence.

The success of the initiative is perhaps best captured in an unsolicited comment from a business user: "One of the best things that happened to me, this is an individual I don't see him often…” Rajan shares. “He turns around and he tells me, I have to tell you. Alation is the best thing that you guys have done. It's so easy."

Conclusion: Building for the future

Swire Coca-Cola's data transformation journey demonstrates that building trust in data requires more than just technology—it requires a fundamental shift in how organizations think about data products, governance, and user experience. By focusing on creating standardized, reusable data products and embedding governance into existing workflows, they've created a foundation that not only solves current challenges but positions them for future innovation.

Looking ahead, the team is excited about leveraging their strong data foundation for AI and advanced analytics initiatives. "For me, it's anything that you plan, I want to make sure that we're able to integrate into the relationship, right? So at the end of the day, it's a critical part of our technology stack and how do we make sure that all of the data that lies everywhere is going to be integrated well enough so that every user, every team member, every one of the organization has access to data at the right time, at the correct place, and quality of that data," Bharathi envisions.

The success at Swire Coca-Cola offers valuable lessons for other organizations struggling with data trust and governance challenges. By focusing on data products rather than just data sets, embedding governance into workflows, and using business-friendly metrics to measure success, they've created a sustainable model for data excellence that continues to evolve with their business needs.

As organizations increasingly recognize data as a strategic asset, Swire Coca-Cola's journey shows that the path to data maturity isn't just about having the right technology—it's about building the right culture, processes, and partnerships to make data truly trustworthy and valuable for business decision-making.

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