Who should ultimately own master data within an enterprise: the IT department, or the business teams that interact with and rely on that data every single day?
This fundamental question served as the guiding thread for a compelling customer panel discussion at the recent Automate Community on Tour event in Chicago. Moderated by representatives from Precisely, the panel brought together industry leaders from vastly different organizational backgrounds to discuss the complexities, challenges, and triumphs of master data management. The discussion featured insights from Organic Valley, Fender, and Mondelēz International—three distinct enterprises operating across different industries, varying company sizes, and diverse stages of SAP maturity.
Despite the operational differences that naturally exist among a dairy cooperative, a legendary musical instrument manufacturer, and a global snacking powerhouse, their perspectives on master data automation aligned in striking ways. The shared conversation illuminated critical lessons regarding business ownership, end-to-end workflow visibility, the pragmatic deployment of artificial intelligence, and the indispensable human element required to drive digital transformation.
Business Ownership as a Catalyst for Master Data Automation
The journey toward modern master data management looked entirely different for each participating company at the starting line, yet all three eventually converged on a singular philosophy: empowering the business teams closest to the data.
Organic Valley, for instance, went live on SAP S/4HANA in April 2024 as a Greenfield implementation. For their organization, this meant undertaking the massive task of rebuilding virtually every master data intake process from the ground up, many of which had previously relied on manual SharePoint-based forms. Lindsay, representing Organic Valley on the panel, explained to the audience that tools like Automate Studio initially helped relieve immediate operational pain points. However, the organization’s long-term vision extends far beyond simple relief; the goal is to successfully push the ownership of master data directly to the operational teams that utilize it most, minimizing tedious data entry and shifting organizational focus toward robust governance and strategic analysis.
Jon, representing Fender, echoed this sentiment with a blunt and pragmatic assessment of what true data ownership means for modern corporate structures. Pointing out that corporate employees should not be relegated to the status of administrative data entry clerks, he emphasized that they are, in fact, analysts. Their core responsibility should be setting governance rules and ensuring those established rules are meticulously followed, rather than wasting valuable time chasing down internal approvals or double-checking disparate spreadsheets.
Srini described a nearly identical operational pattern unfolding at Mondelēz International. The team at Mondelēz initially began their automation journey by focusing strictly on finance master data maintenance. As their implementation progressed and various exception scenarios were built out to handle complex business requirements, the scope expanded organically. Other departments across the enterprise quickly noticed the operational efficiencies being achieved and enthusiastically adopted the tooling for their own processes.
This collective industry movement reinforces a core philosophy held across the data management sector: the people who understand the context of the data best should be the ones entrusted with its ownership.
Full Workflow Visibility Drives Speed, Accuracy, and Traceability
As enterprises mature in their data management strategies, they naturally transition away from isolated desktop automations toward orchestrated, end-to-end processes that offer comprehensive workflow visibility. This evolution allows teams to work significantly faster, drastically reduce operational errors, and maintain a clear audit trail across every phase of a transaction.
Fender’s relationship with process automation runs deep, having utilized Automate Studio for approximately 14 years. Recently, Jon’s team implemented Automate Evolve, drawn specifically by the power of its advanced workflow capabilities. Because Fender’s material build process is inherently complex, visualizing the entire lifecycle laid out in clear swim lanes made it vastly easier to manage and monitor. Rather than attempting a massive overhaul all at once, the team adopted a measured approach by starting with small workflows. They pulled existing Automate Studio scripts directly into the Evolve environment and layered robust workflows on top of them, building a solid foundation before tackling larger initiatives like new product introduction.
For Organic Valley, workflow integration served as a vital educational mechanism as well as an efficiency driver. Because many of Organic Valley’s business users are still actively learning SAP’s complex terminology and official definitions, leadership deliberately designed the automated process itself to act as an instructional tool. Their overarching goal was to weave governance directly into the data collection process, ensuring that requesters intuitively understand the quality, context, and compliance standards of the information they submit before it enters the core system.
Mondelēz approached the workflow challenge through the lens of seamless operational execution and regulatory compliance. Srini noted that Mondelēz prioritized touchless processing, meaning that once an automated workflow successfully completes all required internal validation steps, the final posting to SAP triggers automatically without manual intervention. Furthermore, stringent audit traceability stood out as a primary driver for standardizing these workflows across the organization.
Diverse Enterprise Approaches to Artificial Intelligence
While artificial intelligence remains a dominant topic of conversation across nearly every sector of the technology landscape, the panel revealed three distinctly pragmatic approaches currently being deployed in the enterprise space: diving in completely, taking a cautious wait-and-see stance, or adopting a targeted try-and-see methodology.
Organic Valley leans firmly into the try-and-see category, balancing innovation with a cautious, protective approach toward safeguarding its cooperative farmers. Lindsay shared that key insights gathered from recent conversations at a Gartner event helped shape their strategy. Her primary lesson learned from conducting targeted proofs of concept was straightforward yet profound: if the underlying data is not clean, organized, and ready for utilization, no tool or process will make the technology function as intended. Rather than fruitlessly hunting for speculative use cases, Organic Valley’s team allows practical use cases to surface naturally through existing business requests, subsequently preparing the priority data required to support them.
This perspective aligns directly with broader industry warnings: attempting to implement sophisticated automation on top of poor-quality data simply automates bad data, while emerging AI tools merely amplify those foundational data errors at scale.
Fender, by contrast, finds itself further along the AI adoption curve. Jon’s team successfully executed their first major artificial intelligence project by constructing a vector database leveraging approximately 12 years of historical product copy stored within their product information management system. This repository empowers product managers to rapidly draft new marketing copy that accurately reflects each specific brand’s unique voice. Furthermore, Fender developed an AI agent named Leo, securely connected to their EnterWorks data architecture, designed to intelligently generate product recommendations. According to Jon, Leo successfully produced 119,000 product recommendations in a matter of months, dramatically outpacing the 55,000 recommendations generated by a team of five human analysts over a three-year span. Reflecting on the iterative nature of technological development, Jon noted that even flawed initial demonstrations can quickly turn into productive new rules, proving that organizations are often inspired by their early failures.
Meanwhile, Mondelēz has already operationalized several foundational AI use cases, including advanced knowledge assistance systems. Srini explained that the company is actively collecting and prioritizing new ideas designed to deliver rapid business benefits, emphasizing that a strong, well-structured foundational data layer remains an absolute prerequisite for any successful artificial intelligence initiative.
Essential Advice for Master Data Automation Beginners
As the panel discussion drew to a close, the moderator asked each participant to share what they wish they had known when they were first embarking on their respective master data automation journeys. The responses from the enterprise leaders were nearly unanimous, centering squarely on the vital importance of finding internal champions.
Jon described the ideal organizational advocate as an individual who possesses a keen eye for detail and genuinely enjoys the challenge of bringing order out of corporate chaos. Lindsay emphasized that the most significant operational momentum occurs when someone within the business unit experiences a breakthrough and becomes genuinely excited about how automation eliminates friction in their daily routine. She also highlighted how emerging technologies like artificial intelligence are already compressing certain development timelines from an entire week down to a single hour. Srini concurred regarding the immense value of internal champions, noting that once business teams fully comprehend the true capabilities of modern automation tools, their dependency on IT naturally decreases while overall adoption rates soar.
Reflecting on their recent SAP S/4HANA go-live, Lindsay offered a crucial piece of advice for peers: organizations must begin thinking about and planning their master data processes much earlier in the implementation cycle. Looking toward the future, Srini noted that Mondelēz is currently managing a migration from SAP ECC to S/4HANA while simultaneously preparing for modern Fiori-based transaction processes.
Ultimately, the most consistent and resonant theme to emerge from the Chicago event transcended specific software features, technical architectures, or advanced algorithms. Success in master data automation fundamentally starts with people—empowered business owners who take accountability for their organizational data, passionate champions who cultivate internal momentum, and master data professionals who are recognized and valued as strategic analysts. When these foundational human elements are properly aligned, the underlying technology performs at its highest potential.
