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AIPROPX ReportForbes · 3h ago
Solving The Shoebox Problem
--:-- / --:-- This voice experience is generated by AI. Learn more . This voice experience is generated by AI. Learn more . Summary Fragmented data, exemplified by a patient's shoebox medical history, creates significant inefficiencies in healthcare and enterprises alike. Many companies struggle with siloed legacy systems, hindering workflows and decision-making. Biopharma giant AbbVie faced this challenge, with record merging taking an hour. Partnering with Reltio, AbbVie implemented a modern data platform, enabling seamless integration of Allergan's data during a major acquisition, even remotely during the pandemic. This robust data foundation provided a unified view, accelerated insights, and contributed to AbbVie's stock outperforming the market. A strong data foundation is critical for successful mergers and effective AI implementation, urging enterprises to adopt data unification platforms for greater speed and precision.
Picture a typically busy afternoon at a local medical clinic.
The beleaguered receptionist has just taken a patient’s insurance information, and the next person in line steps up to the desk. A new patient. A 75-year-old man named Henry, who’s holding a battered and worn shoebox.
The receptionist looks at it in wonderment. Then Henry explains. Inside that sad, seemingly worthless cardboard container is his entire medical history . He had moved frequently, seen dozens of specialists, and had no better system for keeping all that information together than by throwing all his lab results, diagnoses, prescriptions, etc. into a humble shoebox.
The receptionist froze. The nurses whispered. The clinic halted for almost an hour. They couldn’t take Henry, not because his condition wasn’t urgent, but because they had no way to understand his past .
“We’ll need to reschedule,” the medical services administrator told him. “It may take a week to sort through this.”
Henry had to apologize for needing care. For being there. He shouldn’t have had to.
If Henry’s past providers had had the benefit of agentic intelligence, all the data from his medical history might have been linked and easily retrieved by the clinic. The personnel wouldn’t have to spend hours sifting through a shoebox. They would only have to type in a few prompts that would have spurred AI agents, autonomous software systems that can communicate with different data systems, to gather Henry’s history from a variety of sources.
Unfortunately, the vast majority of enterprises have their own versions of Henry’s shoebox: fragmented, incomplete, and duplicated records, forcing customers or employees to connect the dots themselves. Enterprise data is trapped in various siloed systems that can’t talk to each other—hardly an improvement from the filing cabinet system from the pre-digital era. The result is friction that slows workflows, fragments information, and leaves organizations with pieces of a puzzle that don’t fit together, rather than with easy, efficient solutions.
AbbVie was living this problem when it came to us at Reltio, looking for solutions. AbbVie ranks as the second-largest biopharma company in the world, so data was vital to its efforts. However, the company had been dependent on legacy systems to store it. These systems were fragmented and slow, with limited governance and low user confidence. Because there were multiple platforms, AbbVie’s master data management (MDM) system performed poorly and was difficult to use.
How severe was the data situation? In the words of Vivian Wu (associate director at AbbVie): “It took an hour to do just one record.” 1 That hour was spent solely on merging records to ensure consistent data across platforms.
Working with AbbVie, we created a modern data platform that integrated all its information and made it instantly accessible. That platform ended up paying significant rewards when, in 2020, AbbVie acquired Allergan, another pharmaceutical company of similar size. Because we had helped AbbVie build a strong data foundation and establish an integration hub, it was able to seamlessly incorporate Allergan’s data remotely—during the pandemic, no less—with minimal disruption. In fact, due to COVID-19, this massive data merge was completed entirely remotely . And the resulting platform provided a scalable, cloud-native hub for data consumption and integration, allowing for rapid creation of a unified 360-degree view of customer, product, and transactional data across the merged organization. It also accelerated the generation of actionable insights, reduced operational disruptions, and shortened time-to-value for the combined organization’s commercial and clinical functions.
The stock market has recognized the tangible benefits of this data-driven approach: The company’s stock has outperformed the S&P 500 since the merger closed in May 2020, delivering returns of over 100 percent by December 2025. 2 This positive valuation reflects the successful integration of Allergan’s assets, made possible by a solid data foundation that enabled the quick realization of synergies. The capital market’s appreciation highlights an important lesson: A modern, flexible data platform is a strategic necessity that directly influences an organization’s ability to execute successful mergers and acquisitions.
AI implementation is, of course, at the top of the enterprise agenda right now. But the problem is, without a strong data foundation, AI can’t do its job. It ends up being as helpless as Henry was.
Siloed legacy data systems increasingly slow down an enterprise’s workflows. When AI is introduced into this environment, it often creates more problems than it solves. If the people who work at a company can’t access unified, trusted data, then AI won’t be able to either.
The first step toward a more productive future is to build a data unification platform empowered by agentic intelligence. That foundation gives enterprises more than cleaner data. It gives them the context needed...
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