Bayer: Customer Data Strategies on Measuring ROI and Impact

This video, featuring a representative from Bayer, details the significant return on investment (ROI) and operational impact achieved through the optimization of their customer data management program, focusing heavily on data quality improvements and process efficiency. The core narrative revolves around transforming slow, bottlenecked data processes into agile, daily operations, fundamentally changing how the commercial field force interacts with critical customer reference data. The speaker highlights several key metrics demonstrating this transformation, moving beyond abstract quality goals to concrete improvements in speed, capacity, and user engagement.

A major achievement discussed is the shift in data mastering cycles. Previously, mastering cycles—the process of cleaning, matching, and consolidating customer data—ran only twice a month. Through program improvements, this was accelerated to a daily mastering cycle. This daily capability is described as "very powerful," enabling the organization to immediately leverage data provided by external data vendors on a daily basis, a crucial factor in maintaining timely and accurate customer profiles (physicians, accounts, affiliations, and addresses). Concurrently, the capacity to handle Data Change Requests (DCRs) dramatically increased, moving from attending to only two types of requests to managing over ten different types, covering all data entities relevant to customer data.

The speaker also addresses the initial concern that implementing stricter data change request processes might create friction or delays for the sales representatives (field force). Surprisingly, the opposite occurred. By providing the field force with access to a "bigger view" of open data on demand, the overall need for creating DCRs decreased. Furthermore, when a DCR was necessary, the turnaround time improved drastically. Historically, DCRs were processed on a monthly basis, forcing the field force to mark their calendars for data updates. This slow process was replaced with a Service Level Agreement (SLA) measured in just a few hours. This rapid turnaround fostered a change in mindset among the sales reps, who began viewing the time spent submitting quality requests as a worthwhile investment, knowing they would quickly receive accurate data to support their commercial activities.

Finally, the presentation emphasizes the massive reduction in time required for data integration projects and the elimination of data silos. Data integration, which previously took close to three months—creating "red alert" situations for new data acquisitions or product launches—was reduced to just a few weeks of effort, especially when leveraging solutions like Veeva. This efficiency gain allowed the team to invest more time in strategic planning and data profiling rather than worrying about execution bottlenecks. The overall solution empowered "power users" to move out of data silos, granting them self-service access to data that was previously locked away in a "black box," thereby democratizing data access and fostering greater organizational efficiency.

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Key Takeaways

• Accelerated Data Mastering: The organization successfully transitioned from a bi-monthly data mastering cycle to a daily cycle. This daily capability is essential for pharmaceutical companies that rely on timely customer data updates from external providers and is a critical metric for measuring data management program ROI.
• Increased Data Change Request (DCR) Capacity: The ability to handle DCRs expanded significantly, moving from only two types of requests to managing over 10 different types, ensuring comprehensive coverage of all customer data entities (physician, address, affiliation, accounts).
• Dramatic SLA Improvement for Field Force: DCR turnaround time was reduced from a monthly cycle (requiring field force calendar marking) to an SLA measured in just a few hours. This rapid response time is crucial for maintaining data relevance and supporting agile commercial operations.
• Field Force Mindset Shift: The improved speed and quality of the data management process changed the sales reps' perception, leading them to view the investment of time in submitting quality DCRs as worthwhile, knowing they would quickly receive high-quality data in return.
• Reduced Need for DCRs via Transparency: Providing the field force with access to a "bigger view" of open data on demand surprisingly reduced the overall volume of DCRs, suggesting that data transparency can preempt many common data quality issues.
• Massive Reduction in Integration Time: The time required for new data integration projects was drastically cut from approximately three months to just a few weeks, particularly when leveraging platforms like Veeva. This efficiency eliminates bottlenecks that previously delayed product launches and new data acquisition.
• Strategic Focus Over Execution Worry: By streamlining data integration and mastering processes, the team was able to shift their focus, investing more time in strategic planning and data profiling rather than being consumed by execution challenges and "red alerts."
• Elimination of Data Silos and Empowerment of Power Users: The implemented solution provided a clear view and access mechanism to previously siloed data ("black box" data), enabling power users to perform self-service data tasks and fostering data democratization across the organization.
• Customer Data Entities Covered: The data management program specifically addresses key customer data entities, including addresses, physicians, accounts, affiliations, and associated attributes, highlighting the comprehensive nature of the quality initiative.

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Tools/Resources Mentioned

• Veeva: Explicitly mentioned as a platform that helped drastically reduce data integration efforts and is implied as the core system supporting the customer data strategy (likely Veeva Network/CRM).

Key Concepts

• Data Mastering Cycles: The process of cleaning, standardizing, matching, and consolidating customer data from various sources into a single, accurate master record. Accelerating this cycle (from bi-monthly to daily) is a key measure of data management maturity.
• Data Change Requests (DCRs): Formal requests submitted by users (e.g., sales reps) to correct, update, or add customer reference data (e.g., a physician's address or affiliation). The efficiency of the DCR process directly impacts field force productivity.
• Service Level Agreement (SLA): A commitment regarding the speed and quality of service delivery. Improving the SLA for DCRs from monthly to hourly turnaround demonstrates significant operational improvement.
• Data Silos: Data stored in separate systems or departments that is not easily accessible or integrated, often preventing a unified view of the customer. The solution aimed to move users out of these silos.
• Data Profiling: The process of examining the data available from an existing information source (e.g., a database or file) and collecting statistics and information about that data. This is a crucial step in data integration planning.