Optimizing Field Execution with Unified Customer Data
It isn’t always a lightning-bolt moment but a growing frustration with disconnected, low-quality customer data that sparks many biopharmas to switch to a master data model. As one U.S. data management lead at a top 20 pharma reflected, “With bandaid after bandaid, we stepped back and asked, ‘Is this finally the time? What will it take to reimagine data unification?’”
The alternative to a master data source is a ‘best-of-breed’ customer data management approach, which involves employing survivorship business logic for data attributes sourced from different vendors in an effort to build a golden record. While the data strategy still exists in the industry, maintaining it is costly and complex as sources multiply. The resulting data silos can create hurdles to expansion in new regions or therapeutic areas. And the more time your field team spends searching for information, the less time they spend with HCPs.
One way companies can address this complexity is by relying on Veeva OpenData as their master record for customer reference data. With OpenData as a single source of truth, data management teams can free up internal stewardship resources while improving field teams’ confidence in their data.
One master record ensures quality, efficiency
While there is merit to the base principles of best-of-breed data management, maintaining data records is difficult. Over time, new data sources can pose challenges for support teams and technical partners. When team members leave, institutional knowledge of the solution is also lost. Those maintenance efforts drain resources and create inefficiencies, such as delayed enhancements and bug fixes.
Switching to a master record for reference data allows teams to maintain every customer record meticulously and with much less effort. OpenData data stewards ensure records are accurate and up to date. Unraveling a complex, multi-source data strategy and replacing it with a master record provider demands a thoughtful approach to avoid disruption, but it can be done and it is often well worth the effort.
“We’re seeing the field is requesting fewer DCRs for specialties,” explains the U.S. data management lead who recently went through such a transition. “In our recertification process, we can let go of many manual overrides and return the actual specialty calculation to our organic data with OpenData at the front.”
“With bandaid after bandaid, we stepped back and asked, Is this finally the time? What will it take to reimagine data unification?” U.S. data management lead at a top 20 pharma
3 tips for a successful data switch
Entering new markets, launching new products, and striving to meet HCPs’ changing needs require constantly focusing on serving internal customers better, who are more data-hungry than ever. Here are three considerations for biopharmas looking to simplify their reference data strategy while meeting increasing field demand:
Tip 1: Track value and communicate it to end users.
Evolving from best-of-breed data management toward utilizing OpenData as the single source of truth requires the application of change management principles. Provide stakeholders with proof points that capture the value of how data has improved outcomes — for example, reduced DCR processing times and faster time-to-insights.
Show field teams that you have considered their roles and goals by offering possibilities and use cases for what they can achieve using unified data. The competitive advantage goes far beyond reps being better prepared for HCP meetings. Operational teams benefit as well, receiving fewer tickets and duplicate tasks.
Tip 2: Consider all use cases and downstream impacts.
When changing a master record, there are domino effects to consider. For example, a specialty might change from internal medicine to hematology-oncology, altering a subset of individual HCPs’ records. As a result, it’s essential to communicate the impacts at the macro and micro levels to stakeholders.
In addition, conduct analyses ahead of time and prepare for conversations with a wide range of stakeholders — some adept at data analysis and others not. Reference data has a specific purpose, though the use of it by various stakeholders can be very different and have wide-ranging effects. Investigate all potential use cases and develop a solution for everyone. Because any later changes to the rules can disrupt operations.
Tip 3: Fix data fragmentation issues sooner rather than later.
It’s common for commercial teams to become highly focused on operations, resulting in temporary fixes to data fragmentation and quality problems. The corrections often include adding new sources and vendors, new coding, and more data stewards.
“We knew that every time we wrote code, we created debt that we would have to undo. The data switch was an intelligent decision we had to make,” the data lead says.
Hear about one biopharma’s move to a globally harmonized data model.