As we’ve entered 2024, what do you view as the key opportunity biopharmas can unlock for future success?
Unifying data is key to biopharmas with plans to scale globally, use AI successfully, and become more agile in their business. Over the years, biopharmas have gotten used to a piecemeal approach when working with data and software. While most biopharmas have streamlined software across the organization, data still sits in silos within many separate systems, departments, and local markets. Fragmented data also prevents biopharmas from delivering a superior customer experience – another key priority for organizations today.
How can the industry move towards a more connected data approach?
The first step is acknowledging that the fragmented legacy model does not support a customer-first approach. For example, most organizations likely have different field teams contacting the same customer. A healthcare professional (HCP) can be an investigator working in the clinical area, a key opinion leader (KOL) talking with medical affairs, and a prescriber interacting with the commercial team. Each part of the organization then records these interactions in different systems. You can see how this could result in a disconnected experience from the customer perspective.
The next step is connecting the dots using a common identifier to ensure that one HCP is the same whether they’re an investigator, KOL, or a prescriber. A key piece in achieving this connected customer experience is having consistent customer reference data that serves as the foundation for HCP engagement.
I imagine this standardization can also help companies bridge the gap between clinical and commercial teams.
Exactly. A global data model that uses a single ID for an HCP connects all touchpoints and enables better collaboration and visibility across functions. This results in a more orchestrated, connected experience for the HCP.
What are other considerations when trying to harmonize data?
Having a structure where you can take that consistent reference data and easily apply it for different uses across global markets is key. For example, scaling analytics across markets requires a global data architecture. Imagine a global report where you want to know which oncologists were visited last year. However, every country has a different definition for oncologists. You must rebuild your model again and again because each country’s underlying data structure differs. This is why a McKinsey study found that data scientists and business analysts spend 80% of their time repeatedly preparing the same data.
The same applies to scaling targeting and segmentation. Typically, you have to normalize and restructure data in every country. Using different data models causes friction in business applications and prevents the organization from being agile. A global data model for reference data can align all HCP types using one global identifier and streamline picklists to create consistency across all regions and countries. This saves time, money, and resources and allows the business to operate faster and be more nimble.
Can you quantify the impact of standardizing reference data globally?
Operating with a global architecture can make a huge impact on your business. Let’s assume a EUR 20 million investment in a global next-best-action initiative. Reducing data preparation by even 50% could lead to EUR 8 million savings.
How should biopharmas be thinking about their data strategies for the future?
The success of biopharmas will depend on connected software and data. You can have the best software in the world, but if your data quality is poor, you won’t reap the benefits of that investment. Better data and a structure to apply it across the organization will allow more scalability and agility. A robust data foundation will also support implementations like AI that require good data to work well. As we harness more data from different sources, we need to connect the dots and ensure that it’s serving us instead of blocking our mission to deliver superior customer experiences.
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