Using Veeva Link Data to Fuel AI and Advanced Analytics

Apr 29, 2025 | David Medina Tato

Generative AI is starting to deliver for life sciences. As AI and advanced analytics tools mature, biopharma organizations are shifting focus from experimentation to understanding their business value and deciding whether to accelerate investment.

Robust data is critical to making the most of these new technologies. Without this foundation, companies risk missing opportunities. Here are three ways high-quality, deep KOL data can help your organization address its business challenges.

1. Embed consistent data quality and structure

A high-quality dataset in life sciences must have specific attributes to generate business value and be AI-ready: data must be robust and comprehensive, accurate and trustworthy, structured and globally harmonized. Analytics teams must feel confident that the information in front of them covers the full picture, requires limited manual effort to prepare it for integration, and can be easily analyzed.

Access to a high-quality dataset is foundational yet often challenging to achieve. As one data leader at a top biopharma explains, “It’s hard to find a consistent dataset that is actually usable to match [an enterprise AI] strategy. This is where Veeva Link Key People has been vital for us. …[We] can truly say we have an end-to-end strategy and a data source that can track through that.”

To be AI-ready, data must be robust and comprehensive, accurate and trustworthy, structured and globally harmonized.

2. Bring people with you

Once you’ve established the right use cases, building confidence and data literacy within the business is key to maximizing the value of deep data, whether teams are adopting AI or other advanced analytical tools.

Data leaders whose organizations are further advanced note that unexpected challenges can arise from failing to prepare teams for change. Key considerations include:

• Education. Equipping teams to navigate change as well as the tools
• Focus. Ensuring that AI projects address pressing and impactful issues
• Collaboration. Demonstrating AI uses cases that support, rather than replace, human expertise

Once your teams are AI-literate, you’ll need to identify business experts. Human expertise is essential to defining the right inputs and interpreting the results. When AI and analytics are applied to the right business initiatives powered by high-quality data, resources can be refocused on these higher-value activities.

One vice president of digital excellence at a large biopharma uses the metaphor of AI as a lens that helps improve organizations’ visibility and focus. Another noted: “With the right data + AI + humans, you can move into the future being courageous and not anxious about what AI might bring for you.”

“With the right data + AI + humans, you can move into the future being courageous and not anxious about what AI might bring for you.”

3. Unlock productivity through data partnerships

Data partnerships can enhance productivity by bringing industry-wide experience and external resources to ensure the highest quality datasets.

One data leader explains that partners often help overcome common pain points (such as cleaning, structuring, and harmonizing data), enabling teams to focus on other priorities. Another data leader adds that data partnerships can mitigate internal limitations: “We use internal data… but we also try to break free from our own beliefs, [blind spots, biases], and prejudices to get a fuller customer understanding. This is why we’ve built partnerships with external data like Veeva Link.”

The business impact is often far greater than anticipated. One large biopharma used Veeva Link to transform key opinion leader (KOL) mapping capabilities so that tasks that used to take months now take minutes. As its data leader points out, “What would you do if your medical science liaisons (MSLs) had two extra months a year?”

“What would you do if your medical science liaisons (MSLs) had two extra months a year?”

Make the case to get started

To convince senior leadership, a strong business case for data partnerships must demonstrate:

• The anticipated value with relevant use cases and tangible outcomes
• Benefits before and after implementation
• Opportunity costs of a weak data foundation and the need to remain competitive
• Plans to prototype, pilot, and iterate. The data leader continues, “After a couple of these, no one will ask for justification again.”

With high-quality data powering their AI and advanced analytics tools, leading organizations will get more mileage from their data strategies.

Learn how Link Key People data can help unlock the potential of advanced analytics within your organization.