Pre-Launch Prep for Emerging Biopharmas: Data First, Then the Rest

March 13, 2025

Feature

Article

Making early commercial data and analytics decisions for launch cen future-proof their data management.

Todd Foster
Associate partner
Beghou Consulting

Before considering AI or advanced analytics, emerging biopharmas must first establish a solid data foundation. Without well-structured, high-quality data, even the best AI tools will fail to deliver reliable insights—for example, over 85% of GenAI pilots¹ failed to reach production in recent years. But achieving a solid data foundation can be more challenging than it sounds.

In a perfect world, every emerging biopharma company would have a clear short- and long-term vision leading up to launch. Then, aligning their commercial data and analytics investments with their objectives would be much easier and remove some of the uncertainty of the risky, early, and often expensive decisions leading up to launch.

However, this is rarely the case, purely because predicting what the future holds is challenging, especially for first-time launchers with limited resources and short timelines.

Yair Markovits
Partner
Beghou Consulting

Since we don’t have the benefit of a crystal ball, this article presents a framework based on our decades of experience helping life science companies successfully bring their products to market. Emerging biopharmas can use it as a guide to make early commercial data and analytics decisions for launch and future-proof their data management.

Prioritize at-risk investments

The first critical question is when to invest: too early can waste resources, while waiting too long can leave you unprepared for a successful market entry. To strike the right balance, define your launch-ready date (when you’ll have all your commercial data systems running and the field team in place), which is typically about four weeks before launch and can differ based on the situation. Then, work backward to create a launch timeline that identifies the key areas to invest in at risk.

A typical timeline is shown in the figure, which visualizes the immediate pre-launch priorities vs the post-launch requirements.

Typical timeline for prioritizing data investments prior to launch.

For example, with a rare disease company that had fast track designation and possible early FDA approval, we held stakeholder meetings to identify a launch readiness timeline everyone was comfortable with. We aligned around three months prior to anticipated approval: one month felt too risky, and six months felt too early for large investments. The launch plan considered their core use cases and the dependencies that would drive various work streams.

Engage internal stakeholders early

Determining the specific investments you need to make at each time point is much easier if you involve your stakeholders—sales, brand/marketing, market access, sales ops, analytics, forecasting, finance, HR, alignment, incentive comp, health economics and outcomes research (HEOR)—early in the process. They possess a wealth of knowledge and experience about what the data they need to do their jobs well.

The data swamp, characterized by the sheer volume and variety of healthcare data, can easily overwhelm even the most seasoned data professional. Rather than getting mired in the details, use a systematic approach to establish a holistic enterprise data strategy that prioritizes the right data sources and partners.

Collect information from your stakeholders about their roles, business processes, workflows, the information they need, and how they use it. This information feeds into use cases for why the data is used and the insights needed about healthcare organizations, healthcare providers, patients, and the landscape, now and in the future.

The stakeholders using the data and technology often won’t know the full capabilities of the potential solutions and therefore what to ask for. This is where your IT and data teams can bridge the gap by providing education about the features and functionality.

Ultimately, you want to avoid buying data or investing in technology that won’t address your use cases or end up without data or functionality you need because use cases were overlooked. For example, we’ve found that companies often focus only on commercial use cases initially and lack the data and systems to support other functions later. Incorporating stakeholder input into your decision-making will result in fewer revisions, less cost, and greater adoption.

Navigate the data swamp

The data swamp, characterized by the sheer volume and variety of healthcare data, can easily overwhelm even the most seasoned data professional. Rather than getting mired in the details, use a systematic approach, as shown in the figure below, to establish a holistic enterprise data strategy that prioritizes the right data sources and partners.

Framework to create a future-proofed, dynamic data ecosystem.

Key questions you should be asking include:

Budget and business constraints: What can you afford, and what existing data and technology contracts do you have?
Use cases and insights: What types of insights about which stakeholders (e.g., healthcare providers, patients) do you need, and who will be using them?
Launch timeline: When do you need to have everything in place? In other words, how much time do you have to implement the systems, purchase and integrate the data, and extract the insights you need?
Pipeline timeline: When will other assets you have in the pipeline be launched?
Data compliance and connectivity: Is your environment compliant for the level of data (e.g., HIPAA compliant for patient-level data)? Do the different data sources have the level of connectivity needed to easily, securely transfer data?
Internal capabilities and analytical approaches: What expertise do you have on your team, and which capabilities will you need to outsource? What types of analytics will you need, and do you have the capabilities to support those?
Therapy area and market dynamics: Who are your competitors, and what is emerging within your therapeutic area? What external factors could influence each market you want to enter?
Patient access and unmet needs: Are there challenges to access treatment, and do you understand the unmet needs?

Using this information, you can create a checklist to evaluate your data sources and vendors in terms of:

Data coverage: Does the data cover your therapeutic area, your target patient population, payers, providers, and the time period you need?
Longitudinality: Can you track a patient’s journey through most or all their treatments? What timeline is covered for the population and individual patients?
Biases: Are the required sub-populations represented, do gaps in geographical or payer coverage exist, and is there equal representation of different patient characteristics as well as provider characteristics?
Backend: Is it a one-time purchase, or will it be refreshed and how often? Are the data tokenized and linkable to other sources? Are desired integrations easily achievable?

Before buying any data set, we recommend looking under the hood to prevent the need for additional purchases or major system changes later. For example, we reviewed a data set for a client that was priced at $300K+ and had empty health equity fields, including gender, for 80% of the patients. The company had to purchase and integrate additional data at a crucial time in their launch.

Investing in relevant, complete, clean, and current data sets your foundation up to successfully support all your commercial activities, from understanding your population to advanced analytics and reporting.

Choose tech for today and tomorrow

Choosing technology solutions often feels like navigating an alphabet soup of acronyms as well as a moving target, with the explosion of AI-enabled tools. Just as in selecting your data sources, it is helpful to use the why, what, how, when framework shown above to identify the essential pre-launch features and flexibility you’ll need to scale post-launch:

Review your use cases: You’ll want to ensure functional support for all your stakeholders.
Allow review: An easy, iterative review process of the systems, dashboards, and reporting during deployment allows your users to flag any concerns or suggestions for improvement.
Assess the technology’s integrations capabilities: You can ask your vendor for real-world test case studies, demos, or other examples of how they’ve integrated with the systems and data sets you’re planning on using and how they’ll support future integrations.
Look for vendors with clear roadmaps: Ask where they’re headed and for a proven history of keeping up with technology.
Compare multiple vendors: Before making partnering decisions, understand their offerings, service levels, and long-term support.

Launching a new product is stressful enough without the added burden of worrying about data quality and reliability. Investing upfront to build a solid, scalable data foundation ensures robust insights at launch and the ability to adapt to market shifts or an evolving pipeline. In addition, when (not if) your organization implements AI, the same foundation will readily support advanced analytics. We’ve seen firsthand that the proactive approach based on the framework presented here minimizes the risk of costly rip and replace cycles and sets the stage for a seamless transition to AI-powered insights.

SOURCE

Proprietary data from 30+ AI service providers. Everest Group.