Veeva exec: hidden flaws in your projected data might be costing you

This video provides an in-depth exploration of the critical role of data quality and strategy in optimizing commercial operations within the life sciences industry. Peter Stark, President of Compass and EVP of Data Cloud at Veeva, discusses how pharmaceutical commercial teams must adapt their go-to-market strategies to meet evolving stakeholder needs in an increasingly specialized and competitive market. He emphasizes that the industry's ultimate goal is to improve patient outcomes by ensuring accurate and early diagnosis, which necessitates a deep understanding of Healthcare Professionals (HCPs) and the changing landscape of medicine delivery, moving beyond traditional "white pill retail" to include procedure-based, in-office, injection, and infusion therapies. The core message revolves around the indispensable need for a robust data foundation to support effective analytics and AI initiatives, asserting that the output of these advanced tools is only as good as the underlying data.

Stark outlines three key components for a forward-looking data strategy, particularly concerning "projected data." First, a "complete view" is essential, integrating both retail and non-retail data sources to reflect the current complexity of medicine distribution. Relying solely on historical retail data for modern challenges is insufficient. Second, "full visibility" requires the ability to tie all data together across different levels, from national to subnational (zip, HCO, HCP), ensuring a single source of truth and accuracy, especially when building projections from patient data. He highlights a common pitfall where data bundled at different levels doesn't reconcile, leading to inconsistencies. Third, "unlimited access" to a comprehensive data network is crucial, challenging the historical practice of artificially constraining customer access to data. This unconstrained access empowers deeper analytical exploration and fosters a more inquisitive approach to data utilization.

The discussion then pivots to the significant risks associated with an inadequate projected data strategy. Stark warns that incorrect forecasting is the most immediate and impactful consequence, leading to a detrimental trickle-down effect on commercial operations. This includes misaligned promotional planning, such as incorrect allocation of field representatives, suboptimal digital media buying and placement, and inaccurate territory alignment. The analogy of an inaccurate snow forecast powerfully illustrates how being over- or under-prepared due to faulty projections can waste resources or leave critical needs unmet. Ultimately, these operational inefficiencies directly impede the industry's primary mission: getting patients on therapy as early as possible. Stark concludes by asserting that the single guiding principle for commercialization leaders should be data quality, urging a continuous evaluation of existing data strategies to ensure they are fit for purpose in today's and tomorrow's markets, rather than relying on past methods.

Key Takeaways:

• Evolving Go-to-Market Strategies: Commercial teams in life sciences must adapt to specialized and complex medicines by deeply understanding HCPs and the diverse channels of modern medicine delivery (e.g., procedure-based, injections, infusions) beyond traditional retail.
• Data as the Foundation for AI and Analytics: The effectiveness of scaling analytics and AI solutions is directly dependent on the quality and completeness of the underlying data foundation. Without robust data, advanced tools will yield inaccurate or incomplete outputs.
• Three Pillars of a Forward-Looking Data Strategy: A strong data strategy for projected data requires a "complete view" (integrating retail and non-retail data), "full visibility" (tying data across national and subnational levels for a single source of truth), and "unlimited access" to data to foster deeper inquiry and analysis.
• Pitfalls of Incomplete Data: Relying on outdated or incomplete data, such as retail-only views for modern medicine, leads to significant gaps in understanding the market and stakeholder needs.
• Risks of Incorrect Forecasting: A flawed data strategy results in incorrect forecasts, which have a severe trickle-down effect on commercial operations, including misallocation of field resources, inefficient promotional planning, and suboptimal digital media placement.
• Impact on Patient Outcomes: Ultimately, an inaccurate data foundation and subsequent incorrect commercial strategies can delay patients getting on necessary therapies, compromising patient outcomes and the industry's core mission.
• Guiding Principle: Data Quality: Commercialization leaders should prioritize data quality above all else, recognizing that a better understanding of the market, whether current or future, begins with superior data.
• Continuous Data Strategy Validation: Organizations should not passively rely on past data foundations or strategies. It is crucial to continuously evaluate and validate whether existing data and methods are sufficient for current and future market needs, rather than assuming past success guarantees future relevance.
• Beyond Just "New Data": Tackling new data streams isn't solely about acquiring novel datasets but also about ensuring existing data provides completeness and full visibility, and having the courage to re-evaluate how data is constructed and utilized.

Tools/Resources Mentioned:

• Veeva Data Cloud: Peter Stark is EVP of Data Cloud at Veeva, implying that Veeva offers solutions related to data management and analytics for the life sciences.
• Data Network: The concept of an "unlimited access to a data network" is discussed as a crucial component of a modern data strategy.

Key Concepts:

• Projected Data: Refers to data used for forecasting and strategic planning, particularly in commercial operations.
• Go-to-Market Strategies: The plans and approaches companies use to introduce products or services to new markets or customers.
• HCP (Healthcare Professional): A key stakeholder in the pharmaceutical and life sciences industry, whose interactions and understanding are critical for commercial success and patient outcomes.
• Retail vs. Non-Retail Data: Distinguishes between data from traditional pharmacy sales and data from other channels like procedure-based treatments, in-office administration, injections, and infusions.
• National vs. Subnational Data: Refers to data aggregated at broad geographical levels (national, state) versus more granular levels (zip code, HCO - Healthcare Organization, HCP).
• Single Source of Truth: The concept that all data points should originate from or reconcile to one authoritative source to ensure consistency and accuracy.
• Commercial Operations: The activities related to selling and marketing products, including sales force management, promotional planning, and market forecasting.