Data Quality: A Pillar for AI and Machine Learning in Pharma Marketing

In the pharmaceutical industry, data quality is not just a technical necessity—it is critical for improving patient outcomes, ensuring regulatory compliance, and driving marketing success. As AI and machine learning become integral to pharma marketing strategies, their effectiveness depends on robust, high-quality data.

Pharma companies are increasingly adopting AI-assisted analytics to enhance decision-making, deliver personalised marketing, and streamline reporting processes. The year 2023 saw an unprecedented surge in AI solutions across industries, with 73% of new martech products designed to integrate AI-driven capabilities across areas such as content marketing, analytics, and eCommerce intelligence(1). For pharma, these applications offer transformative potential, but only if built on a foundation of clean, reliable data.

Leveraging Data from Digital Platforms: GA4 in Pharma

GA4’s powerful machine learning features provide predictive insights that can be invaluable for pharma marketers, especially in identifying audience behaviours and anticipating campaign outcomes.

Before connecting digital data to your cloud environment for broader reporting, it’s essential to audit the accuracy of your user and campaign tracking. This ensures that key interactions—such as patient portal activity, prescription requests, and healthcare content engagement—are correctly captured and optimised. High-quality data collection helps unlock GA4’s full potential, from generating actionable insights to leveraging predictive analytics that can drive marketing efforts targeting healthcare professionals (HCPs) or patients.

GA4’s built-in machine learning capabilities also make it easier to surface insights like website performance anomalies and shifts in user behaviour. For instance, detailed event tracking can reveal patterns in how healthcare professionals engage with your content, while predictive metrics such as churn probability or predicted revenue can guide campaign targeting and allocation of ad spend on platforms like Google Ads.

Optimising Pharma Data Ecosystems for AI

In the broader pharmaceutical landscape, Google Analytics is just one data source. To fully harness the power of AI and machine learning, pharma companies must integrate data from multiple systems—clinical trial results, CRM, HCP engagement platforms, and patient feedback. AI models thrive on large, well-organised datasets, and the pharmaceutical industry, with its rich data sources, is uniquely positioned to benefit from this.

The first step in your AI journey should be ensuring your data is structured, clean, and centrally accessible. By consolidating data into a single source of truth—such as a cloud data warehouse—you can enhance the efficiency of marketing campaigns, improve compliance reporting, and enable deeper insights into drug performance, market penetration, and patient behaviour. Data centralisation allows pharma companies to gain a holistic view of their marketing efforts, helping to assess the return on investment for various outreach programmes targeting HCPs and patients.

Integrating data from different platforms is a complex but crucial task. It requires creating automated data pipelines, preprocessing to remove inconsistencies, and mapping key identifiers—such as product codes or prescriber information—across systems. This enables pharma companies to create robust dashboards that seamlessly blend data from marketing, sales, and clinical sources. As a result, these dashboards provide a comprehensive view of marketing performance, helping to target campaigns more effectively and make data-driven decisions.

AI-Assisted Data Management in Pharma

AI tools like Google’s Gemini for BigQuery are transforming how data is managed, even in pharma. These AI-powered collaborators(2) help data engineers streamline workflows by making code suggestions and identifying potential data anomalies. By automating repetitive tasks, AI can free up resources, allowing pharma marketing teams to focus on refining strategies that improve patient engagement or HCP outreach.

For example, AI-driven data checks can automatically flag discrepancies in prescription tracking, identify trends in patient adherence, or optimise the targeting of marketing campaigns based on predicted HCP behaviour. This speeds up the data preparation process and ensures that pharma marketers are always working with the most accurate and timely insights.

Preparing for the Future of AI in Pharma Marketing

Natural language processing (NLP) capabilities in AI are rapidly evolving, making it easier for marketers to ask questions of their data and get actionable answers. Pharma marketing teams will soon be able to use AI tools to explore data, generate insights, and create reports simply by asking questions in everyday language. This shift will democratise data analysis, enabling more stakeholders—from marketing to compliance teams—to access critical insights.

Centralising and transforming data today is essential to unlocking future AI-driven insights. Pharma companies that invest in robust data infrastructures now will be well-positioned to experiment with and adopt the next generation of AI tools that can revolutionise their marketing efforts, improving everything from patient acquisition to HCP engagement.

Conclusion: Build a Data-Driven Future in Pharma Marketing

For pharma marketers, a focus on data quality and centralisation will enable successful AI applications, whether you are targeting patients, healthcare professionals, or other stakeholders. High-quality data is the bedrock of effective machine learning models, and it’s never too early to begin preparing your data ecosystem for AI’s transformative potential.

Tom Carter is a Business Director (Technology) within Space & Time, working closely with global pharma clients as part of the Health and Pharmaceutical division, headed by Nick Beckingham.