The top three data challenges impacting pharma in 2025

Pharma data management

As the pharmaceutical industry continues its digital transformation, how data is managed and handled has become more critical than ever. Currently, pharma companies are facing several key challenges related to data and data management that could determine their success or failure in this increasingly competitive landscape.

ONTOFORCE has over a decade of experience in the life sciences industry and throughout this time we’ve witnessed how organizations struggle with handling, managing, and leveraging their data as industry trends, regulations, and technologies shift. Modern pharma data management requires a commitment to strong data governance while maintaining agility. As the next trend or wave of technology crops up, data strategies and solutions need to be able to evolve to meet the changing demands.

In this blog, we are covering the top three data challenges that pharma companies must navigate in 2025 against the backdrop of the fast-paced technological and regulatory changes characterizing this and recent years.

1. Data integrity issues and poor data management processes

Maintaining high-quality, reliable data continues to be one of pharma’s most pressing challenges. Inaccuracies, incomplete records, duplicate entries, and inconsistent formatting are all too common, and their consequences can be severe. From undermining clinical trial outcomes to delaying regulatory approvals and impairing patient safety, the ripple effects of poor data integrity are far-reaching.

The issue of data quality can find root well beyond technical issues. Inconsistencies across datasets, incomplete or inaccurate annotations, or reliance on outdated standards contribute to the (poor) quality of data. These hidden flaws can quietly flow across the drug development timeline, ultimately jeopardizing experiment reproducibility, testing results, or trial design protocols. While the true cost of poor data quality is likely invisible at first, it’s often profound in the long term: delayed timelines, resource drain due to costly mistakes, or even a misguided decision to abandon an otherwise potentially promising project. It is thus hard to accurately quantify just how costly poor data quality can be to an organization but according to a recent Forrester report, millions of dollars were lost across industries in 2023 due to poor data quality, with some employees even estimating their organization lost $25 million or more.

Data integrity and data management processes are deeply interconnected: strong data management is the foundation that upholds data integrity. Without clear, defined processes for data collection, validation, storage, and governance, organizations leave themselves vulnerable to errors, inconsistencies, and compliance risks. Data integrity refers to the accuracy, consistency, and reliability of data throughout its lifecycle, but achieving that integrity depends on disciplined data management practices. These include standardized data entry protocols, regular audits, defined ownership and accountability, and robust systems for version control and traceability, to name a few.

Data management is the operational discipline that ensures data remains trustworthy, actionable, and compliant, making it a critical enabler of research and regulatory success. One striking example is the 2019 case of Zogenix, which faced an FDA denial for its seizure-control drug Fintepla due to missing toxicology studies and submission of an incorrect version of a key clinical dataset. The missteps in data management led to a significant regulatory setback and a drop of over 30% in the company’s stock value—highlighting just how costly poor data management can be.

Breakthroughs and market approvals may be the visible outcomes, but it’s the strength of underlying data management processes, and the integrity they uphold, that ultimately determine whether those milestones are reached at all.

2. AI-ready data

Closely tied to data quality and data management is the growing challenge of ensuring data is ready for artificial intelligence (AI) and other secondary uses. The success of an AI initiative or project hinges on the quality and structure of the data it relies on. Unfortunately, many pharma organizations are still grappling with fragmented data sources, legacy IT systems, inconsistent formats, and the absence of standardized protocols, all of which pose major barriers to AI readiness.

According to Gartner, 63% of organizations either do not have , or are unsure if they have, the right data management practices to support AI. Even more striking, Gartner predicts that by 2026, 60% of AI projects that lack AI-ready data will be abandoned altogether. While these are wider trends across industries, these stats aluminate the pertinence for pharma companies to move beyond haphazard approaches and experimentation to deliberately insuring they have a strong data foundation and data management processes in place to operate from. Without this, organizations expose themselves to risk, wasted resources, and lost time that could translate into millions of dollars of lost market share.

A key part of a strong data foundation is adopting the FAIR data principles: Findable, Accessible, Interoperable, and Reusable. These principles ensure that data is structured and standardized, and ultimately ready for AI use. As Gentiana Spahiu Pina, Director and Data Governance Lead at Pfizer, explains:

“FAIR primes data for AI use by enabling trust and quality. What are the implications of putting data that we don't trust the quality of or we don't know the provenance of into an AI model? What is the liability of that? FAIR plays a very critical role in terms of building the credibility for the data sets that are inputted into models.”

Here more from Gentiana and other FAIR data experts by watching the on-demand recording of ONTOFORCE’s 2024 FAIR fireside chat.

3. Empowering a data-driven culture

Even with the right tools, infrastructure, and governance in place, pharma companies cannot unlock the full potential of their data without the right culture in place. Technology alone is not enough. It's the mindset and behaviors of people across the organization that ultimately determine whether data is used effectively to drive decisions, innovation, and value.

In many pharma organizations, data still lives in silos, guarded by departments or trapped in legacy workflows. Teams may be hesitant to share data across functions, lack trust in data quality, or simply be unfamiliar with how to interpret and act on data-driven insights. These cultural and organizational barriers often slow down digital transformation efforts, regardless of how advanced the underlying technology stack may be.

We at ONTOFORCE witness these challenges every day in our work with pharma companies across the globe. We’ve seen that even the most advanced companies, with impressive, resource-intensive technological ecosystems in place, are still struggling to adopt and foster a data-driven culture. Often in these cases, organizations aren’t getting the most out of their data and they aren’t fully realizing the potential of their tech investments.

Pinpointing the root of cultural resistance to data-driven practices isn’t always straightforward. Some argue it starts at the top, when leadership fails to prioritize certain data strategies or technology, it sets the tone for the rest of the organization. Others point to a more distributed challenge: ingrained “anti-data-sharing” mentalities embedded within teams and departments. These silos, whether driven by legacy processes, risk aversion, or internal politics, can block the flow of information and hinder collaboration. Addressing this requires more than just policy, it demands structural support for cross-functional collaboration, enabled by interoperable systems, clear data governance, and a shared understanding of goals.

Ultimately, empowering a data-driven culture in a pharma organization shouldn’t be viewed solely through the lens of efficiency or operational improvement. At its core, it’s about recognizing that fully leveraging data can be the difference between delay and discovery, and, in many cases, between delivering a life-saving treatment for patients or not. When teams are aligned around high-quality, trusted data, they can make faster, more informed decisions that directly support the timely development and delivery of innovative therapies. In an industry where time and accuracy can directly impact patient lives, cultivating a data-first mindset isn’t just a competitive advantage, it’s a necessity.