he life sciences industry has long waited for big data to transform how new medicines are developed and delivered. With artificial intelligence (AI) and machine learning (ML) now coming of age, research and development teams can finally seize the opportunity if their data is clean, standardized, interoperable, and secure.

Biopharmaceutical companies layer together data from multiple disparate sources to understand a treatment’s potential for a specific patient. Some of these data sources will be common to all disease areas; for example, patient demographics, electronic medical records, and quality-of-life scores. However, the majority, including genetic information, imaging, and activity data from wearable devices, will be unique to each individual. Since the clinical effectiveness and safety profile of a new treatment will be different from patient to patient, all relevant stakeholders must be able to trust the data to make medical and business decisions confidently.

Reassessing the approach to quality, ownership, and connectivity will bring usable data to the core of the strategy, even when working with millions of relevant data points. Leading biopharma companies are also rethinking processes and systems to achieve “first time right” submissions. With access to clean data, they gain the insights needed to identify which areas can move the needle and speed up time to market for innovative new treatments that can help patients.

Speeding Time to Patient

Historically, data collection initiatives have been broad in ambition and scope. These ranged from sequencing, imaging, and electronic health record data to text-based information, like interactions with health authorities. The objective was data completeness, but the scale made it challenging to spot patterns or identify the most effective uses.

With go-to-market and approval requirements increasingly becoming more complex, biopharmaceutical companies can benefit from accessing and analyzing their study data far sooner. This approach shifts the focus from how data is collected to managing governance and ownership. Gaining more control and oversight will change the dynamics in biopharma relationships and contracts with third parties. Connected systems are becoming critical so relevant stakeholders can view the data anytime rather than waiting for data to be sent back in metadata formats or final text-based documents.

Sponsors can now more easily pinpoint the most impactful inefficiencies during the clinical development phase, which is vital to compressing time to market so that novel medicines remain commercially viable. Analyzing data on the cycle times between two critical clinical milestones could indicate whether inefficiencies and operational challenges typically arise during protocol design, site selection, initiation, or elsewhere. These insights can help the whole organization become more productive. A single source of accurate data can create competitive advantages by driving better decision-making on patent filings and patient recruitment and efficiency gains in outsourcing, procurement, or portfolio rationalization.

For example, one biotech company that centralized its clinical trial data lessened the overall effort required to capture data. The company reduced data entry for its partner research sites by 30% while also lowering the query and monitoring burden across their trials.

Even though analytical and data science capabilities have improved, limitations persist. Some companies that centralize their information find that raw data is not standardized, and industry or even intra-company reference models are limited. If common pain points around cleaning, ownership, and standards can be resolved, the volume and frequency of access to study data will increase. This will require a transparent data model with stringent user access controls to address privacy and cybersecurity concerns.

Establishing Clean Data Leads to Useful Big Data

Having clarity on the purpose and objective is vital when starting a data initiative for clean and accurate information. Prioritize these steps to get started:

Enlist experts to lead and inform: Suppose the goal is to accelerate the time from “first patient, first visit” to database lock. In that case, it’s best to choose a group of experts before data is collected and cleaned to decide the approach and exact use cases. Data scientists, subject matter experts, and even external experts (e.g., Health Care Providers, Key Opinion Leaders) could all help to make decisions and test hypotheses for improvements for this key clinical development milestone.

Establish a governance plan and secure support from leadership: Many biopharmaceutical companies have the right foundation of people and technology but struggle with effective governance due to siloed data sources, a lack of transparency across systems and teams, and unstructured information. Proper governance may require a more collaborative effort between functions that have not worked together much, such as research and basic science, business, and IT. Securing commitment from leadership is a prerequisite for companies to start thinking this way. Management teams must test, learn, and further experiment with different models before deciding which approach best suits the company’s culture.

Align with corporate initiatives and goals: Once crossfunctional roles and responsibilities have been defined, align people, processes, and technology to broader corporate goals, agreed problem statements, and hypotheses. This can help drive momentum for centralizing data and adopting strategies to make it usable and accessible much faster.

Agile resourcing is essential to prevent delays: An urgent drug safety issue, for example, could have immediate clinical (and downstream commercial) repercussions for a company unless the right experts come together quickly to tackle the issue. Clean data sets from one source are critical for statisticians, molecular biologists, chemists, medical geneticists, and data scientists to analyze and work through to get the drug back on track.

Once a specific situation has been well defined for applying big data, collaboration across teams will improve as all functions will be working toward a common goal. The result may well be higher-quality documentation, reduced cycle times, and more right-first-time submissions. The growing impetus for direct data application programming interfaces (APIs) that allow software applications to communicate with regulatory and health authorities (HAs)—and potentially contract research organizations (CROs) and other third parties—could lead to more cooperation. The benefits of better industry collaboration and faster regulatory decisions will be felt directly by patients.

More Strategic Data Use Across Drug Development

The costs and risks of developing new medicines challenge even the most efficient R&D functions. Enabling smarter data use can help companies break down the long and complex drug development journey to pinpoint which “little problem” to tackle first.

For large biopharmaceutical or biotech companies with siloed data sources and lengthy analysis timelines, consider bringing your data together into one environment and execute the steps discussed above. With a team of experts, leadership support, a governance plan, cross-team alignment, and agile resourcing, companies can centralize data and clean it efficiently. This foundation is the heartbeat for big data insights. When big data is clean, standardized, and connected across systems and functions, other exciting possibilities can be explored. This includes finding novel biological targets or new patient populations.

Eventually, a centralized approach to data management could support the long-held ambition of connecting real-world data—such as patient data, electronic medical records, digital therapeutics, etc.—to clinical development, so we can improve the patient experience. These advances will move the industry toward the shared goal of providing life-enhancing medicines to patients who need them.