Data Integration for Biotech and Pharma Innovation

Biotechnology and pharmaceuticals are trillion dollar industries. You’d think they could afford to fail - and they can. But here’s the catch: Each drug is over 12 years in the making and costs a staggering 1 billion pounds to develop. And for every new drug that reaches the market, on average, 9 others have failed.

This represents an enormous amount of manpower, resources and lost opportunity to advance the field of medicine.

But for many of the reasons that costs soar, timelines elongate and trials fail - stricter regulations, scientific complexity and evolution, as well as concerns over legal liability - data integration might have a solution.

The ability to combine, format and visualise vast amounts of disparate datasets from various medical sources, from previous trials to real-time patient data, could fast track the process from R&D to scientific breakthrough.

In essence, an efficient and actionable information ecosystem has the potential to reduce a billion-dollar failure.

The power of unified data is so apparent that the pharmaceutical data and analytics market is experiencing incredible growth, and is predicted to expand from $1.47 billion in 2024 to $4.18 billion by 2034.

Data integration in biotechnology and pharmaceutical fields refers to the process of combining data from multiple sources, formats, and systems to create a unified information ecosystem that scientists and clinicians can use to advance their work.

The data combined covers everything from traditional clinical trial data and lab results to genomic information and health equity data.

However, the data poses quite a challenge not only to collate and clean it but also to present it in a way that it can be used to derive meaningful insights due to its seemingly endless variety. Everything from structured databases to free text files, image files, and streaming sensor data need to come together in clear and consistent workflows.

Data integration isn’t just a hack for driving efficiencies here and there; it can fundamentally alter the innovation process. Using AI, machine learning and natural language processes, for example, researchers and clinicians can visualise and build insights from nuanced patterns between diverse data points and sources. 

The role of AI in drug discovery has already been transformative. 

In 2024, the Wall Street Journal reported that the number of clinical drug trials where AI either discovered, designed or repurposed molecules, vaccines and target diseases increased from 27 in 2021 to 67 in 2023, according to Boston Consulting Group data. 

Data integration was essential to this effort. Without the vast datasets on which AI algorithms rely, the numbers just wouldn’t be possible.

Optimising clinical trials using data integration represents one of the most immediate and fruitful applications. 

One organisation saw a 30% reduction in development costs, 25% reduction in time to market, 20% increase in operational efficiency by integrating workflows, which allowed them to better coordinate efforts between teams, access real-time data, avoid duplicate analyses, and reach final, evidence-backed decisions quicker.

This is groundbreaking for the R&D phase. Beyond clinical trials, some estimates believe that by using integrated data, medical device manufacturers could also reduce effort in the development phase by 75%, taking creation time from one month to one week.

Integrated data can also potentially break down barriers with regulatory agencies, which require highly sophisticated solutions to demonstrate absolute safety and efficacy across varied patient groups before approving a drug.

And when all drug trials - the successes and failures - are operating with integrated data, they become bigger than the sum of their parts. By analysing the result of each, researchers can avoid retreading old ground, develop new approaches, and accelerate the timeline to discovering a new drug.

Even in the face of proven benefits and potential ROI, uptake of data integration strategies among biotech and pharma organisations is lagging behind other sectors.

As of 2022, only 20% of life sciences and biopharma companies had successfully carried out digital transformation plans, compared to 35% of companies in other industries.

But pharma and biotech come with their own unique challenges that cause those in the industry to tread more carefully.

Healthcare, for example, has the highest volume of third party data breaches (35%), making data security, particularly across the vast datasets in question, a significant concern.

Hesitation in this area is understandable when the repercussions can cost millions. In 2022, a French software company was fined €1.5 million for a data breach affecting approximately 500,000 people, which not only had a financial implication but also undermined public trust and damaged the company’s reputation.

Size and variety of datasets also plague the technical integration process and raise concerns for an overwhelming majority of organisations, 92% of which are concerned about fragmented data and data volume, and 93% of which are concerned about data formatting issues. 

Throw the reality of sensitive patient data and possible adverse drug outcomes into the mix and the path becomes all the more treacherous. Not to mention, successfully and safely integrating data of this magnitude, particularly from legacy systems and siloed data repositories, requires significant manpower and expertise before researchers can begin using it.

The sheer cost of drug development leaves little room to maintain the current failure rate, particularly when competing drug companies are already closing the gap through data integration.

In a market which is seeing rapid growth in this area - healthcare big data was valued at $22.2 billion in 2024 and is projected to reach $58.4 billion by 2033 -  a business’ reputation, competitiveness, and sustainability relies on its ability to act now and make the necessary upgrades in order to capitalise on the possibilities of integrated data. 

The missed innovations are also a tragedy for the field itself. 50% of clinical trials are left unpublished, amounting to a huge loss of potential learnings for future trials. Data siloed or shielded from view in legacy data systems present further missed opportunities to avoid duplicating efforts and work collectively towards faster and more successful outcomes.

And when AI has proven so valuable to the analysis and discovery stage when applied to integrated data, it seems an immense waste not to use it.

Businesses also open themselves up to greater risk when data stays fragmented, not just from failed trials but also to delays in approvals from regulatory bodies which require heavily data-backed studies. Instead of learning from past trials, drug companies may find themselves running additional clinical trials needlessly, or face rejection due to insufficient evidence, and waste years of development effort and investment.

Companies also need to consider the competitive edge afforded to them through attracting top talent. These candidates will seek out companies with cutting-edge technology, the latest research, and a history of successful outcomes to give themselves the best chance at achieving their goals.

Data integration requires a plan of action in order for businesses to work meaningfully towards improving their processes and give themselves a competitive advantage in the industry.

The first involves setting strategic goals for data integration, such as increasing the time to market for a new drug or improving your current success rate in clinical trials, as this will focus all subsequent efforts.

These goals are also important to consider alongside what are potentially competing objectives or considerations, such as patient safety, data security, and cost reductions.

Auditing existing tools, databases, and workflows allows you to analyse current processes against your strategic objectives and how well they serve them. It’s also a necessary due diligence activity as well as a step towards collating disparate datasets within the business.

All resulting efforts depend on ensuring data integrity and stewardship, and the ability to prove this to regulatory agencies. 

Create clear policies for data ownership and retention, access, and quality standards. Your governance strategy should also lay out the responsibilities for data handling and protection across departments.

Technical implementation is a tricky phase and requires careful selection of integration platforms. Choose those that can handle the vastly diverse data types found across pharmaceutical and biotechnical research. 

Modern integration solutions should be able to synchronise real-time data, offer collaboration between teams, and provide solutions to scale up in the future as your data grows and evolves.

Successful data integration depends on user buy-in, which means training for all staff operating the system, working with the data, feeding into it, and maintaining it.

This includes heavy docs on data governance and quality to ensure the system remains fit for purpose and on track to achieve its strategic goals.

While the road to comprehensive data integration requires thorough and sequential organisational change, it paves the way for long-term success for businesses, drug trials, and the patients they serve. 

Explore the wide range of data acquisition, sensing, and control products from leading brands on the RS website to get started on your integrated data project today.