Digital Transformation of Pharma and Biotech

Author photo: Janice Abel
ByJanice Abel
ARC Report Abstract

Executive Overview

Compared to other consumer-focused industries, pharmaceutical and biotechnology have traditionally been slow to adopt emerging technologies.  This is largely due to regulatory constraints, intellectual property issues, and a generally conservative culture.   That said, the digital transformation of pharma and biotech industries has already started, as many companies are developing roadmaps for digital transformation, and many have already started to adopt these newer digital technologies. 

Digitalization allows sensors, machines, equipment, and people to communicate and collaborate, while providing real-time data to improve both plant processes and the products they create. Digitalization should enable new approaches for innovation and creativity as opposed to simply enhancing or supporting traditional approaches.

For some companies, digital transformation, may just be a matter of going paperless (by digitizing paper-based data), connecting data silos, and/or implementing cloud-based solutions.  But for most, digital transformation includes technologies such as cloud computing, advanced analytics, cameras, video, augmented reality, and mobility.

While the Internet of Things (IoT) connects people, processes, data, and “things” over the internet; digital transformation includes integrating information technology (IT) and operational technology (OT) and data to enable better insights, optimize processes, and create efficiencies.  The Industrial Internet of Things (IIoT) extends the IoT into industrial environments. 

While we’re seeing some progress in adopting digital technologies for business and manufacturing processes in the pharmaceutical and biotech industries, manufacturers still struggle to exploit the full potential of digitalization. Often, cultural inertia – rather than technology - holds them back.  ARC believes that digital technology will drive value for pharmaceutical and biotech manufacturing, which – ultimately - should drive widespread adoption.

Along with newer therapeutics such as genomics and personalized medicine, the industry is evolving and adapting for the digital transformation.  Production processes will evolve to support higher volumes or adapt to personalized medicine with “batches of one.”    Additionally, the industry is experiencing a cultural change that includes a newer generation of workers. 

Major Challenges for Digital Transformation of Pharma and Biotech

In addition to operational problems, the pharmaceutical industry faces major challenges such as finding and gaining regulatory approvals for new drugs and then scaling up from laboratory to full production.  The regulatory challenges demand new and improved track-and-trace solutions.

While as many as 70 percent of pharmaceutical companies are initiating pilots, ARC has not yet seen widespread expansions and scale up of digital transformation technologies across the enterprise, where the real value is to be found.

For some companies the initial strategy is to first improve cybersecurity and data integrity and move regulatory documents to the cloud.  Others have begun advanced analytics initiatives and moved historian and other data to the cloud, edge, and new types of on-site devices.  At least one major data historian supplier has a cloud offering that some pharma companies are using for real-time data.  Other companies are implementing new enterprise architectures and platforms and scaling up digitalization, analytics, and visualization throughout the enterprise.  Most of the companies that have not yet started on the digital transformation journey are now developing their initial plans and strategies.

The need for more efficient methods to produce breakthrough therapies and biosimilars, along with strategies to manufacture conventional and new drugs more economically and reliably sparks investments in more innovative methods for making drugs and biologics. The shift to personalized medicines and gene therapies will require the production of small batches. At the same time, pressure to reduce manufacturing costs and avoid shortages and recalls demands more reliable methods for ensuring the quality of large batches of conventional drugs.

Digital transformation enables manufacturing companies to allow patients to play a more active role in their own care. Some trends will impact manufacturing within the next few years, while others may take a little longer to adapt due to the transformation that will require changes in technologies, therapeutics, people, and processes. 

While many companies view digital transformation as being technology-driven, in fact, it’s more about people and processes. Digitalization enables collaboration among coworkers and partners, improves access to data and intelligence across the organization, and provides tools for remote expert support.

As digital natives, the new generation of workers tend to adapt faster to newer and often easier-to-use technologies.  But where do workers fit in? The changes that digital transformation will have in the workforce are likely to be the most far-reaching and sustained effects.  Not only will it change the size and profile of the workforce, it will rewrite organizational structures and how the work gets done. Managing the human element successfully can be the most difficult aspect.  These transformation initiatives almost always have direct impact on humans.  Often, a well-conceived and executed change management process works best to minimize impact and maximize productivity.  Knowledge and expertise, hiring practices and staffing levels, teams and organizational design, reporting structures, executive support, sales and support, customer engagement, and more are all affected.


For digital transformation to succeed, training will be very important.  Not just for the manufacturing employees but also for the company’s customers.  Manufacturing teams should be trained for new techniques, working with new technologies and ways to use them better.

Some companies are investing in and empowering digitally-minded people at all levels of the organization.  These people need to help bring slower adopters on board with the new generation of technologies. 

Providing a Digital Transformation of Pharma and Biotech Culture

Adopting a digital transformation culture is one of the most important aspects of the digital journey. Pharmaceutical and biotech manufacturing companies need to adopt a startup approach and create a culture that inspires and embraces innovation.  Some companies support failures by acknowledging them as part of process and moving on quickly. This encourages innovation, new initiatives, and experimentation that helps determine future successes and opportunities for scaling up across the organization.

IT and OT Reporting Structures

While an increasing number of automation and control organizations in manufacturing now report to IT, some organizations are deploying IT people that report to manufacturing.  Manufacturing IT people and OT people need to see eye to eye.  Manufacturing leaders believe that IT needs to better understand manufacturing’s issues, processes, technologies, and culture. Without alignment and executive support for manufacturing, a cultural transformation would be very difficult.

Digitalization, combined with new manufacturing processes, automation, and therapeutics and personalized medicine have the potential to transform the pharmaceutical and biotechnology industries in new and exciting ways.

Digitalization represents a significant opportunity for companies to become more competitive globally.   Many companies have already started on their digital transformation journeys with cybersecurity initiatives and new platforms; moving data to the cloud; and adopting new technologies such as additive manufacturing, robotics, analytics, mobility and wearables. 

Digital Transformation of Pharma and Biotech japharmadt.JPG

Automated validation testing of scripts and regulatory documents and data are being moved to the cloud.  Companies are adopting advanced analytics to help them optimize their processes.  While not as widely adopted, augmented reality (AR) and virtual reality (VR) are also helping optimize processes.  Similarly, simulation and digital twins are being used to make faster changeovers and to help determine process bottlenecks ahead of time.

Enhancing Cybersecurity and Secure Computer Access

Because cybersecurity is a huge concern, there is a major effort to improve the security of manufacturing technology. Without proper attention, business systems, factory systems, machines, tools, and sensors, and engineering systems could be vulnerable to cyberattacks.   Companies are using multi-prong approaches to cybersecurity including upgrading to the latest security system, adding endpoint protection for older systems that can’t be upgraded, and locking down firewalls on the factory floor.

Most manufacturing companies today rely on software to automate processes, manage supply chains, and facilitate research and development, which could increase cyber-crime risk.  All stakeholders should be vigilant in implementing cybersecurity measures.  The supply chain has become an increasingly attractive target for cyber-criminals. However, attacks against many manufacturing companies tend to be targeted at stealing intellectual property (designs and customer lists), or interrupting operations. Even small- and medium-sized manufacturing businesses should not assume that their size precludes them from threats.  Mastering IT security issues provides a foundation for successful implementation of the digital enterprise. 

The US Food and Drug Administration’s (FDA) 21 CFR Part 11 regulations require secure access to computer systems.  Many companies have invested in two-factor authentication approaches, including biometric finger scans, hand scans, eye scans, and facial recognition. 

Data Connectivity and Data Management

Many manufacturing facilities have already been connecting their manufacturing data silos and most suppliers’ APIs or SDKs to make it easier to integrate data sources.  As companies digitize, more data silos will be connected.  However, the different organizations within a manufacturing enterprise (manufacturing, engineering, finance, marketing, etc.) store data in different environments.  Many suppliers have discussed master data management, but most manufacturing companies still have at least some data silos.

Master data can leverage technology and business processes to present data in a consistent, contextual, and timely manner.  However, if stored in the cloud, execution would often be too slow for many manufacturing processes.  In this case, the master data is located in the manufacturing facility or close to the process. 

Data and technology can enable a radical shift in a company’s business models and methodologies, but planning is critical.  A crucial component to digital transformation is dealing with staggering amounts of data.  This makes data management, contextualization, integrity, interoperability, and synchronization critical.  Audit trails regarding changes, physical location, and time stamps of changes are other issues that companies must deal with. 

Digital transformation provides an opportunity for companies to work with data experts and business innovators to leverage new technologies and develop new insights that can increase efficiency for operational excellence. 

Data Integrity for MHRA and FDA Compliance

FDA’s data integrity guidance emphasizes the need and requirements for “secure computation system access.” Controls must be in place to restrict the ability to alter specifications, process parameters, or manufacturing or testing methods. Other computer changes must be restricted, possibly by limiting permissions to change settings or data.

Data integrity is a critical focus area for both the FDA and Medicines and Healthcare Products Regulatory Agency (MHRA).  This is because without basic data integrity controls the agencies cannot rely on that company’s data or records to determine compliance, quality, or safety risks to consumers and patients. Data integrity is the cornerstone of FDA compliance, since data and documentation provide the only reliable information to determine a company’s actions and intent. FDA trains investigators to detect signs of data problems who look closely for signs of altered and doctored records.  MHRA’s “GXP Data Integrity Guidance and Definitions” publication describes the core elements of a compliant data governance system for manufacturing.

Any evidence of misrepresented data or problems with records related to current good manufacturing practices (CGMP) found during an inspection could lead to further investigation.  Here, FDA would focus on the greatest sources of risk to patients. Inaccurate or falsified data threatens FDA’s efforts to streamline regulatory processes. Companies with perfect quality systems will benefit from less FDA interference. Data integrity issues have real consequences. ARC believes that some of these risks can be reduced or eliminated using automated systems in conjunction with adequate procedures, standards, and enforcement policies. 

Excellent quality data has always been important to FDA because data integrity issues can lead to serious CGMP violations.  Dealing with poor data slows down FDA inspections and costs both the agency and the business, money. Unfortunately, data integrity issues are not uncommon and enforcement in this area is increasing.

FDA’s Data Integrity and Compliance with CGMP Guidance for Industry, clarifies the role of data integrity in CGMPs for drugs. The guidance covers the agency’s current thinking on creating and handling data in accordance with CGMP requirements.  FDA believes that ensuring data integrity is an important component of industry’s responsibility to ensure the safety, efficacy, and quality of drugs, as well as its own ability to protect public health. This is a key part of the digital transformation in these industries.  ARC believes that it is in everyone’s best interest for pharmaceutical companies to implement effective strategies to manage their data integrity.

All therapeutics shipped to the US must be produced under processes that comply with US FDA CGMPs and all computer systems used to produce a therapeutic must be validated and meet 21 CFR Part 11, which includes the accuracy, reliability, integrity, availability, and authenticity of required records and signatures.  Other global regulations such as those of the European Medicines Agency and MHRA also apply for companies that ship products to those countries.  However, the US FDA tends to have the most stringent policies.  While promising, the movement toward global regulations has been in the works for many years.

Enterprise Architecture and Integration

Consider the Internet of Things (IoT) platform as the middleware. Most IoT platforms include a communication network and software for monitoring, troubleshooting and administrating the connected devices and managing the network and data management.

Some IoT platforms have software for translating or analyzing the data and support for developing apps.   The IoT platform is the connector between the data collected from the “things” – devices or sensors at the edge and the user facing applications. 

Pharmaceutical companies are updating automation systems to adapt to the digital transformation.  Many of the systems in older pharmaceutical manufacturing facilities have not changed in over two decades. Many were implemented for Y2K and are now getting a major update to adapt to newer standards and the digital transformation. A lot of older systems are being replaced, since platforms older than 10 years often experience issues with support, maintenance, integration, and security.

Pharmaceutical and biotech companies are beginning to make use of the cloud to store, access, and connect all their data from research to clinical trials to manufacturing to the supply chain.  Their cloud-based infrastructure will enable participants in the pharmaceutical and biotech industries to connect all ecosystems and enable real-time communication between globally disparate production systems.

Moving Manufacturing Data to the Cloud

Much manufacturing data is being moved to the cloud.  Typically, this includes non-mission critical data, such as historical or aged data.   But data from historians, recipes, LIMS, MES, PLM, etc. are also being moved to the cloud.  Most of the data is in on- or off-premise private clouds.  With the appropriate controls in place, data can be distributed across a cloud’s hardware in several geographic locations.

While hesitation around cloud adoption in the pharmaceutical industry continues, current cloud technologies (even public clouds) can be more secure than what the companies can provide internally.  This is particularly true for smaller companies with limited IT resources. 

Moving Regulatory Documentation to the Cloud

After cybersecurity, the movement of regulatory documentation to the cloud is currently the largest focus area for the digital transformation in the pharma and biotech industries.  Many regulatory documentation technologies are being deployed in the cloud.  Users want fast uploads to transfer GMP data and the ability to query, access, and make data available later.

FDA’s recently updated guidance for industry, Data Integrity and Compliance with CGMP, mentions cloud infrastructure as being part of computer systems.   This enables the use of cloud for regulatory documentation, including the documents required to validate the manufacturing process. 

Data Lakes and Big Data Investments

Companies are starting to invest in data lakes to store, manage, and analyze data.  Some are putting structured and unstructured data in the cloud.  This includes GMP data, with unstructured data dumps for non-GMP data.  Some are using data historians to feed data into the cloud. Others are using semantic graph technology to help analyze dissimilar data. In concept, this allows IT and end users to combine diverse sources of structured and unstructured data including videos and images, internal and external into a vast data knowledge network.  However, data integrity needs to be ensured and all data must be attributable, legible, contemporaneous, original, accurate, accessible and subject to easy change management.

Additionally, data lakes are working with existing enterprise data warehouses for an integrated approach to data.  They cost less to operate than enterprise data warehouses for most applications.  Pharmaceutical and biotech companies are using these for research and clinical trial data and starting to use data lakes for manufacturing too.  Many companies are using data lakes to pull data from multiple sources as a research dashboard with analysis and visualization.

Data is usually stored in their native format and then contextualized when needed.  Real-time and relational databases are typically managed as part of the data lake to make it easier to access some of the data sources.  Because the data are stored in their raw, uncontextualized format, it’s easier for users to reproduce what actually occurred if they do not know what they are looking for ahead of time or the interrelationships

On the other hand, some of the real-time automation data or relational databases that require time stamps, more specialized historians, and/or faster insights will still be used in conjunction with the data lakes and cloud technologies. This is because historians are more efficient at aggregating, contextualizing, and storing real-time data, including all the metadata for time-stamped data such as alarms, limits, and values that are also part of the data store.   Other companies are “on the fence” about data lakes because they believe that once the volume of data gets high enough – it may be difficult to analyze the data or take too long to analyze.

While cloud and edge both play a big role, early adopters of digital trends tend to be the smaller and medium-sized companies due to the cost savings from having someone else manage IT and cloud.  

By moving data to the cloud, manufacturers can connect, interact, integrate, and collaborate on an unparalleled level.  Some companies are enabling their OEMs to maintain the equipment or the systems so that they are always up to date and prevent any potential downtime.  Collaboration will ultimately enable safer therapeutics. 

A cloud-based environment can connect all the data from the various data silos and seems to represent an ideal method to manage, deploy, access, and maintain this critical data.  Cloud also enables a paperless manufacturing environment for documentation including regulatory documentation.


  • Executive Overview
  • People, Process, and Culture
  • Technology Adoption
  • Business and Manufacturing Processes
  • Critical Digital Transformation Initiatives
  • Recommendations


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