The Top 8 IT Challenges in the Pharma Industry: What Really Matters

The pharma sector is often described as “slow to change” — but if we look closely, its IT landscape is anything but simple. Between shifting regulations, new standards, and emerging technologies, the industry faces an uphill climb to modernize while staying compliant.

The explosion of information technology is transforming the industry. AI-assisted drug research and clinical trials, automation and IoT in manufacturing, Big Data Analytics to understand patient needs are just a few new concepts we have learned in the past few years, and more are sure to come.

Let’s take a look at how we see the top 8 IT challenges the pharma industry is facing.

The Top 8 IT Challenges in the Pharma Industry

1.       Overall, accelerating digital transformation

Digital maturity varies greatly across industries – of course, IT leads and agriculture is last. While pharma is slightly more digitalized than the average (according to McKinsey Global Institute’s analysis, as quoted here), there is still vast room for further improvement, for example, in the fields of quality management systems (QMS), regulatory information management (RIM), updating legacy systems and supporting drug development.

2.      Data-driven operations

Big data can be helpful in many areas, e.g. supporting drug research, clinical trials, drug manufacturing. Ensuring the quality of data is, however, the first step – missing, outdated, erroneous or duplicated data will compromise the move towards data-driven operations. This is where ALCOA+ principles come into play – data must be

• Attributable: Any data should be attributed to the person who collected or modified it
• Legible: Any data should be intelligible and easy to read
• Contemporaneous: Data should be contemporary in nature
• Original: The original records of data (or an authentic copy) must be retained
• Accurate: Data must be error-free – and also complete
• Complete: Data must be available in its entirety, without omissions or deletions
• Consistent: Data must be logically organized in a timely order
• Enduring: Data must be recorded on robust electronic media for long-term availability (i.e. sticky notes, CD-ROM or USB sticks are not suitable)
• Available: Data must be accessible whenever necessary over its lifetime

These principles ensure that data remains relevant for BI/analytics purposes: a nice dashboard is not useful if source data does not meet these criteria.

3.      Compliance with various regulatory requirements

To ensure patient safety, pharma companies must adhere to strict laws, regulations and standards, overseen by agencies like EMA in Europe or FDA in the USA as well as local authorities. Key areas of compliance include

• GxP (Good Practices) covering several areas of operation including manufacturing, laboratory, clinical, documentation, distribution and storage
• Pharmacovigilance (PV) involving monitoring, collecting and reporting adverse events to maintain drug safety
• Data Integrity & Security to ensure the protection of sensitive health data and accuracy in all records

4.     Applications of artificial intelligence

AI can support the pharma industry in many areas, from drug research through clinical trials to manufacturing and pharmacovigilance. However, using AI is not without its challenges:

• Data quality is often insufficient, hindering the effectiveness of AI models
• Data privacy and security must be ensured at all times to prevent leaks and breaches of sensitive health data
• Expertise is often lacking to bridge the gap between AI experts and pharma scientists

5.      Automating manual processes

Repetitive, manual tasks offer huge room for efficiency improvement (and reduction of human error). This can cover a variety of areas, e.g. clinical data management (CDM), various forms of documentation, and activities aimed at regulatory compliance.

6.     Supporting operations

IT can support operations in the pharma industry in many ways, e.g.

• Manufacturing: production monitoring and optimization, digital twins using AI to improve quality control, etc.
• Supply chain management: product tracking, inventory optimization, supply planning, etc.

However, integrating legacy systems with new technology, ensuring data security, and finding experts with the right skills still pose challenges.

7.      Introducing and validating cloud-based and hybrid solutions

Cloud technology provides the necessary computing power for processing and analyzing massive amounts of data, enabling faster and more efficient AI-driven systems. However, moving core systems (e.g. regulatory information management, pharmacovigilance, quality management, etc.) to the cloud isn’t just “lift and shift.” Every change must be validated against regulations.

8.     Finding the right IT professionals and expertise

Going (more) digital means more need for IT expertise. Finding the right experts (software developers, testers, business analysts, project managers, etc.) to support internal IT teams is often a challenge in the pharma industry – especially when strong domain knowledge is required.

How do you see the key challenges the pharma industry is facing? Would you agree with the points listed? We would love to hear your perspective.