If you have any questions, please feel free to contact us.
Operationalizing Your GxP Data Lake: A Solid Foundation for Compliance in a Pharma 4.0 Production Environment
(4 min read)
Are you aspiring to become a data-driven enterprise in life sciences? Industry 4.0, or rather: Pharma 4.0, is your best bet. Data integration and cloud-based, data-driven analytics are key in understanding your processes and in generating more transparency. Main drivers of digitalization within pharmaceutical industries, like data science-derived manufacturing processes and product understanding through AI/ML, enable pharmaceutical industry experts to integrate their facility, site, supplier and client data for continuous improvement of products, processes and quality standards, and even to initiate new business models. This is how you stay on top of your GxP regulations and drive business value, by integrating your data and making clever use of data lakes.
Ahead of the Competition? Proactively Collecting Your Data Pays Off
Big pharma seems to be ahead of the game: across the top ten pharmaceutical production companies, all have integrated data lake setups, allowing their data scientists to look for and find relations across datasets and IT systems within the business. This can be used to, for example, build drug discovery systems, continuously improve production processes through production line feedback loops and personalize healthcare on the patient level with the use of algorithms.
By proactively collecting and analyzing all data throughout the organization as well as from external sources, companies are ahead of their competition: organizations that haven’t ventured into the domain of data science yet are quickly getting behind.
The knowledge gap between organizations that invest in data-driven technologies and organizations that don’t is growing bigger and bigger. Making use of your historical data is key in understanding relationships and allowing for predictions, also for smaller-sized organizations. This means that the effort to start collecting data from internal and external sources should not lag much longer.
From Risk Averseness to a Lack of Required Resources – Challenges when Integrating Your Data
Still, many companies choose to postpone moving towards a data-driven intelligent enterprise. Why is that? We often see that it’s a case of risk averseness. Because even though implementing a data lake set up can massively improve your GxP standards, the road towards it sometimes seems daunting when it comes to ensuring the quality and safety of your products and processes. Besides that, difficulties in understanding data science, the required technological advancements and knowing how to validate solutions built on top of data infrastructures make it seem like a complicated task. We also often see a lack of required resources in data engineering and data science domains (MLOps) and the inability to meet the data integrity requirements (ALCOA+).
Your Data-Driven Enterprise: Predictive Quality Management, and Many Other Possibilities
There is, of course, hope on the horizon. Because over time, after having overcome these challenges, the benefits of integrating your data are undeniable. Companies that are willing and able to formulate a sound data strategy in regards to Pharma 4.0 can expect endless possibilities, like more transparency and increased adaptivity. And it is entirely possibly to do so, without neglecting your GxP and quality standards.
With Pharma 4.0, organizations connect their data and generate transparency and adaptivity for a digitalized plant floor. The use of quality metrics increases process and manufacturing transparency and the integration of feedback loops within the life cycle allows for real-time improvement and compliance.
Breaking down the siloes to harmonize data in a data lake and applying data science solutions allows for, for example, batch record automation, smart pharmacovigilance or moving towards personalized medicine platforms. One of our best cases yet involves predictive quality management for (purified) water quality in production processes. We leveraged historical sensor data from a SCADA system connected to a WFI loop, and we combined this with sample outcome data from water monitoring procedures in LIMS and SAP data. This allowed us to organize datasets in a time-series database and to establish cause-and-effect relationships between environmental parameters and the impact on the WFI quality. We were then able to predict the quality of the water, generate data for batch reporting to provide proof of water quality adherence and perform root cause analyses on the spot.
Data Integrity By Design: Unlocking that True Business Value
Attaining Pharma 4.0 can be an overwhelming process. But that’s where we come in. By accompanying you towards becoming a data-driven enterprise, we help take away uncertainties and provide support in moving forward in a controlled way. We do this according to the concept of ‘data integrity by design’: our GxP Data Lake Framework helps you to attain a validated data lake set up, based on the internal rules and procedures of your organization. It includes templates and guidance to document transformation steps within data lakes, an insight in required lake zones and the possibility to monitor the data quality within the lake. This allows for better governance to meet requirements from a GxP point of view. It also factors in the possibility for GxP enhancements for data catalogues in order to manage all the data in the lake and to integrate a look-back functionality on top of it. This way you can proof that all built analytics, algorithms and applications (especially those allowing GxP-related actions) make use of data that is coming from validated sources.
By setting up a data lake according to our framework, organizations can directly start collecting all historical data that is required for data science solutions, unlocking true business value whilst staying on top of quality standards. We support you in all that is needed to start operationalizing your own validated data lake today.