Structuring the Road to Digitisation: Management and Guidance for Data Automation
7 February 2022
Automation & Digitisation
Skills, Talent & Development
Meeting for the third time at the close of 2021, group 1 continues its work towards a roadmap and set of recommendations to the industry to progress digitisation. This will cover all aspects of a process including research, process development, manufacture, supply chain, and regulatory considerations.
Guidance on achieving incremental progress towards digitisation is important. Many companies, particularly SMEs, will enter GMP manufacturing on paper-based records. This is often due to a focus on speed when entering the clinic stage.
Flexibility in development: Why do we desire flexibility and how do we implement it?
Interest in this varies depending on which aspect of the product lifecycle a given company is working on. An R&D team will differ from a CMO, and touchpoints like quality management systems (QMSs) may present bottlenecks.
Systems and architecture: A key issue is that current systems governing systems architecture are not flexible enough.
How can we make this more efficient? A practical first step may be to work on the categorisation of data and its intended purpose before standardising a data pipeline. Guidance on this will allow organisations to quickly turnover this aspect of work, making it easier and faster to make informed decisions.
As the answer to questions of this manner will vary so drastically between specialisms and companies, the group aims to separately consider what efficient data architecture and handling mean before combining findings into an output.
Connecting the dots: While the concept of ‘data lakes’ is increasingly prevalent in the pharmaceutical industry, it nevertheless requires specific skills and what is often a significant cultural shift in the handling and perception of data.
This subject also should involve consideration of how we request written experiments and how we put together the data packages required for the release of a product.
The link to clinical professionals is also critical. In the clinical space, digitisation allows a company to provide information to clinical professionals and marry it to patient outcomes. This provides significant value. On an ontology level, alignment allows a company to harmonise and connect applications more easily.
Metadata and data attributes: How do we compartmentalise and stratify information? Based on previous discussions among the group, opinions on the best path forward vary. Many companies lack even the structure required to populate metadata, which again indicates the degree of cultural shift and drive required to affect a change.
Where and why is data value lost: What’s missing? How do we lose data and why is data missing in existing processes? Often, there is simply not enough being collected. This limits the ability of organisations to pursue digitisation and can require them to start from scratch.
Many factors impact this, including the loss of knowledge and talent due to staff turnover. Professionals who are effectively the custodians of datasets may leave a company without passing knowledge on, lowering or eliminating its value. Legacy or fragmented systems and those with poor documentation also cause issues.
Context is massively important. If you collect data without context, you can’t reconstruct it. Often, professionals in labs are aware of the context themselves but must pass this on as a process progresses.
There is also an awareness and skills gap between smaller and larger companies. Where a larger organisation can draw on or recruit talent for digitisation, SMEs may lack the skills or budget to secure what they need.
Business Case Tools
Broken once more into several themes, this discussion is aimed at the definition and scoping of business case tools for digitisation.
Capturing data early and at scale: A formal framework is an ideal output for us and will help companies to define what data should be captured and why. At its heart, the issue here is fetching data and structuring it in a useful way.
Industry-standard servers that are more open and share a common language are critical. This raises the need for proficient IT support which can enable and sustain a consistent flow of data.
Delivering a portfolio for CMC automation: A goal of the group is to provide a set of programs, projects, and technologies that will advance CMC automation. For this to be effective, data must be gathered at all stages. Understanding project requirements is mandatory; data should not be collected needlessly. At present, there are gaps between tech providers and users.
It’s also common for issues to develop with regulators where unexpected pushback is received. This is often due to concerns over “invisible” data which not collected by the applying company which is believed by regulators to be influential or critical. This common scenario reminds us of the high value of true data awareness and is relevant to the current discussion regarding ICHQ12. A wealth of operational data is also valuable for improving modelling, cost evaluation, planning, resourcing, and more.
Data management awareness: We must improve this as relates to regulatory requirements and business process and risk. Without this, proprietary platforms expand in numbers and are adopted across the industry, making future digitisation and automation difficult.
To further the awareness of data management for processes and risk in addition to regulatory concerns, cost and quantitative analyses will be vital. Our output should focus on this as a means of proving the value of digitisation, helping organisations to discover and defend their own key drivers. To help in defining and crystalising thoughts on this subject, it will be important for us to prioritise the subject of communication with both internal and external stakeholders.
For delivery of this area of our work, two specifics are needed. The first is a better understanding of data and IT infrastructure in companies, which becomes increasingly challenging the larger a business becomes. The second is people-focused, with courses and training combined with recommendations on the staffing of roles such as biostatisticians, data scientists, data engineers, and IT support. This also goes hand-in-hand with raising awareness of the value and function of these professionals within a company.
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