With data assets increasingly seen by biomanufacturers as strategic, business managers demand visualizations, reports and want AI to provide predictive insights and inferences. However, creating that output is only the tip of the iceberg: 80-90% of the effort has to go into preparatory work that senior managers never see. All large companies need data strategy and governance. With many systems contributing disparate data, there needs to be a data catalog to make it findable, and master data management to make it usable. That relies on a data ingestion and transformation strategy, and a stack of tools that form a data and AI platform.
Implementing data architecture frameworks is not straightforward
As manufacturing plants have been built or acquired over the years, biomanufacturers often have several systems of the same broad type, but from different vendors and different generations. Particularly in a GxP environment where filings restrict change and every update needs to be validated, the operational systems take a long time to converge, and data remains hard to use. Ownership is often local to the facility, making global insights hard to pull together.
There has been much thinking from hi-tech companies on the principles of data architecture. However, it is difficult and unclear how to implement the principles of data mesh and other frameworks in a biopharmaceutical setting. In order to make sense of data, it needs to be business people that take ownership of data, but skillsets and strategy are undefined. There is no best practice information architecture for the pharma industry – which limits the scope for reuse. We need systems to interoperate smoothly. There is no industry-established framework for data governance. Data quality is not improving over time. There is a plethora of tools and infrastructure technologies and it is not always clear how to use them effectively in a pharma organization.
Therefore it is difficult to meet new business needs for data products, visualizations and AIs efficiently, and empower users to make well-informed decisions.
By sharing the experiences in a group of practitioners with strategic oversight of data architecture in a range of biopharmaceutical organizations, we are getting a more complete and more widely agreed understanding of the distinct challenges we face. As case studies are shared, we reduce risk by learning from the experience of others and accelerate the analysis, design and build of data products. By combining those experiences into a set of guidelines, we aim to increase our maturity in producing and managing data products, increasing the impact and value gained from data assets.
Alignment between sponsors, CMOs and vendors will simplify and reduce costs of digital transformation in the extended enterprise. Once industry-wide approaches and standards become established, it creates fertile ground to innovate advanced analytics and harvest more benefits from data assets.
Data Architecture deliverables
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