“In God we trust, all others must bring data”.
–W. Edwards Deming
Odyssey VC and Compliant Cloud CEO Oisín Curran gives a high-level overview of data analytics and looks to the possibilities ahead
Engineer and statistician William Edwards Deming paved the way for how analytics plays a key role across the lifespan of a regulated product today, and he gets straight to the critical point. We rely on and collaborate with our data scientists to build robust analytical models that inform and control the supply chain and manufacturing processes. Without data we will flounder, and crucially it must be accurate and reliable data. Let’s remember our first principles – garbage in, garbage out.
The foundation of an analytics model is a train; validate and test cycle followed by continuous model maintenance and retirement procedures. As such, analytics processes are developed in what are often called analytics “sandboxes”, or development environments. Here they undergo multiple iterations of development and improvement and are robustly tested prior to deployment into a production environment. But what resides in these sandboxes? Who has access to them? And how can we be sure of the integrity of the data underpinning these models that we are becoming more and more reliant on in production environments?
Believe it or not, in the past these sandboxes have existed on the data scientist’s machine. Yes, take a breath! Snapshots of data have been, and at times still are, gathered from varying sources such as the production historian, MES, LIMS and transferred by simple means to a single person. Processes are improving, and we now see analytics sandbox environments pointing to central and shared databases. However, the level of control of such environments is at best questionable. When dealing with the challenges that come with analytics processes, such as time alignment and cleansing of data, a large amount of data manipulation is required. When this is being undertaken on snapshots of data in relatively uncontrolled environments and by any number of data scientists across the enterprise, the opportunity for error is massive.
We need to get better at this. We need to ensure that the data which informs so much of a regulated products lifecycle is of the utmost integrity, whilst of course ensuring it is readily available to the teams and processes that need it most. Having data pertaining to the entire lifecycle in one space, ideally incorporating everything across R&D, Manufacturing, Quality and Supply Chain, means we can further drive efficiencies with reliable analytics. The centralisation of disparate data sources in a compliant and controlled environment opens a massive opportunity for efficient analytics and accurate, targeted decision making. The CompliantCloud.com team are passionate about data, data integrity in particular, and that drives the platform we deliver to our customers.
Imagine a world where data scientists are not just deployed to react but are continuously innovating and deploying analytical models to enhance operations. Imagine they had full end-to-end visibility of a product’s lifecycle in real time, predicting issues and informing preventative action. Imagine they were doing this in a controlled and compliant environment that satisfies regulatory requirements. Imagine no longer. It’s time to act. Then again, in the words of Deming again – “It is not necessary to change. Survival is not mandatory”.