This interview in in two parts. Part 1 can be read here.
What kind of master data is associated with clinical trial data?
The pharmaceutical industry has a wide variety of specialized tools for handling clinical trial data and few of them are designed to use or handle master data. However the whole clinical trials process is full of master and reference data. If you think about it, the clinical trial, or clinical study, is itself a master data entity. It has a unique identifier and a study title. To operate the trial within each country, you need a licence from the relevant regulatory authority, which itself has an identifier. You also need to identify the products within the clinical trial and the indications (or medical conditions) that it is focused upon. Then there are the Study Sites, including the primary investigators and the healthcare organisation (or facility) who are recruiting patients and conducting the clinical trial. And of course we have human subjects, who are the anonymised patients involved in the trial, and frequently biospecimens that are collected from the patient to support further investigation and research. All of these can be considered master data. There’s an even longer list of Reference data, things like Study Phase, Study Type, Country, Route of Administration, Dose Form – all of these need to be standardised to correctly interpret the clinical trial.
What kind of innovative approaches are you building to counter your challenges with the transformation here?
The main change we are driving is to recognise that this data needs to be separated from the operational systems and integrated via REST API’s. We are already well advanced on this path with our Reference Data Platforms. We combine this with a strong data governance function who ensure that data is managed and trustworthy.
The other element of innovation is to combine our delivery of reference and master data with published and internal ontologies. Implementing in this way establishes rich links in our data which we can exploit in Knowledge Graphs/AI and analytics.
How do you see the impact of Covid on the healthcare industry and your work in particular?
Covid has had a big impact on AstraZeneca, ranging from development of new Vaccines and monoclonal antibodies through to implementing covid testing processes. We’ve even invested time in changing the way we plan and operate clinical trials to work within the pandemic.
As a data leader, what is your view on technologies like AI on MDM and other data solutions?
I see the solutions that I’m responsible for as providing the foundations for AI by providing clean, trustworthy linked data.
I’m always cautious of statements that I often hear that AI will eventually replace technologies like MDM. The reality is that all technologies will be supported by AI in the future, but the role of master data management in providing unique identity for data will remain and will always need strategic thinking.
Editor’s note: Agree wholeheartedly. Data mastering is the foundation for AI and analytics.
How do you see the impact of cloud technologies on your day to day work?
Cloud is built into the way we work at AstraZeneca. Our MDM and RDM platforms are both cloud based.
You have a background in Maths and Physics and are now working on Chemical Substances. What is your learning strategy to evolve to new aspects of your work and what are some tips and techniques you would like to share with our readers.
A lifelong ambition to learn about science is essential to my role. We’ve not touched on it, but I’ve also found learning about biomolecules, such as proteins/monoclonal antibodies to be even more fascinating than chemical substances. I would never describe myself as an expert, but I’ve found that I can quickly scan text books and web sites to learn sufficiently about the concepts to be able to have an effective conversation with scientists about their data. This is absolutely key to master data, the world of science is full of localised and conflicting terminology and you frequently find several terms are used to describe the same thing in different groups or even within the same process. Terms like product are particularly bad and can mean multiple things. This is where I apply my scientific knowledge to probe what the scientist really means.
It’s also important to look at the data itself. Often when you observe the data you spot patterns in the data that weren’t revealed in discussions with the scientists. If you can decipher the pattern, you can understand what is happening and you may be able to propose a significant improvement or simplification.
What is your advice to someone starting on the MDM journey?
- MDM is not about technology – paradoxically it’s mostly about process. Understanding the data and the process used to create that data is the most critical thing. You should always identify the process that creates the data entity first and you should always define an end to end process for managing the master data.
- You must really understand the data…anyone who talks vaguely about mastering something like product, without defining what that means, will not be successful.
- An additional point is that you should never expect an MDM process to arrive through consensus; most organisations will try and retain their existing processes and you’ll end up making multiple concessions and will fail to generate trustworthy data. As an MDM architect you have to be prepared to define the way forward and argue your case as a simplification.
Thank you so much Colin for these insights. We wish you the very best of luck in your upcoming tasks and for the future. Stay safe!Tweet
« Master Data Maestro – Colin Wood