The amount of clinical data is compounding at an astonishing rate. Largely, this is due to our increased ability to generate and collect vast amounts of data that was previously left untouched. While this new data holds tons of potential, it also presents new challenges like frequent changes to oncology standards, uncurated backlogs of clinical data assets, and untapped utilization of valuable reference data. Data organizations within life science companies are struggling to keep pace with all this change.
Recognizing the problems that data volume and variety present, now is the time for the industry to embrace DataOps. It offers automated process-oriented best practices to improve the quality and agility of highly curated data that can be consistently delivered to data consumers for operational and analytic impact.
Understanding the principles
The principles of DataOps can be framed around the fundamental concepts of process, technology, and people. At a high level, the framework components can be summarized below:
The DataOps approach tackles modern data initiatives with all three components in mind to guarantee successful, transformational outcomes within organizations.
How DataOps can accelerate initiatives
Compared to traditional approaches to clinical data harmonization, Tamr’s DataOps approach enables life science companies to deliver curated data products at a pace and scale previously impossible — opening up a large variety of use cases previously not feasible.
At GSK, for example, Tamr helped harmonize 30 domains of over 1500 legacy clinical study data into GSK’s SDTM standards within one year. This achievement of processing millions of source attributes across more than 40,000 clinical trial data sources in a data pipeline that can process 10 billion records a day allowed GSK to gain tremendous leverage in the variety of operational and analytic applications across their R&D teams. DataOps played a crucial role in achieving this.
As David Cowen, Director, Data and Computational Sciences at GlaxoSmithKline, pointed out during his presentation at DataMasters:
“I think the DataOps changes that we’ve seen in the industry have been led by companies like Google and Amazon that have a real data focus. They understand data is a key factor in their product.
In the life science industry, that’s not so much the case. GSK, to some extent, is pharmaceutical with drugs, and so data is on the periphery and while for years we’ve known that we got a goldmine of data that’s available to us, really having the will to go after that and make it manageable that’s something that has not been there. It’s only been recently here at GSK that the value of data assets and information assets has been realized, and we’re in the process of trying to capitalize on that.”
How Tamr creates a DataOps framework for Life Sciences
Tamr plays an essential role in the DataOps technology landscape; it has been built with all three components of the DataOps principles in mind. As a technology platform, Tamr promotes the DataOps principles in its approach to architecture and infrastructure:
Modern data ecosystems have become more complex than ever, so DataOps principles provide the best practices to ensure a successful approach to all data transformation initiatives.
A final thought: How Life Science companies are responding to DataOps
From what we’ve seen from our customers, the DataOps approach has proven critical in transforming large varieties of data initiatives across the life science industry. One of the most important yet elusive measures of success that we have seen is the fundamental shift in data culture throughout an organization. Tamr’s involvement in many of our customer’s digital transformation journeys has enabled the organization’s data consumers to have (and even expect) highly curated data to succeed in their roles for faster drug discovery and more efficient clinical development.
We’d love to help with your DataOps initiatives: whether you’re far along in the journey or just beginning. We offer tailored workshops where we can evaluate your data and then work together to identify areas to transform your latent data into an asset.
The original article by Bernie Kuan, formerly the global solutions and sales engineering for Tamr’s Life Science and Healthcare, is here.
The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/metamorworks