Actian > Case Studies > On the hunt for the next big medical discovery: Oxford University Clinical Trial Service Unit reduces the data supply process from days to just minutes thanks to Actian Vectorwise

On the hunt for the next big medical discovery: Oxford University Clinical Trial Service Unit reduces the data supply process from days to just minutes thanks to Actian Vectorwise

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Company Size
1,000+
Region
  • Europe
Country
  • United Kingdom
Product
  • Actian Vectorwise
Tech Stack
  • Database Management
  • Data Analytics
Implementation Scale
  • Enterprise-wide Deployment
Impact Metrics
  • Digital Expertise
  • Productivity Improvements
Technology Category
  • Analytics & Modeling - Real Time Analytics
  • Application Infrastructure & Middleware - Database Management & Storage
Applicable Industries
  • Education
  • Healthcare & Hospitals
  • Life Sciences
Applicable Functions
  • Product Research & Development
  • Quality Assurance
Services
  • Data Science Services
About The Customer
The Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU) of the University of Oxford is an organization that conducts extensive research in the fields of bioresearch and healthcare. They study the causes and treatments of chronic illnesses such as cancer, heart disease, and strokes, which account for the majority of adult deaths in the developed world. Through their involvement with the UK Biobank project, CTSU curates many thousands of pieces of information from over half a million volunteers and makes it available anonymously to the worldwide scientific community. Scientists all over the world can then use that data to find out more about the causes of illnesses, the links between the causes and diseases, and hence improve prevention and treatment. The organization is committed to providing fast, cost-effective technology to identify, manipulate, and extract exactly the data which researchers need and make it available to them within extremely fast timescales.
The Challenge
The Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU) of the University of Oxford is involved in extensive bioresearch and healthcare studies. They extract and analyze data related to the causes, prevention, and treatment of chronic illnesses such as cancer, heart disease, and strokes. However, they faced a significant challenge when they realized that their existing database platform could not cope with the large data volumes involved in their research. The legacy platform struggled with complex queries, especially when they involved several thousand fields. The analytics could take days, which was not acceptable for the fast-paced research environment. They needed a solution that could handle high-speed analytics and deliver results within extremely fast timescales.
The Solution
CTSU decided to implement Actian's Vectorwise, a high-speed analytic database, to improve their analytics and service to their users. After installing Vectorwise, CTSU could change the way they mined and analyzed the data. The database allowed them to do aggregations, pivot the data, and sum the columns within a few minutes instead of a few days. The database was easy to use and did not require a steep learning curve or lots of hardware to benefit from its performance. The current Vectorwise deployment contains the UK Biobank data. User requests come in via the web from approved scientists and are then turned into database queries through custom scripts. As such, CTSU does not need to use any specific ETL or BI tools to manipulate or visualize the data.
Operational Impact
  • Fast data loading; data loads take just minutes
  • Ad-hoc analytic queries against large data volumes accelerated dramatically from days to just seconds
  • Future-proofed analytic and reporting infrastructure with Vectorwise
  • The turnaround for analytical queries is simply amazing. Instead of taking days to satisfy users’ very complex queries, Vectorwise returns results in just minutes
  • The database is operational and does not take much maintenance time to support the system
Quantitative Benefit
  • Data loading time reduced to minutes
  • Analytic queries against large data volumes accelerated from days to seconds
  • Complex queries that previously took days now run in just seconds or minutes

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