Medidata Enhances Machine Data Management with Sumo Logic for Improved Clinical Trials
Customer Company Size
Large Corporate
Region
- America
- Europe
Country
- European Union
- United States
Product
- Sumo Logic Continuous Intelligence Platform
- AWS Elastic Compute Cloud (EC2)
- AWS Simple Storage Service (S3)
- AWS Relational Database Service (RDS)
- AWS Elastic Container Service (ECS)
Tech Stack
- AWS
- Sumo Logic
- Python
- Java/Scala
- Ruby on Rails
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Productivity Improvements
- Digital Expertise
Technology Category
- Analytics & Modeling - Predictive Analytics
- Platform as a Service (PaaS) - Data Management Platforms
- Infrastructure as a Service (IaaS) - Hybrid Cloud
Applicable Industries
- Life Sciences
- Pharmaceuticals
Applicable Functions
- Quality Assurance
- Business Operation
Services
- System Integration
- Cloud Planning, Design & Implementation Services
- Data Science Services
About The Customer
Medidata is a leading life sciences technology provider. The company’s mission is to aid pharmaceutical firms and medical equipment manufacturers in validating that their experimental drugs or devices are safe, effective, and help treat a given disease. The upshot is that Medidata plays a significant role in helping patients lead healthier lives by expediting the process of bringing a new treatment to market and then monitoring it in the population at large after approval. Customers use its cloud-based products to carry out a wide variety of tasks, such as designing studies, managing them, and analyzing results to glean significant conclusions. All of these activities are carried out within a consistent user interface on information that’s maintained by Medidata. The company offers its SaaS products to customers in the U.S. and Europe, with failover capabilities across geographies. Multiple constituents either directly utilize the platform or are represented within it, including patients of all ages, physicians, and biostatisticians, to name just a few. Medidata also gathers data directly from patients via smartphones, wearable devices, and sensors.
The Challenge
Pharmaceutical companies and medical device manufacturers rely on Medidata’s solution to power the studies that determine whether their products are ready for delivery to the market. Medidata was generating a tremendous volume of logs from its systems, but this raw, unprocessed data was neither centralized nor efficiently scrutinized. At any given moment, Medidata’s customers are managing more than 5,000 active clinical trials on its platform. This means that, on average, every hour the company must respond to greater than 1 million transactions composed of 3,000 unique logins, up to 300,000 patient data page requests, and 150,000 Web service API calls. The company’s management set a goal to democratize access to its machine data as a critical precursor towards increasing the quality and availability of its Software as a Service (SaaS) offerings.
The Solution
Medidata incorporated Sumo Logic’s cloud native, machine data analytics platform into its technology portfolio. As part of this initiative, the company aggregated all machine data into a central location. This made it possible to derive meaningful insights from these logs via an extensive assortment of dashboards, alerts, and custom-built applications. Medidata implemented its solutions on a hybrid cloud environment that’s comprised of U.S.-based private data centers and those from Amazon Web Services (AWS). The company elected to employ distributed data centers to reduce latency, as well as comply with regulations such as the European Union (EU) requirement that patient data be stored on servers within the EU. Medidata was an early AWS adopter and now fields an extensive - and evolving - collection of its services for all aspects of its technical landscape: compute - Elastic Compute Cloud (EC2); storage - Simple Storage Service (S3), Elastic Block Store (EBS); data - Relational Database Service (RDS), Dynamo, Redshift, Kinesis; containerization - Elastic Container Service (ECS), Lambda; machine learning/analytics - Hive, Pig, R, SageMaker, SageMaker Ground Truth, Python; and management - CloudFront, Elastic Load Balancing (ELB), Application Load Balancer (ALB). The company’s application architecture is equally disparate and includes logic written in .NET, Ruby on Rails, and Java/Scala.
Operational Impact
Quantitative Benefit
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