Case Studies Medidata Enhances Machine Data Management with Sumo Logic for Improved Clinical Trials

Edit This Case Study Record

	Medidata Enhances Machine Data Management with Sumo Logic for Improved Clinical Trials

Medidata Enhances Machine Data Management with Sumo Logic for Improved Clinical Trials

Technology Category	Analytics & Modeling - Predictive Analytics Infrastructure as a Service (IaaS) - Hybrid Cloud Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries	Life Sciences Pharmaceuticals
Applicable Functions	Business Operation Quality Assurance
Services	Cloud Planning, Design & Implementation Services Data Science Services System Integration
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Predictive Analytics

Infrastructure as a Service (IaaS) - Hybrid Cloud

Platform as a Service (PaaS) - Data Management Platforms

Applicable Industries

Life Sciences

Pharmaceuticals

Applicable Functions

Business Operation

Quality Assurance

Services

Cloud Planning, Design & Implementation Services

Data Science Services

System Integration

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.

About 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.

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.

Impact #1	Medidata’s machine data endeavor has become widely popular across multiple organizations within the company. By positively impacting hundreds of users, Sumo Logic is now an integral part of their daily activities, helping them identify and correct problems more quickly and accurately.
Impact #2	The ultimate beneficiaries of these optimizations are Medidata’s customers, who are now able to bring their lifesaving solutions to patients faster and at a lower cost.
Impact #3	Sumo Logic presents its insights to the Medidata user community through several different conduits such as prepackaged tools, dashboards, alerts, and reports. Dashboards have proven to be particularly popular: most Medidata engineers have at least one instance visible at any given time.

Impact #1

Medidata’s machine data endeavor has become widely popular across multiple organizations within the company. By positively impacting hundreds of users, Sumo Logic is now an integral part of their daily activities, helping them identify and correct problems more quickly and accurately.

Impact #2

The ultimate beneficiaries of these optimizations are Medidata’s customers, who are now able to bring their lifesaving solutions to patients faster and at a lower cost.

Impact #3

Sumo Logic presents its insights to the Medidata user community through several different conduits such as prepackaged tools, dashboards, alerts, and reports. Dashboards have proven to be particularly popular: most Medidata engineers have at least one instance visible at any given time.

Benefit #1	Medidata’s platform handles more than 5,000 active clinical trials at any given moment.
Benefit #2	The company must respond to greater than 1 million transactions every hour.
Benefit #3	Sumo Logic aggregates up to 250 GB of Medidata’s machine data per day.

Benefit #1

Medidata’s platform handles more than 5,000 active clinical trials at any given moment.

Benefit #2

The company must respond to greater than 1 million transactions every hour.

Benefit #3

Sumo Logic aggregates up to 250 GB of Medidata’s machine data per day.

Download PDF Version

Overview

Medidata Enhances Machine Data Management with Sumo Logic for Improved Clinical Trials

Operational Impact

Quantitative Benefit