One of the biggest challenges for life science and healthcare organizations is dealing with a vast array of heterogeneous sources, objects, clinical data, and real-world evidence.
Quantori has deep domain expertise that applies to widely used public and private data sources. Our data scientists are experienced in working with diverse types of data and in combining data from multiple sources to achieve optimal results.
Data science is only one part of the process — wrangling the right data points and engineering them for optimal results is what makes Quantori uniquely positioned to take on this kind of projects.
• Design a data strategy that reflects business goals and scientific needs
• Develop a data architecture blueprint and executable roadmap
• Develop approaches to building-up cloud-based solutions for managing and storing large volumes of data
• Migration from on-premises environment to Cloud, including RDBS replacement
• Complex data migration from legacy systems to a client’s new stack
• Data extraction from various sources
• Source data cleansing
• Real-time/streaming and batch data processing
• Data format conversions, transformation, and normalization
• Data standardization services, reducing data complexity
• Working with structured, semi-structured, & unstructured data
• Build complex architectures for future DWH
• Adjust existing solutions to increase scalability and performance
• Configure data pipelines to feed into Data Lakes
• Connect BI solutions
• Develop database architectures
• Database performance tuning
• OLTP systems creation
• Best practices for building business logic around/inside a database layer
• NoSQL solutions
• Provide a proven methodology for establishing data governance within the organization
• Provide solutions for data high availability and consistency
• Provide robust data security solutions/data anonymization according to requirements of regulatory authorities
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• Complex ML/DL pipelines that can accommodate multimodal data, including text and images engineering
• AI solutions design and implementation
• Data insights extraction using AI and ML
• Cloud-based AI/ML platform integration
• Computer vision, natural language processing, and optimization
Intelligence
• Business Intelligence implementation and providing intelligent insights
• Business Intelligence performance management prototypes, architecture, and choosing technical stack design
• Interactive data visualization and custom visualization development
• Dashboards, paginated reports, presentations, and mobile solutions creation
• Access management and implementation of GxP requirements
• Interactive reporting solutions development
• Dashboards, platforms, and UX/UI services for data representation and manipulation development
• Improvement of existing visualization solutions for clients
• Preparation of client data for visualization and analytics
• Providing client with valuable insights using data visualization tools
• Analytical power by providing interactive visualizations enhancement
• Streaming and real-time data visualization
• Data quality assessment
• Deriving relevant business and scientific insights from the data
• Creation of data reports and data repositories
• Entire data analysis process optimization
• All possible types of data analysis: searching of the common patterns for data groups comparisons, exploring outlying data points, assessing missing variables, etc.
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RWD and RWE Part 2: Challenges and Solutions
We have described RWD and RWD in our previous blog. There is strong promise and increasing use of Real World Evidence to accelerate data-driven decisions, improving not only the clinical trial experience, but healthcare in general. But, can we believe Real World Evidence?
Real World Data and Real World Evidence Part 1: Overview
Real World Data and Real World Evidence (RWD/RWE) is a hot topic in Pharma in the context of clinical trials.