Organizations report difficulties in navigating regulatory requirements due to complicated data management and reporting issues.
Organizations cite diverse data platforms and systems as a major hurdle for effective data utilization.
Increase in productivity and operational efficiency was achieved by organizations who successfully implemented robust data analytics systems.
We employ a data-driven approach to data engineering for R&D-IT in the life sciences industry. Our domain experts and seasoned engineers provide comprehensive, end-to-end services for pharma decision makers to go forward with data-driven decision-making.
We bridge the gap between science and technology, supporting your complete digital transformation from strategy definition to implementation and ongoing support.
We leverage AWS to deliver exceptional data engineering services tailored for pharmaceutical clients. By utilizing AWS's robust and scalable cloud infrastructure, we can efficiently manage and process large volumes of diverse data, ensuring seamless integration and storage. Our expertise in AWS services such as Amazon S3, Redshift, Glue, and Lambda allows us to build sophisticated data pipelines, enabling real-time and batch processing to meet the dynamic needs of the pharma industry.
Utilizing Azure's comprehensive cloud platform, we effectively manage and process vast amounts of data, ensuring seamless integration and secure storage. Our proficiency in Azure services such as Azure Blob Storage, Synapse Analytics, Data Factory, and Azure Functions allows us to create sophisticated data pipelines that support both real-time and batch processing, catering to the unique demands of the pharma industry.
We leverage our partnership with Snowflake to provide outstanding data engineering services tailored for pharmaceutical clients. By utilizing Snowflake's cloud data platform, we manage and process vast amounts of data efficiently, ensuring seamless integration and high-performance analytics. Our expertise in Snowflake's features, such as its data warehousing capabilities, secure data sharing, and scalability, enables us to build robust data pipelines that support both real-time and batch processing, addressing the specific needs of the pharma industry.
i2e Consulting excels in merging deep domain knowledge in life sciences with cutting-edge data engineering and technology expertise, creating holistic solutions that bridge scientific research, data management, and technological innovation.
Our team of seasoned data engineers and domain experts with extensive experience in life sciences and pharma. Our team possesses deep industry knowledge and technical expertise, allowing us to deliver tailored solutions that meet the specific needs of pharmaceutical companies.
We have a proven track record of successfully implementing data engineering projects, ensuring high data quality, regulatory compliance, and robust security measures. Our engineers are adept at utilizing the latest technologies and best practices to optimize data workflows, integrate diverse data sources, and support advanced analytics.
We offer comprehensive, end-to-end data engineering services, including life sciences data strategy and consulting, data collection and summarization, data transformation and ETL, data storage solutions, data pipeline development, data architecture modernization, and data quality and governance.
A data pipeline is a method in which a set of processing elements are connected in series which ingest raw data from various data sources, transform and store them into a data lake or data warehouse for analysis.
It is the process of managing the ingestion, and transformation of high volume of data from various disparate data sources within an organization.
Data engineering can help pharma and life sciences companies to extract insights from the data within various data sources. These insights facilitate data-backed decisions x in drug development, clinical trials, drug safety and patient outcomes.
A data warehouse is a centralized repository that stores structured data from multiple sources, optimized for querying and analysis rather than transaction processing.
