By Carlos Amat, Executive Manager at Farma Global and Samuel Martinez, Managing Director/Partner at SDG Group USA.
The pharmaceutical industry has not only been characterized by its structural resilience to economic fluctuations, but has also always been at the forefront of innovation. In fact, among other factors, improving global healthcare requires more efficient processes for drug development and clinical trial management; this is where new technologies have much to contribute.
In this regard, one of the most important challenges currently facing the pharmaceutical sector is the analysis of large volumes of data. Increasingly, companies have more and more analytical needs and, therefore, must have platforms capable of managing them. In many cases, their legacy infrastructures have become obsolete and are unable to meet the current capacity and computing needs. In order to remain competitive, they must migrate to much more flexible, efficient and modern cloud platforms.
At SDG, we recently had the opportunity to work with a large multinational pharmaceutical corporation whose data platform was becoming unable to match the pace of innovation and development set by the company. For that reason, our team helped them to migrate their Pharma and Oncology business units to Big Data technologies in a cloud architecture with Snowflake and Matillion, as an ETL tool to automate the data integration processes.
The first step to launch the project, after having identified the scalability and efficiency challenges we were facing, was to develop a detailed guide of all the actions to be carried out:
It should be noted that, in this type of large project, it is essential to develop an initial plan or roadmap and follow all the steps carefully in order to succeed and achieve the objectives agreed with the client. Thus, the initial phase consisted of assessing the existing infrastructure, data and applications to determine the feasibility of the migration, aligning it with the business strategy, identifying priorities, timelines and requirements of the cloud platform needed.
The second phase was the "analysis and strategy" phase, which took a close look at your technology environment, workloads, risks, security, compliance or performance needs, to define the final migration strategy.
This was followed by the "detailed planning" phase, where all the technical requirements necessary to carry out the project were specified: from cataloging and rationalizing the inventory, to quantifying costs and risks, to drawing up the plan for coexistence and decommissioning of the previous system.
Finally, in the fourth phase, the final "migration and production" was addressed, i.e. the actual movement of data, applications and workflows to the new cloud environment. This is when all the work involved in configuring the target architecture, testing and quality control or data replication, among others, took on special relevance.
Every migration project involves significant changes in the day-to-day life of users, both in terms of technology and processes. For this reason, SDG always helps its clients to define good practices to ensure a smooth transition. Thus, in the project with the multinational pharmaceutical company, we identified all affected parties, helped communicate the reasons for the change and its benefits, assessed its impact, developed a detailed transition plan with deadlines and responsibilities, organized training sessions and workshops to ensure a more efficient use of Snowflake, conducted extensive testing of the new environment to validate its functionality and performance, implemented a pilot phase in a controlled environment, solicited feedback from users to incorporate necessary improvements, implemented a change control process and an ongoing monitoring and support mechanism, and developed detailed user guides.
The migration project carried out with the pharmaceutical organization has yielded significant benefits. It has achieved a 30-fold increase in data warehouse update performance, significantly increased speed and efficiency in analyzing patient requests, reduced the time to analyze market shares for different brands (from seven days to three hours), enabled Snowflake data sharing, and improved the organization's ability to interpret complex data and make better decisions.
In addition, the project has been done with obvious future prospects, as SDG continues to work on different initiatives related to self-service solutions, predictive analytics or data governance. The goal is to help the pharmaceutical company strengthen its development objectives in the field of data, analytics and AI, consolidating its leadership position.
In short, this project is a clear example of SDG's work methodology, which, thanks to its extensive experience in the market, already has different predefined "frameworks" (with protocols and technical assets) that allow it to successfully and efficiently tackle all types of data ingestion, data governance, etc. projects.
Credits to: Luveen Arolkar and Matthew Giroux