

As a software-as-a-service (SaaS) and management consulting firm, PharmLogic provides robust solutions to the life sciences industry by combining best-in-class cloud-based technologies with a global services support model. As Vidal mentions, “We automate business processes and leverage data and make it actionable to provide our clients with a strategic advantage through better use of commercial data and analytics.”
To do this, the company designs and develops its robust PROLOGIC platform by combining its in-depth know-how in big data, SaaS, and the life sciences space. The platform leverages data to provide valuable insights for businesses, helping them augment their critical commercial analytics and operation functions. PROLOGIC contains several powerful modules, for work flow management, master data management, a specialty data hub, alignment hub, and a data lake module, all of which can be easily integrated into a customized solution based on the clients’ varying needs. This hosted cloud solution is predesigned to provide necessary business process automation and computing power to efficiently process the life science industry’s largest and most complex data sets.
The PROLOGIC Big Data SaaS solution allows clients to eliminate several challenges pertaining to managing their data. With its powerful master data management capability, the solution helps businesses identify their customers and provide valuable insights to augment their sales activities.
PROLOGIC’s flexibility and nimbleness in responding to clients’ varying requirements in complex and dynamic environments, gives it an extra edge. The solution easily integrates with clients’ existing platform, eliminating the need for a complete reshuffle in their infrastructure. Additionally, the PROLOGIC platform embraces advanced AI capabilities, allowing businesses to automate their business operations.
To further elaborate on PROLOGIC’s value proposition, Vidal recalls its recent collaboration with a large oncology franchise that was being challenged by difficulties associated with customer mastering and business rule development. They client wanted a holistic view of their customer data and market parameters, which was often impeded by complex and overlapped datasets. PharmLogic worked hand-in-hand with the client and identified their requirements to come up with a solution that was able to streamline their data. Post-collaboration, the client was able to have a powerful platform that could help them manage all their data, which created a huge positive impact in their business operations.
With several such success stories, PharmLogic is rolling out plans to expand their footprint beyond the life sciences industry. To do this, the innovation lab at PharmLogic is constantly striving to enhance their solutions’ capabilities to cater to several other industry verticals. The company is now planning to spearhead its innovation in the CRM space to enhance customer relationship management within the life science industry. “We look forward to serve as a one-stop-shop solution for our clients by delivering an integrated platform that provides data management as well as CRM capabilities,” concludes Vidal.
Company
PharmLogic
Headquarters
San Juan, PR
Management
Jose Vidal, Partner
Description
Founded in 2010, PharmLogic is a big data SaaS and Services firm dedicated to providing solutions to the life science industry by combining best in class cloud-based technology with a global services support model. Their mission is to provide clients with a strategic advantage through the better use of commercial data and analytics. PharmLogic’s global delivery model offers efficient 24/7 operations designed to support enterprise solutions and efficient scaling. They offer PROLOGIC, a Big Data SaaS solution specifically developed for commercial operations. The platform is designed into modules that can be easily combined into a customized solution. As a hosted cloud based solution, PROLOGIC provides enough computing power to efficiently process the life science industry’s largest and most complex data sets