There are plenty of obstacles in big organizations that prevent it from unlocking the full potential of our data. Proliferation of data silos, disparate and inconsistent ontologies, organizational barriers to access, legacy tools that do not work well together, business intelligence tools that are inadequate for analyzing scientific data – all contributing to loss of productivity and user frustration. Even today, the most common technology denominator for disseminating scientific data is an Excel or PowerPoint file shared via email.
While there is a plethora of interactive data analysis and visualization tools available today – like Spotfire, Tableau, and various home-grown tools – none of them work effectively on the web because they rely on a conventional, “chatty” client-server architecture, adversely impacting interactivity and performance. Furthermore, the tools lack domain-specific analytics for chemical, biological and clinical research, which limit their usefulness and applicability.
What is needed is a system for mining, visualizing, and analyzing this data in fast and intuitive ways, test hypotheses, gain insights, and act on these insights to design new molecules and new experiments that advance the science and development of novel therapeutics.
Datagrok, a revolutionary web platform that enables interactive analysis and visualization of massive datasets natively in the browser, was designed to address all of that from the ground up. It was selected as a framework upon which a number of fit-for-purpose applications are being built. The look, feel, and functionality of these applications vary greatly, but they all leverage the same underlying Datagrok engine to enable a blazingly fast user experience and minimize code duplication and reinvention.
During the technical evaluation, the following features were identified as pivotal for the success of the platform: