As innovative approaches to drug discovery are explored in this modern age, innovative strategies for the technology that supports those approaches, especially in the area of information visualization, are becoming essential.
This is the topic of a chapter written by ClicData’s own CEO, Telmo Silva of a newly published book, Bioinformatics and Computational Biology in Drug Discovery and Development by Will. T. Loging.
We know that well-designed data visualization can improve the way we do business and help bring products all the way through to their final stages of development. But it has become a key element of success in many industries including innovative drug discovery.
Data visualization is one of the five components of Computer Aided Information Visualization (CAIV) each of which contributes to the organization and implementation of data visualization in an organization or department.
- Interaction components
- Hardware
- Application design
- Data visualization, and
- User adaptation
Data visualization is in itself the core of CAIV; poor data representation design can undermine the good intentions of analysis throughout the process. According to Edward Tufte, a pioneer in the field of data visualization, “Graphical excellence is what provides the most ideas in the shortest time, with the least ink, and in the smallest space. It consists of complex ideas communicated with clarity, precision and efficiency.”
All five pieces of the CAIV puzzle are essential to effectively provide correct, reliable, actionable data. But data visualization is truly the next generation of BI and data warehouse building. The scientific community has already been using information visualization, awkwardly assisted by disconnected software applications and disparate cleansed data sources. Now, CAIV can transform practices to meet the challenges of today’s business environment.
Yet data visualization does not exist in a vacuum. CAIV encompasses four additional components to assist the viewer in navigating, interacting with and responding to the data presented. Due to the greater complexity, volume and expanded requirements of today’s analytical environment, these components are as critical to success as data visualization. Without all of the pieces of the puzzle, even the most exquisitely designed graphical representation will be lost in obscurity, or worse, contain incorrect, incomplete, or misleading data that can do more harm than good.
Bioinformatics and Computational Biology in Drug Discovery and Development is available on Amazon! To learn more, Click Here.
