From Data to Direction. Scientists and engineers at the R&D, pilot, and production scales need a new way to harness the power of the data gathered… a new way to drive innovation through an enhanced data strategy. Crucial elements for enabling pharmaceutical process innovation include:
Companies often capture all the data they need to improve operations within their data historians and other databases. However, creating insight from this information can be difficult, expensive, and time-consuming using traditional approaches or limited data analytics tools. For example, it is important to ensure that your data management applications provide:
With a facile data strategy, the world looks different. Imagine having the ability to easily search and interact with past and present time-series data in a “google-like” fashion and collaborate in real time. Imagine being able to make business critical decisions with more confidence simply because you have the data in hand.
BIOPHARM INTERNATIONAL NOV 2016, LEVERAGING DATA ANALYTICS INNOVATIONS TO IMPROVE PROCESS OUTCOMES—By Lisa Graham, PhD: http://www.biopharminternational.com/leveraging-data-analytics-innovations-improve-process-outcomes
At the highest level, Figure 1 shows what is needed!
Alkemy Innovation works with a series of available technologies and tools that support optimal data management and retrieval. For example, Alkemy Innovation leverages an application from Seeq that is dedicated to time-series data investigation and works with any process historian (e.g., OSIsoft PI System, Emerson Advanced Continuous Historian) and other database soures (Figure 2). With features like being able to easily search and interact with past and present time-series data in a “google-like” fashion and collaborate in real time, the Seeq tool is an example of a tool that supports the desire for intuitive, visual, and innovative data analyses.
Building upon data access and visualization tools, Alkemy Innovation leverages other tools like JMP, Spotfire, and Matlab to support the development of new models and correlations, along with their implementation into the day-to-day practices.
We value participating in conference presentations, technical poster presentations, and webinars where we can learn about the latest technologies and also share specific case study examples that illustrate how to drive innovation through enhanced data visualization. Using enhanced data visualization, factors that affect product quality can be rapidly identified further enabling definition of key performance indicators in early development. Leveraging historical data allows small scale model verification and insights into challenges of scale up.