Pharmaceutical case studies illustrating data analytics strategies

New webinar hosted by Xtalks focuses on extracting the most value from data assets.

Data Rich, Information Poor. Choosing the appropriate data management and visualization components are important. How many times have you wanted to investigate a key issue, or proactively design a new process, only to be stopped in your tracks because you don't have the data you need? Do you often decide that the pain and effort of gathering the data, that you know exists somewhere, isn't worth the time it takes to pull it together?

With the invent of high-speed, high-throughput data acquisition capabilities of recent years, there is a need for high-speed data analytics and visualization. Analytics tools are the most efficient way to get the most value out of the data that is being produced every day. With the proper tools in hand, your data can become one of your greatest assets.

Yet, most processing is still done in user-specific spreadsheets with time-consuming, manual inputs and calculations. Further, when challenges arise, it becomes necessary to couple reams of disparate data sources, often in multiple iterations, to better understand the complex reactor, bioreactor, or purification dynamics.

It is not uncommon for a technical team to need to leverage significant historical data within very tight product development timelines. Therapies need to be quickly transitioned from a proof-of-concept phase to a scale and quality suitable for commercial production, where the company must also demonstrate full knowledge of both the process and the final product quality metrics. With that knowledge, the company can achieve a successful launch, ensure patient safety, and meet regulatory requirements. Data is a tremendous asset that should be captured and made readily available for analysis, which is imperative for

In this webinar, the speakers will focus on specific pharmaceutical case study examples, with a focus on the Seeq software application. Seeq simplifies the extraction of insights and value from data historians and other data sources. Specifically, it allows the scientist or engineer to quickly access and search the data, add context, cleanse, model, find patterns, establish boundaries, monitor assets, and allow collaboration in real time. Our expert speakers will discuss how you can leverage data analytics strategies to achieve

Scientists in pharmaceutical production must be able to thrive in a challenging business environment defined by operational challenges, regulations, market shifts, and competition.