Streamlining Organizational Decision-Making in a Data-Driven Life Science Industry


In today’s data-driven life science industry, streamlining decision-making processes is essential for R&D success. Image Credit: Flickr use Kristjonas Dirse

When it comes to doing research in the life science industry today, the one thing you don’t have to worry about is generating enough data. In the past, one of the most significant challenges of pharmaceutical R&D was producing enough reliable data to be able to draw valid conclusions. Today, the main problem facing researchers is the exact opposite. Technology has made it possible to generate so much data at every stage of the product lifecycle that you can end up feeling like you are drowning in data.

It can be a major challenge to wade through massive datasets and find effective ways to understand and store the information — but it is essential for streamlining decision-making at every level of pharma R&D organizations. Using today’s software solutions and technologies, lab researchers can effectively manage data so that it can support fast, informed decision-making throughout the product life cycle.

Key Strategies for Effective Data Management and Utilization

The challenges associated with big data can significantly impact all aspects of pharmaceutical research and development. If large datasets are not managed properly at every stage of the product life cycle — from product conception to manufacturing and marketing — it can lead to backlogs and bottlenecks that can interfere with both research and business processes. In order to make the most of the abundant data that it is possible to generate today, pharma R&D organizations need to be able to maximize performance in the following areas.

  • Effectively capturing and storing large datasets. Today’s research technologies make it possible to run hundreds or even thousands of experiments in very little time, but this capability provides little organizational value if datasets are not captured and stored properly. It is essential to keep valuable data secure, but when data gets locked away in silos that are inaccessible even to authorized parties, it can interfere with decision-making and increase the time it takes to get a product from benchtop to bedside.
  • Sharing data through collaboration and integration technologies. Communication and collaboration are fundamental to success in pharma R&D, whether it means transferring data around the lab or across the globe. To facilitate more effective communications that increase the speed of decision-making at the organizational level, many pharma R&D firms are embracing the Internet of Things (IoT), which encompasses technologies that can have a positive effects on both data integration and collaboration.  
  • Gaining insights from aggregated data through advanced analytics. One of the most significant challenges associated with big data is effective processing. The mere possession of data has no organizational value — you have to be able to analyze it and view it in relevant contexts in order to gain relevant insights that can support product development and improvement. For this, your organization has to be familiar with the advanced analytics capabilities of modern software.
  • Applying existing data to support further innovation and scientific advancement. There are a variety of ways in which large datasets can be applied to provide organizational value and promote better decision-making. Not only can data be processed and analyzed to directly reveal valuable information, but it can also be applied in the context of machine learning to improve data generation and analysis processes in the future. Again, modern software solutions make it easier to streamline data application efforts.

The landscape of the life science industry is changing at a faster pace than ever before. It is increasingly easy to get overwhelmed by the amount of data you have and fail to manage it in a way that supports fast, well-informed decision-making. However, by making the most of today’s software solution for data storage, sharing, and utilization, organizations can maximize the value of the data generated at every stage of the product life cycle.

If you’re interested in learning more, consider attending the Global Pharma R&D Informatics Congress on November 30, where BIOVIA is going to be a platinum sponsor. We will be giving a presentation on Connecting Data and People to Drive Scientific Decision Making, and we will also be hosting a round table on Leveraging IoT in the Lab. We hope to see you there! Contact us today to learn more about the conference and all of our software offerings!