Life Science & BioTech

Life Sciences professionals “live in data”– clinical data, submission data, FDA correspondence, and operational data, just to name a few. Developing drugs, biologics, and medical devices involves massive amounts of data related to testing outcomes, dosages, chemistry, manufacturing information, and complex regulatory compliance. The data challenges are many:

Availability. Your organization has multiple systems across multiple functions, or this may exist within your own team. Not all your data is located in one place, and it certainly isn’t easily accessible in the places the data does live.

Ownership. Who owns the data? Do people point to others as owners regardless of the data origin? With no one “in charge” to verify or certify that your data is current, accurate and trustworthy, you’re left using the latest data you can find, or worse, “what we have always used”.

Efficiency. The data may be structured and well-managed, but is it tuned to be used within your specific business processes? Access to data is useful, but not when its ALL of the data, and what you need is the RIGHT data to complete the regulatory documentation, or the clinical trial report.

Noise. It is difficult to find insight in jumbled up data collections, especially when “critical path” varies by function and department. “Data noise” to on person, may be crucial to another. It’s too easy to have important data go unused when it is not structured and findable when it is needed.

Finding ways to just deal with this complexity is not something that can be brushed aside due to higher priorities. Through our knowledge engineering and data management capabilities, EIS delivers the means to overcome these challenges and realize the potential that a data-centric life sciences enterprise can experience. 

 

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