Top 5 Data Challenges for Life Sciences R&D Organizations

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As anyone in Life Sciences already knows, every second of every day in R&D we are surrounded by data. We have pre-clinical data, clinical data, submission data, FDA correspondence, and operational data, just to name a few. Seeing it listed like that, pre-clinical and clinical data especially, seems like just part of a daily routine. But the truth is, developing drugs, biologics, medical devices, involves massive amounts of data filled with testing outcomes, dosages, chemistry, manufacturing information, you name it.

So often, in Life Sciences, we are so used to being inundated with data that we don’t even realize when we are struggling to manage and use it. We’ve found ways to just deal with the reality for so long that we become numb to the pain, and we find ways every day to perform our jobs. Collecting, organizing, making use of and storing data is a headache. What I’ve found, is often times we come up against data challenges and we either brush them aside due to higher priorities, or worse, we don’t even realize it’s a problem (let alone solvable) until someone else points it out.

Here are my Top 5 data challenges Life Science R&D departments come up against.

Availability of Data You have labs, CROs, patients, coordinators, doctors, scientists and a myriad of other people generating data surrounding your product, and every little bit of it counts. When it comes time to file with an agency, make crucial development decisions, or even just understand what the research is telling you, your data is everywhere. Chances are you’ve got multiple systems across multiple functions, or maybe even 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.

Lack of Data Ownership Who in your company is responsible for owning the data, in the big sense? If I had to guess, you don’t have business functions claiming ownership, instead each department points to others as owners regardless of the data origin. Each department or entity, especially if outsourcing any of your work, probably moves or creates the data needed to complete the task. 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”.

Lack of Data Standards How do you capture, move and report on something as simple as a patient’s sex? Do you write Male or male, or M?? We as an industry have standards on how data is collected and how data is structured. But even within individual companies, data standards tend to be loose recommendations at best, and can vary by department, and even from one protocol/crf to the next

Data Analysis With multiple systems, across multiple departments, no clear owner, and a lack of standards, it only follows suit that it’s difficult to run any kind of metrics and analytics that you need to make important decisions for your company. We always find a way to get what we need, but at what cost? Extra data manipulation and organization, coupled with subpar technological resources, forces efficiency right out the window, costing us not only time and money, but valuable insight.

Data Noise Piggybacking off of data analysis, it’s hard enough to find insight in jumbled up data collections, but think of how much data we collect that isn’t that important. I’m talking about the data that we have just in case we need it, but it’s really not a part of our critical path. To further complicate the issue, of course, is “critical path” varies by function and department. So what is “data noise” to on person, may be crucial to another. It’s easy to miss important data points when you have all this extra information floating around. Often times, this “extra” data can be combined and better structured.

Do you agree with my Top 5 list? Are there other data pain points you want to add? Tell me what you think in the comments section, or continue the conversation on LinkedIn.

 

 

 


 

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Tags: data quality, data governance, Life Science R&D

   

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