Silos of data
Life sciences consultants at NNIT work across the value chain, from clinical, to regulatory affairs, drug safety, production, and quality management. A key observation that NNIT’s life sciences experts have made from working with the different business units in life sciences companies is that many of them work isolated with their own data.
– We see a tendency that the different business units protect and care about their own data, but few organizations realize their full data potential, because they are not capable of exchanging data across the value chain. Unfortunately, silo thinking means that a lot of knowledge and learning gets lost, says Brian Troelsen and adds:
– In the life sciences industry, there is a tendency to outsource parts of the value chain, such as clinical research, to external business partners. This adds a new layer of data extraction and complexity.
Data is a prerequisite for AI
The next step in technological development will without doubt be the use of artificial intelligence (AI) due to its incredible potential to skyrocket efficiency and cut costs.
– Across industries, AI has been the talk of the town for a long time, and the benefits and enormous cost savings are obvious. It’s no longer a question of if but how you apply the technologies, yet for many pharma companies that are not born out of the tech world and that have a natural conservatism, the use of AI still seems to be in a distant future, says Brian Troelsen.
But AI is a reality, and it is taking place right now – and the use of AI, machine learning (ML) and natural language processing (NLP) is also growing in the life sciences industry. Some organizations have already realized impressive results, cutting as much as 50 man-years off their workload in less than 20 weeks.
– Data and AI go hand in hand, and controlling your data is the prerequisite for AI. This only makes the need to control and utilize your data optimally even more imperative, if you want to maintain a competitive edge in the future, Brian Troelsen says and adds:
– At the same time, you can reverse your approach to data and AI by using smart AI tools to prepare your data for AI enablement. For example, there are relevant AI tools to use in the process of data quality assurance and data cleaning, before you apply data in other relevant AI tools.
Taking the temperature on data enablement
The potential for pharma companies to leverage their data is enormous, yet in many organizations it remains unfulfilled. That is why NNIT’s Expectation Barometer 2023 is dedicated to put focus on data enablement in pharma companies, taking its starting point in the observations and experiences NNIT sees in this field. With the survey we collect data on the use of data in the life sciences industry to deliver fact-based and relevant analysis and advice on how the industry can move forward.
– Using data to solve concrete problems with concrete solutions is where the magic happens. But the truth is we still see a huge difference between the pharma companies that leverage the full value of their data and those that are still struggling to translate piles of paper into valuable data. With this year’s Expectation Barometer, we want to take the temperature across the industry, and use this insight to present both the challenges and potential solutions, Brian Troelsen says.
– Therefore, it is important to me that the Expectation Barometer inspire other players in the industry by showcasing what has already been achieved and what can be achieved when you excel in the discipline of data enablement. Raising the bar in the data field is a prerequisite for staying competitive as a pharma company, and ultimately improve the quality of life for patients. AI, ML and NLP are not futuristic technologies, they are tools you can use right now, Brian Troelsen ends.