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Top Tech Challenges Life Science Enterprises Face and How to Approach Them

Feb 01, 2021

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Part of Life Science Series

By Monstarlab

We have seen technology consistently rise to the occasion for Life Sciences. Along with its significant improvements made to operations, it has offered the sector multi-billion dollar opportunities and the consumers with greater service and better products. However, what some eager enterprises might miss in excitement for the payoff is that these amazing opportunities come with great challenges. What are these challenges and how do you stay above them, you ask? 

In this article, you will learn:

  • The value of data management preparedness and how the lack of it translates into failure
  • The importance of “personal” planning and the risks of impulsive and templated transformation
  • The gap and delayed dynamics between research and supply chain 
  • The core strategies you can employ to overcome such anticipated or existing issues

Data Management Issues

In the latest report on the latest emerging technology trends in the sector, companies were reported to use technologies such as Artificial Intelligence, the Cloud, and Machine Learning largely for data-related processes [1]. The top three most popular use cases of IT were also all directly linked to data management. Despite such information seeming to indicate a fair level of experience with data, this area of focus proves to be where they’re struggling the most.

As reported by industry experts, data compromise has been identified by firms in the sector as one of the three macro areas of digital disruption that threaten enterprises in the life sciences – with each malware attack averaging costs of $2.4 million and fifty days to resolve [2]. Compounded with the struggles in high data managing costs and the sector being bottom-ranked in cybersecurity preparedness in recent years, this weakness in data management abilities imposes serious losses. Fortunately, there are cost-efficient ways to address these cybersecurity issues. Such also offering solutions to related data issues with processing capacity and interoperability. 

What you can do according to experts:

  • Create a well-defined, coordinated data strategy enabling effective response to a security breach and a more proactive approach to data management
  • Invest more to scale and centralise data processing
  • More heavily encrypt and reduce human interaction with sensitive data through automation
  • Ramp up the number of training sessions and in-house simulations/hackathons to QA your plans
  • Draft and secure more stringent third-party responsibility agreements 
  • Add aligned steps of your own 

Unsustainable Digitisation Efforts

Digitisation has been a visible trend in the Life Sciences sector since its boom in the mid 2010s. However, along with the rise in popularity came an influx of unprepared companies creating either DIY or copied versions of the process. Moreover, scattered, templated, and impersonal planning has brought numerous companies to spiral down, with 70% of enterprise-level attempts ultimately facing failure [3]. With a few structured steps, however, your business could avert such penalties.

What you can do according to experts:

  • Assess your own needs instead of following successful plans that have worked for others
  • Align the timeline, scale, and steps of your efforts with your goals
  • Construct an advanced end-to-end plan in lieu of individual and uncoordinated initiatives 
  • Employ or outsource of a great talent pool equipped to execute plans on a more expert level 

Keeping Up with “Real-World” Innovations

Whether in medicine, nutrition, or in other rapidly developing Life Science industries, the speed of innovation is something to celebrate. However, such also incurs an increased pressure for mass-level productions and technologies to adapt – a struggle all too familiar with the drug discovery, manufacturing, and supply chain ends of the sector. 

What you can do according to experts:

  • Get up close and immerse yourself in the industries you service
  • Regularly analyse updated development projections and forecasts
  • Automate time-consuming and repetitive tasks
  • Optimise data management and interpretation systems
  • Create interoperable software for research, modeling, and even supply chain purposes to boost speed exponentially and get ahead of their game. 

With all that said, it’s important to remember that the digitisation process is almost never perfect the first time, (nor the second or the third), but it can always be worked on and it definitely is worth seeing through. 

Key Takeaways

  • Larger valued data needs larger efforts – Life Science companies need to properly assess and acknowledge the value scale of the information they work with and act accordingly (preferably with allowance)
  • “Owning” digital transformation is essential to your success; ripping off a strategy also means foregoing your own goals in pursuit of an unrelated other’s
  • Forecasts and projections are there to help you navigate the future better. Advance your thinking to advance your success and speed. 

Endnotes:

[1] Axtria, “Emerging Technology Trends In the Life Sciences Industry”, 2020
[2] Accenture, “Build resilience to data compromise in three steps”, 2018
[3] Forbes, “Companies That Failed At Digital Transformation And What We Can Learn From Them”, 2019

Other references:

Accenture, “Life Sciences Companies See the Cloud as a Path to Product Innovation”, 2018
Deloitte, “2020 Life Sciences Industry Outlook”, 2020
Forbes, “100 Stats On Digital Transformation And Customer Experience”, 2019
Forbes, “A Very Short History of Digitization”, 2015
McKinsey, “The Rise of Digital Challengers”, 2018
Statista, “Business Digitization – Statistics and Facts”, 2019

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