Life is Still Beautiful
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Advancements in digital technologies are going to transform many aspects of life sciences industry. They will revolutionize the way companies operate, manufacture products, distribute products, innovate, conduct research, and meet consumer expectations.
Trend 1: Does data make sense?
According to Craig Mundie, head of research and strategy at Microsoft, “Data is becoming the new raw material of business.” Applications of big data analytics are growing rapidly across many industries, but the life sciences industry is just in the beginning of the revolution. The two main technologies that will make a massive change in the industry are the following:
- Enterprise analytics: This technology takes full advantage of the data in the company, using it to adopt new business models, trigger the latest innovations, and optimize the performance of each department (finance, operations, supply and demand chain, strategy, IT, HR).
- Advanced data science: It is a technology that can be employed in various fields within the areas of statistics, mathematics, operations, and computer science. Companies can extract insights from the data. They can then apply those insights to make themselves more efficient.
Both technologies have invaluable applications to the life sciences: more personalized medicine, a greater variety of products, improved performance of sales and marketing, and better decision-making abilities. The chart below describes the process of converting raw data into processed data by using these technologies. That allows decision-makers to move faster from hindsight to insight to foresight.
Trend 2: The last invention we do as humankind
Machine learning and deep learning have enormous applications to life sciences: to our health care, pharmaceuticals, food processing, biotech, and e-health. With machine learning, companies in the life sciences industry can provide more efficient customer service, enhanced logistics, and better equipment. According to research from the McKinsey Global Institute, 45 percent of all work activities in the life sciences industry can potentially be automated. On top of that, 80 percent of those automated activities can be performed using machine learning.
In addition to machine learning and deep learning, artificial intelligence (AI) is making a large-scale impact on the life sciences industry. AI can help analyze vast data sets arising from clinical trials, health records, and genetic profiles. In pilot testing, it was reported that the adoption of AI technologies has reduced healthcare costs by a staggering 50%, while improving patient outcome by even more than 50%. According to Amkidit Afable, head of development and deployment of new go-to-market strategies at Johnson & Johnson, “Disruption is already coming from the outside. With AI, for example, we can help a doctor make better, faster, more accurate diagnosis and treatment plans. We have to force ourselves to ride with the tide.” It is no wonder that venture capital and private equity firms are investing in AI technology companies, especially in startups that are targeting the life sciences industry. In 2016, an astounding $790 million was invested in AI technology companies in healthcare and drug discovery – and that trend will only continue to accelerate.
Trend 3: All you need to know is in the cloud
Cloud technology is disrupting the traditional patient value chain. The implementation of cloud computing can improve the quality of healthcare data. In recent years, numerous government bodies and companies are making the shift from off-the-cloud onto-the-cloud, making the healthcare data accessible across the organization, thereby breaking down the walls separating various departments.
In the near future, cloud applications will have a substantially positive effect on almost every aspect of the life sciences industry — in research & development, operations, enterprise resource planning, financial management, data analytics, compliance & risk, and innovation. According to Digitalist Magazine, by 2025, we will be in a consumer-driven global life sciences market. Therefore, companies who will not adopt cloud technology will be at a big disadvantage. Staying off-the-cloud will render them unable to quickly crunch big data and thus to rapidly react to customer demands.
Trend 4: Is it really secure?
With increasing usage of data storage on clouds, the security of the cloud becomes increasingly important. The risk of a data breach is much bigger in the pharma and healthcare industries. The reason is that hospitals and pharma companies have a treasure trove of intellectual property and sensitive patient data that is vulnerable to hacking. Governments have massive information databases of population healthcare and human life science. As noted by Ann Sellar from Crown Records Management, “It takes 20 years to build a reputation but just 5 minutes to ruin it with a data breach… and then up to 2 years to rebuild it”. Those types of cyber attacks are excellent examples of the enormity of the risk of a data breach involving highly sensitive data. For example, NotPetya ransomware attack at Merck cost the company $300 million due to the disruption it caused in its operations. Therefore, “Boards that choose to ignore or minimize the importance of cybersecurity oversight responsibility do so at their own peril.”
No wonder cybersecurity is becoming a hot topic in the life sciences. According to Accenture Global, companies spent $84 billion on cybersecurity in 2015, and that amount is expected to grow to $125 billion by 2020. The chart below tells a sad story about the security of our most valuable information, as life science received the lowest score in cybersecurity (19% performance).
Summary: What to do
Healthcare and pharma companies still have a lot of catching up to do when it comes to new innovative technologies and business models. Life sciences companies are generally slow to innovate and adapt new technologies (as we saw above).
My vision is that life science companies should be more innovative, adopt new technologies quickly, and partner with startups in the field of machine learning, artificial intelligence, and cybersecurity.
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