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There is, perhaps, no better time on Earth to be a scientist. The acceleration of digitalization in life science over the next few years will enable a scientist to find answers to questions which used to take weeks in just a matter of minutes. The distracting business of manually dealing with logging experiments, re-ordering supplies and other administration duties of the lab will no longer take precious time away from breakthrough thinking. And, breakthrough thinking will thrive. Replicating experiments consistently from multiple locations will be done from a researcher’s smart phone or tablet. Discussion and collaboration about potentially life-altering discoveries and next steps in research findings will happen as effortlessly as a teen Facetime’s her friends on the bus ride to school.
"The future of digital in our industry is going to be distributed cloud computing based platforms for discovery, collaboration and product research and purchase"
While deep advancements have been taking place in the fields of gene editing, personalized patient care and diagnostics and therapies, behind the scenes a new backbone for the digitization of our industry has been constructed frame by frame. The future of digital in our industry is going to be distributed cloud computing based platforms for discovery, collaboration and product research and purchase. The benefits will be felt by both researchers and procurement professionals as we build on the shoulders of the open source innovations offered to the software community by the big names in digital to innovate further.This will offer a no-fail architecture with sub-second response from any connected device anywhere in the world. As you might imagine, it is only in this kind of elastic infrastructure that we can truly realize the benefits of emerging tech science to help us run faster in our efforts to save lives and restore health to those in need.
There are three lenses which are shaping digital transformation:
1. Increased visibility
2. Improved Customer Experience
3. Innovations and offerings of new products and services.
Increased Visibility. With reams of data available online, helping scientists pinpoint what they need, when they need it, is critical. The process of doing “voice of customer” surveys and market studies with a small sample size and making assertions and strategies is just obsolete thinking.
On an average day, we have the opportunity to collect years of online behavioral data of customers. With elastic cloud computing, deep learning algorithms and analytics we can segment customer behaviors and predict the problems they are trying to solve and help them with potential protocols and consumables to get the job done in a frictionless experience.
Improved Customer Experience. We’ve all heard things like, “the customer is in control now,” or “the era of the customer,” etc. But, what is driving this change? Increased visibility into business operations also presents the opportunity to expose that data directly to the customer. We can all see how this plays out in the ecommerce world today. Customers no longer need a physical interface to see what’s in stock, pricing, etc. They can search and buy all on their own through digital means and thus are in control. Through the internet of things, this digital intimacy with end users will only increase. Imagine a drug trial where you are reading information directly from the user’s vitals and they are entering side effect information in real time. Imagine the reduction in failed phase three trials and the improved speed in regulatory filings.
New Product and Service Innovations. The opportunities opened up by digitization have already transformed our world, but we on are the brink of another revolution. The capabilities of softwares like deep learning or cloud technology have essentially allowed us to reset the clock on Moore’s Law. The game now isn’t how small youcan make transistors, but how can you harness resources to do truly massive computations in seconds. Add to this our increased understanding of genomics and you arealready starting a see a blend of digital and real word products. Take CRISPR design tools for example, you load a sequence in, check against known genes and then build your CRISPR. Someday, you may even model a CRISPR effectiveness virtually! Cloud-based IoT eco-systems in the labs are coming in layers of iterations. Longevity is the operative word in decision-making there. Data analytics with ever-increasing predictive qualities are helping scientists to question more and to test those questions from anywhere.
It is not just an exciting time to be a scientist, but also to be a software engineer helping to drive the future of science.