Companies across the life sciences industry have turned to TetraScience to unlock the full value of their scientific data for years. To date, these interactions have focused on the collection and organization of scientific data from customer-specified instruments of interest. However, these instruments are typically chosen by management with an eye towards research or scientific areas of interest, not scientific pain points. They typically also do not consider reusing the data for Scientific AI. This leads scientists to continue using workflows that do not suit their needs for extended periods and to miss the opportunity to improve their work and its outcomes through AI.
The Sciborgs
The Tetra Catalysts offering embeds experts like Scientific Business Analysts (SBA) and Scientific Data Architects (SDA) with the customer to identify the most painful processes for scientists and design solutions with the Tetra Scientific Data CloudTM to address scientists’ pain directly. We call these experts Sciborgs, as we have deep expertise in scientific and technology domains.
I work at TetraScience as an SBA, supporting scientists directly by discussing their workflows with them and identifying their pain points. I then communicate these to the TetraScience team and we work together to address those issues directly and immediately in the architecture we design. Traditionally, architecture would not include deep knowledge of the workflow pain points, so working with the scientists directly the way I do has really helped my customers see value quickly. I have also been able to uncover scientific use cases that they haven’t considered before. The additional benefits of this approach include decreased time to value, prioritization of higher value workflows, and accelerated and improved scientific outcomes for customers.
Removing the Burden
The customers I work with enjoy the Tetra Catalysts offering because it removes the burden of scientific use case discovery and architecture work from them and places it into our hands, the Sciborgs. Once we’re deployed at a customer, we interface with the customer’s subject matter experts (scientists, data engineers and data scientists), perform use case discovery including opportunities for Scientific AI, perform requirements gathering for complete end-to-end workflows, develop the architecture required for each instrument and use case onboarding, as well as develop and implement user training programs where relevant. Allowing us to come in as a trusted partner and interface directly with scientists, data scientists, internal project teams, and management has led to acceleration of use case discovery, improved prioritization of implementation activities, and identification of optimal solutions for end-to-end scientific workflows.
Flexibility
Many of our customers simply do not have the personnel nor the overhead to work through these tasks on a company-wide scale, making a dedicated Sciborg team a point of acceleration and augmentation. Moreover, our packages can be adjusted for more SBA or more SDA support. This allows customers to scale up discovery activities while development resources are at capacity or prioritize architecture building to ramp up development at pace with their internal resources. The Sciborgs also interface with TetraScience’s own Professional Services department, so development of end-to-end workflows and custom work is never stalled due to lack of use case comprehension. All activities regarding the Scientific Data and AI Cloud implementation are constantly supported by the TetraScience team from idea to go-live, making the Tetra Catalysts offering incredibly appealing and valuable to our customers.
Adding Value
In my role as a SBA I’ve expanded the scope of use cases to address pain points customer IT wasn’t aware of and to show the added value of specific Scientific AI opportunities. I have narrowed scope and deprioritized use cases, and given context to prioritized use cases with data such as time savings to scientists, decreased consumables usage, and freedom from outdated formats. Since I have deep knowledge of scientists' pain points, I’m also able to use that knowledge to identify the value of the Scientific Data and AI Cloud for our customers.
My Customers
Recently, in sessions with a multinational pharmaceutical company, I was able to determine that creating a solution for just two of their use cases would result in 1,000 hours of time saved a year. These activities have increased the value of the Scientific Data and AI Cloud for my customers, led to more end-to-end use case deployments, and increased the level of excitement and demand for the Scientific Data and AI Cloud within the customer. One scientist went so far as to say, “I’m old and stuck in my ways but this is something I’m really excited about.” This is the level of excitement I seek to extract from our customers, and the Tetra Catalysts program makes it possible!
Do you want to learn more? Just visit our Tetra Catalysts web page.