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How We Turned Terabytes Of Healthcare Data Into An Automated, Predictive Insights Machine

Fast-Growing Healthcare Startup Accelerates ProductDev With Automation.

One of the fastest-growing healthcare startups in the world data capabilities were growing so fast and becoming so complex that they were pushing the limits of their cloud providers. In some cases, a single data file takes eight hours to process, and some are too big to be run in a single operation.

Using Amazon Web Services (AWS), we helped this healthcare startup accelerate its product development and data processing capabilities by automating and systematizing its core data activities into a repeatable process that is now reused across the organization.

The result: An increased speed of adoption for its data platforms because now internal teams have a standardized, repeatable process for ingesting massive data sets.


First, We Automate, Then We Scale

What makes data valuable? It’s not volume; it’s speed and data relevancy!  The faster the data throughput you have, the more valuable the data is.

To accomplish fast data throughput, we focused on automating core processes in their bioinformatics organization. We developed standardized tools to ingest and process terabytes (yes, terabytes) of data so that anyone within the organization can utilize them in other parts of the organization.

The result was significantly faster data processing. 

The next phase is to scale this platform to other parts of the organization.

0–60 In Six Weeks

We have an internal saying: “Big bang is a big bust, ” and we mean it. What we don’t need is a 3–6 month project plan. We need to get tools into our clients’ hands as soon as possible. 

And we did exactly that. Together, we implemented a first-of-its-kind bioinformatics software proof-of-concept for one of their largest clients on a tight deadline.

The best part: We did it in under six weeks.

Similarly, ConvergeOne deploys experienced engineering leaders to build software and grow capabilities. 

We utilized two small, agile, and highly technical ConvergeOne teams to lead the first implementation of this new data standardization.

This implementation accelerated the transition to their target architecture, and our implementation code is being used as a standard for the rest of the organization to reuse for additional projects. 

Beating Cancer Starts with Precision Medicine

This fast-growing startup knows a lot about being the first in healthcare, and so do we.

We were the first to build two applications using a newly-developed data technology for their bioinformatics and laboratory information management system (LIMS) teams.  

As part of our work with the bioinformatics team and their LIMS teams, we made it easier for them to analyze genes for cancer diagnosis with the freshest data possible.

The (Data) Scientists Know Best

The healthcare startup employs some of the greatest and brightest data scientists in the world to accelerate cancer research. The most important thing for data scientists is that their complex data models are accurately represented in the new applications. 

Our experienced executive consulting team worked directly with their Chief Data Officer and other senior data scientists to develop applications to turn their data models into real, usable, production-ready software.

Speed Is The Game, convergeone Is The Name

Does your organization produce gigabytes or terabytes of data? Are you investing in your product development? Do your teams move quickly, or maybe they don’t move fast enough?

We’ve proven our ability to work alongside one of the fastest-moving companies in the world and served as a showcase for the internal teams on how to accelerate the creation of new data products.

We’d love to help you do the same.

 

     

 

 

 

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C1 is transforming the industry by creating connected experiences that make a lasting impact on customers, our teams and our communities. More than 10,000 customers use C1 every day to help them build meaningful connections through innovative and secure experiences.