Learn how your organization can benefit from our 4-step process, from discovery phase to implementation and beyond.
Our flagship software, Genestack ODM (ODM), and collaborative process are designed to help organizations succeed in their data integration initiatives. Although it’s tempting to jump straight into product features, it is worth spending a few words on describing how we work with clients.
Why? Because this is one of those cases where the journey is as important as the destination, and it is one of the many things that sets us apart from standard technology providers.
The four steps in our collaborative process with clients
- First, we start with a discovery phase to identify gaps & integration points
- Then we deploy and integrate relevant modules of ODM with your existing capabilities, to fill relevant gaps and get your organization up to speed with data management, resulting in an almost immediate return on investment.
- Support and services then ensure that your organization gets full benefit.
- Finally, to keep up with the growth in Life Science Data, we pursue knowledge and cost sharing to achieve strategic and accelerated development
Let’s explore these steps in more detail for a typical organization.
1. Discovery phase
During the discovery phase we typically find, at one end, a fragmented landscape, where an organization stores unstandardized and unorganized data in siloed filesystems and data lakes.
At the other end, the data is consumed by multiple applications, leaving a large gap spanned by many complex and unwieldy links in the middle. The key missing element is a standardized data and access framework.
Does this sounds like you?
2. Deployment & integration phase
During this phase we deploy our relevant product modules with your existing omics and clinical data stores, and connect with the wider data catalog of your organization to bridge this gap. These ODM modules form a central organizing layer for standardizing and connecting data & metadata, which provides a standardized data and access framework for different applications. Your metadata models, along with the terminologies and ontologies of your choice, are fully integrated to enhance your metadata management capability. From this moment on you can easily use API calls to serve your data to data analysis and visualization tools without moving or duplicating data files.
The benefits to your organization are immediate and transformative. Better access to data and higher data utilization increase researcher efficiency and cause fewer redundant experiments, easily amounting to $6M savings per year for a team of 100 researchers, Ultimately, whether you are in the biopharmaceutical, agriscience, or consumer goods sector, it leads to better and more efficient product development.
3. Support and services
It’s no good deploying a fantastic solution if it doesn’t gain traction in your organization and become used. We provide hypercare support, training and workshops so your teams start using the software straight away.
Our services teams are always on hand to address your needs, from best practice industry advice from our data scientists and consultants, to ongoing management of releases / updates and routine maintenance. We allocate a dedicated team working closely with your users post go-live, so that you can focus on getting the most value from your data-driven strategy.
4. Pursue knowledge and cost sharing
What do we mean by this? The omics field is growing rapidly and, as we covered in a previous article, building a scalable and integrated data landscape is technically very challenging. Therefore we believe that building an industry-standard solution together is the only way to strategically avoid common pitfalls and accelerate development. Two of the key areas that we are focusing our development roadmap on right now are single-cell and clinical-Life Science Data integration, especially for ongoing, in-flight clinical trials.
With this model we are working with clients of the likes of Roche, AstraZeneca, and Corteva AgriscienceTM.