A fast-moving healthcare startup set out to determine whether years of intraoperative recordings could be used to improve outcomes. Over a decade of high-fidelity time series signals — captured through probes, sensors, and real-time monitoring — promised extraordinary potential for both clinical and commercial applications.
The challenge lay in preparing this rich but complex dataset so it could be analyzed, validated, and made ready for AI training. With multiple formats, varied structures, and specialized software dependencies, the organization needed expert help to evaluate readiness and establish a clear, scalable approach.
They turned to Six Feet Up to assess the data’s viability for AI training and create a pathway to actionable insights.
Six Feet Up began with a comprehensive data inventory by mapping formats, structures, and metadata to understand what could serve as a reliable foundation for AI work.
To make progress transparent and collaborative, the team introduced a simple readiness model:
Next, Six Feet Up built a cloud-based data architecture to ensure secure, centralized storage with fast, scalable access. Data was migrated into modern, query-friendly formats and integrated with analytical tools for validation. Automation scripts replaced repetitive manual steps, improving both speed and accuracy.
Domain-informed processing techniques were applied to ensure that the resulting datasets were clean, consistent, and relevant for machine learning applications—without altering the underlying integrity of the original time series signals.
In just weeks, the client gained the ability to query and analyze data points from multiple sources in one centralized, scalable environment. The improved structure and accessibility meant teams could connect the dots between previously siloed datasets, uncovering new relationships and patterns.
Leadership can now explore these insights quickly and with greater confidence, accelerating decision-making and opening the door to deeper analytics in the future. Operations benefit from reproducible, documented pipelines that ensure consistency and make it easier to integrate new data as it becomes available.
Most importantly, the project demonstrated that significant value can be unlocked before data is “perfect” — by creating a trusted, queryable foundation, the organization positioned itself to pursue more advanced capabilities when the time is right.
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