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Outdated Data Processes Hindered Business Growth

A major healthcare technology company was struggling with antiquated data processes that were slow, costly, and prone to failure. The organization’s data infrastructure had to be changed to drive business growth.


Empowering Stakeholders and Supporting Business Growth Through Analytics

Data Clymer, a Spaulding Ridge company, implemented an overhaul of the organization’s data infrastructure with a modern data stack. We created a new, more efficient data pipeline architecture for business, converting their old ETL process using custom R functions to a modern ELT solution using Fivetran, dbt, and BigQuery.

We then constructed robust data pipelines, streamlined internal reporting, offered expert analytics support, and standardized processes for future endeavors. We migrated their library of outdated dashboards from Tableau to Looker, while creating new dashboards tailored for various departments.

To establish a unified source of truth of genetics reporting for their stakeholders, we made seven new dbt models and over a dozen new Looker dashboards. We crafted six KPI dashboards for operations executives in addition, eliminating slow, manual processes and providing valuable insights for better decision-making.

By implementing dbt and modernizing the pipeline structure, we empowered the business’ data analysts without R knowledge to create new data models. This shift freed up time for engineers to focus on higher-priority projects and not falling behind.

Lastly, we helped curate optimization strategies and best practices for both internal and external stakeholders, offering ongoing analytics support for complex genetics data issues within their internal BI team.


Cutting Data Pipeline Compute Time from Two Days to Two Hours

The organization saw end-to-end data pipeline compute time drop from two days to two hours post-go live. The team can update business reports daily rather than monthly, bill customers on time, and quickly access crucial insights for decision-making. The new testing framework achieved 100% model test coverage and provided data teams with peace of mind, ensuring that any data issues would be detected before they could impact stakeholders.

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