St. Jude Children’s Research Hospital (St. Jude) was founded with the mission to help children facing catastrophic diseases through research and treatment - all regardless of race, religion or a family’s financial means. Founded in 1962 by Danny Thomas, every day St. Jude provides outstanding medical care and innovative treatments that are funded almost entirely by donations, at a cost of $2.8 million per day.
American Lebanese Syrian Associated Charities (ALSAC) is the fundraising arm of St. Jude, that works hard to ensure generous support reaches those who need it most. Soda is proud to partner with ALSAC, providing the essential data quality assurance and governance needed to help them continue this critical work, so they can raise funds for groundbreaking research and save the lives of even more kids fighting cancer.
With over $1 billion raised annually through 30,000 fundraising events, the accuracy of ALSAC's data is essential to ensure that the right level of funds reaches St. Jude in order to keep it open and operational. ALSAC began their journey to move from a traditional data governance infrastructure towards a data center of excellence in order to be able to efficiently scale and effectively support the strategic goal to maximize impact for children in the fight against childhood diseases. However - the first steps in this journey still relied on manual processes, whereby a SQL-statement would run monthly, with the results shared across the organization to encourage good data behavior and transparency. This approach was time-consuming and didn’t foster a sense of accountability and shared responsibility between IT and the business, limiting the organization's ability to reach its goals.
In 2020, the team deployed Soda Cloud as part of their journey towards becoming a data center of excellence. Alation Data Catalog and Soda Cloud are combined to help users find, understand, and trust the data they need, when they need it. Real-time dashboards and automated troubleshooting within the Soda Cloud Platform assist both users and producers in preserving data quality. Automated insights into data quality, with drill-down capability for failed records allow for corrective actions. Seven dimensions of quality control, with metrics for data health, provide automated troubleshooting and accountability for data producers, to ensure reliable access to data for all users. This has improved trust in the organization's data significantly and increased confidence to make data-informed decisions.