Analyzes Data for a Healthier World with Designer Cloud
Core Challenges
COVID-19 forced the Washington State Department of Health to speed up efforts to move analytics to the cloud. The scale of data coming in from legacy transactional systems and reference tables from hospitals, schools and clinic sites overwhelmed their traditional processes and virtual machines did not resolve the problem. Public Health has been underfunded for the past 50 years and our data systems reflect that. Nearly every data system had been built for a single purpose, overly customized and is not interoperable with other systems. Public health response requires a rapid data analysis to inform decision-making and public health action. Consequently, before one can analyze data they have to conduct long and painstaking work to clean, transform, standardize and restructure data before it can be queried. The tools to simplify or centralize this process were not available, causing both a long time to insight and a great amount of duplicative work done by agency analysts.
How Designer Cloud Solved this Problem
Designer Cloud sits within the internal CEDAR (Cloud Environment for Data Analytics and Reporting) platform on Microsoft Azure. Here, data scientists can access the raw data and create analytics-friendly tables for program analysts. Program analysts can then access these usable data sets and rapidly explore, clean, standardize and transform data in the cloud for analytics. Designer Cloud has been intuitive for analysts and allows them to perform familiar functions much more easily in Designer Cloud than in R or SAS. Data quality epis love the easy standardization, the different clustering algorithms and the ability to turn free text into categorical data quickly.
Benefits
Data Quality
Clean consumable analytics-ready datasets allows for the ability to extract insights from massive data that was being collected, but not analyzed.
Collaboration
Designer Cloud has allowed WA DoH to build workflows that update tables for complex analysis across a number of teams and has reduced time spent by analysts working independently on data prep by 25%.
Increased Productivity
Established data pipelines that teams can create, share centrally and manage themselves without IT has created a self-service culture. Breaking the dependency on IT.