Alteryx Case Studies Washington State Department of Health Leverages IoT for Efficient Data Analysis

Edit This Case Study Record

	Washington State Department of Health Leverages IoT for Efficient Data Analysis Alteryx

Washington State Department of Health Leverages IoT for Efficient Data Analysis

Alteryx

Technology Category	Analytics & Modeling - Big Data Analytics Infrastructure as a Service (IaaS) - Public Cloud
Applicable Industries	Healthcare & Hospitals
Applicable Functions	Quality Assurance
Use Cases	Disease Tracking Time Sensitive Networking
Services	Cloud Planning, Design & Implementation Services Testing & Certification
Challenge	The Washington State Department of Health was faced with the challenge of managing and analyzing a massive influx of data from various sources such as hospitals, schools, and clinics due to the COVID-19 pandemic. Their traditional processes were overwhelmed and the use of virtual machines did not provide a solution. The department's data systems, which had been underfunded for the past 50 years, were built for single purposes, overly customized, and lacked interoperability. This resulted in a lengthy and complex process to clean, transform, standardize, and restructure data before it could be queried. The lack of tools to simplify or centralize this process led to a long time to insight and a great amount of duplicative work done by agency analysts. Read More
About Customer	The Washington State Department of Health is a state agency of Washington. It is headquartered in Olympia, WA, and was created by the state legislature in May 1989 after splitting from the Washington State Department of Social and Health Services. The agency's programs and services aim to prevent illness and injury, promote healthy places to live and work, provide information to help people make good health decisions, and ensure the state of Washington is prepared for emergencies. Read More
Solution	The Department of Health implemented Designer Cloud, a part of the internal CEDAR (Cloud Environment for Data Analytics and Reporting) platform on Microsoft Azure. This allowed data scientists to access raw data and create analytics-friendly tables for program analysts. The analysts could then access these usable data sets and rapidly explore, clean, standardize, and transform data in the cloud for analytics. Designer Cloud proved to be intuitive for analysts, enabling them to perform familiar functions much more easily than in R or SAS. It also provided data quality epis with easy standardization, different clustering algorithms, and the ability to quickly turn free text into categorical data. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Big Data Analytics

Infrastructure as a Service (IaaS) - Public Cloud

Applicable Industries

Healthcare & Hospitals

Applicable Functions

Quality Assurance

Use Cases

Disease Tracking

Time Sensitive Networking

Services

Cloud Planning, Design & Implementation Services

Testing & Certification

Challenge

The Washington State Department of Health was faced with the challenge of managing and analyzing a massive influx of data from various sources such as hospitals, schools, and clinics due to the COVID-19 pandemic. Their traditional processes were overwhelmed and the use of virtual machines did not provide a solution. The department's data systems, which had been underfunded for the past 50 years, were built for single purposes, overly customized, and lacked interoperability. This resulted in a lengthy and complex process to clean, transform, standardize, and restructure data before it could be queried. The lack of tools to simplify or centralize this process led to a long time to insight and a great amount of duplicative work done by agency analysts.

About Customer

The Washington State Department of Health is a state agency of Washington. It is headquartered in Olympia, WA, and was created by the state legislature in May 1989 after splitting from the Washington State Department of Social and Health Services. The agency's programs and services aim to prevent illness and injury, promote healthy places to live and work, provide information to help people make good health decisions, and ensure the state of Washington is prepared for emergencies.

Solution

The Department of Health implemented Designer Cloud, a part of the internal CEDAR (Cloud Environment for Data Analytics and Reporting) platform on Microsoft Azure. This allowed data scientists to access raw data and create analytics-friendly tables for program analysts. The analysts could then access these usable data sets and rapidly explore, clean, standardize, and transform data in the cloud for analytics. Designer Cloud proved to be intuitive for analysts, enabling them to perform familiar functions much more easily than in R or SAS. It also provided data quality epis with easy standardization, different clustering algorithms, and the ability to quickly turn free text into categorical data.

Impact #1	The implementation of Designer Cloud within the CEDAR platform on Microsoft Azure has significantly improved the Department of Health's data management and analysis capabilities. It has enabled the creation of clean, consumable, analytics-ready datasets, facilitating the extraction of insights from the massive data being collected. The solution has also fostered collaboration, with the department building workflows that update tables for complex analysis across multiple teams. This has reduced the time spent by analysts working independently on data preparation. Furthermore, the establishment of data pipelines that teams can create, share centrally, and manage themselves has increased productivity and created a self-service culture, breaking the dependency on IT.

Impact #1

The implementation of Designer Cloud within the CEDAR platform on Microsoft Azure has significantly improved the Department of Health's data management and analysis capabilities. It has enabled the creation of clean, consumable, analytics-ready datasets, facilitating the extraction of insights from the massive data being collected. The solution has also fostered collaboration, with the department building workflows that update tables for complex analysis across multiple teams. This has reduced the time spent by analysts working independently on data preparation. Furthermore, the establishment of data pipelines that teams can create, share centrally, and manage themselves has increased productivity and created a self-service culture, breaking the dependency on IT.

Benefit #1	Reduced time spent by analysts by 25%
Benefit #2	Created a self-service data pipeline, reducing dependency on IT
Benefit #3	Enabled extraction of insights from massive data that was previously unanalyzed

Benefit #1

Reduced time spent by analysts by 25%

Benefit #2

Created a self-service data pipeline, reducing dependency on IT

Benefit #3

Enabled extraction of insights from massive data that was previously unanalyzed

Download PDF Version

Overview

Washington State Department of Health Leverages IoT for Efficient Data Analysis

Operational Impact

Quantitative Benefit