New Dashboard Strengthens Water Quality Monitoring at GLWA

Overview

The Great Lakes Water Authority (GLWA) provides drinking water to nearly 40% of Michigan’s population and provides wastewater services to almost 30% of the state’s residents. GLWA recently developed a Water Quality Monitoring Dashboard that aggregates water quality data from collection devices on the Detroit River, including information from real-time monitoring buoy deployed last year near one of the authority’s water treatment plant intakes.


Impact

The new dashboard uses business intelligence tools to clearly present water quality data from multiple sources, enabling GLWA to quickly identify water trends in the system and anticipate changes in water quality at plant intakes. The real-time monitoring buoy, located upstream from one of GLWA’s treatment plants, measures key elements of water quality such as oxygen reduction potential, conductivity, temperature, pH levels, total algae and more. The device, which also includes a camera that captures 20-second video clips every 10 minutes, can serve as an early warning for changing water quality. Before the launch of the dashboard, GLWA team members manually compared information from the buoy with other regional water quality monitoring systems.


Advice

GLWA offers this high-level outline of the steps districts can take to implement a similar initiative.

Identify data sources: Determine each data source and type needed to build a dashboard to assist in correlating required information and providing desired insights. This data may be in several locations across the network or in the cloud. It may be tabular, financial, time-series or spatially formatted data. Different approaches may be needed to access the data, either directly from an on-premises database or through a cloud-based API.

Correlate the data: Once data sources and formats are identified, the work begins to bring the disparate sources together. A separate target reporting database with a copy of the data is recommended to better organize the data for the dashboard. The initial dashboard concept should be considered at this time to determine high-level data organization.

Design the dashboard: Carefully consider how users will interact with the dashboard on day-to-day tasks. Don’t over-design it. You’ll need to answer questions such as: What visuals should represent the data and the association between the data? What filters are needed? How should spatial locations of data be represented on a map?