Why Toronto Water Opted For Business Process Improvement Software Over A Data Warehouse

Background: The City of Toronto Water Department, Ontario, Canada (Toronto Water) serves nearly three million customers who rely on over 1,500 employees to keep their operations running at their peak. Toronto Water is responsible for drinking water supply and four water treatment plants, wastewater collection and an additional four wastewater treatment plants, as well as stormwater management. While managing these assets and the related processes, a group of self-described 'hard-core' data users were experiencing challenges with effectively utilizing Toronto Water's supervisory control and data acquisition (SCADA) and laboratory information management system (LIMS) data on a regular basis to execute critical tasks.
Selection Process: In June 2017, a cross-functional 'Operational Intelligence Group' was formed within Toronto Water, comprised of plant engineers, central process engineers, and members of the process controls systems group. The group performed market research and became aware that data management software solutions were available that could improve efficiencies and support their complex workflows. The group interviewed vendors and other large utilities from around the world finding that complex data warehouses systems were commonplace, and typically require a high level of effort to sustain. The group also drew from their own understanding of unsuccessful large deployments of enterprise systems to create requirements for a new data management solution. The requirements included a software application that would transform their existing data systems into a single, powerful view of Toronto Water's operations. The software had to support hundreds of active users. Another consideration was a large IT organization with strict cyber security measures in place. Above all, an application that fostered power users was desired, but one that did not require a high level of effort to maintain. A unique aspect of the process included establishing internal governance, which was endorsed by all managers and department heads involved with the targeted data systems. Unanimous endorsement ensured that sufficient resources would be made available to satisfactorily fulfill the roles and responsibilities of the implementation project – including Lead Business Owner, Supporting Technical Leads, Advisory Group, and Application Owners. After thorough evaluations, the group concluded that nothing could provide the level of functionality of the top solution and in February 2018 the group made a unanimous decision to acquire eRIS, a SUEZ owned application. An initial rollout project began in Spring 2019 which was completed two years later in May 2021. By selecting a solution that was widely used commercially 'off-the-shelf', and met all required functional needs, Toronto Water started using (and benefitting) immediately. An extensive library of integrations, alarm capabilities, integrated electronic logbook and a modern, intuitive mobile application streamlined adoption.
Phase One Initial Deployment: Toronto Water initiated the project in 2019 at one water and one wastewater plant. The Toronto Water and SES team developed 20 key reports, performed a rollout of plant operational logbooks, and captured manually collected data into a dedicated data repository. They also created a data validation process to ensure manually collected data was reviewed and corrected prior to including in reports. Support and training were keys to success. Customized training was developed in an e-learning platform, which was interactive and online, and was customized to Toronto Water's exact data sources. New users could easily complete the training as allowed by their workloads and shift schedules. Toronto Water created an internal web tool to serve up quick start guides and a wide range of beginner to power user training resources. As of December 2021, Phase One has been completed and Phase Two is currently ongoing.
Phase Two Expansion: Subsequently to the initial project, Toronto Water engaged the SES team to assist with expanding the application to include all remaining Toronto Water treatment plants. Eight (8) additional plant logbooks were deployed along with additional plant operational reports. Other projects were initiated to improve data integration and sharing including one project to automatically transfer daily operational data to a regional partner and to a co-developed data repository and website. This solution eliminated the manual daily process with a fully automated, reliable data transfer tool. Future deployments will include a mobile application for use outside the office, a digital whiteboard, and lockout-tagout tracking tools.
Findings/ Project Outcomes: Throughout the project implementation, Toronto Water not only improved operation and business decision making, but they also experienced a clear return on investment (ROI). Each treatment facility has an employee who is responsible for monthly regulatory reporting. The ability to extract all required process data with enhanced analytics, automated reporting functions, and powerful calculation tools has resulted in faster, easier, and cleaner data and reports. This turnaround has been a key driver in staff-hours saved. The project owner quantified the annual value of staff time saved is nearly $600,000 CAD, driven in large part by staff utilizing the application to access complex process data. The Phase One Initial Deployment was awarded a City of Toronto Excellence Award for both the value and execution of the project. Digital Water does not need to be as complex as artificial intelligence to be highly valuable to utilities. Prior to deriving value from complex analytical solutions, utilities would benefit from enhancing their enterprise data management to better understand and trust their data quality and control expectations for different datasets. Key activities such as managing data governance is critical in case of staff turnover and ongoing validation of key operational advisory systems.
Learning Objectives: ###1. Upon completion, participants will have a clear understanding of how a large utility successfully implemented data governance to build a foundation for digital water programs.
2. Upon completion, participants will better understand the differences between data warehouse systems and dynamic data hub applications, including level of effort required for deployment, security, cost, and data ownership issues.