Water utilities need to embrace advanced analytics. Surveyed utility leaders recognize multiple opportunities to generate value through data analytics, including billing, operations, and maintenance.
However, a “see what happens” approach to analytics adoption will likely fall short of expectations. Here are three critical steps to success. Invest in an analytics platform with robust functionality.
Utilities are hungry for more insightful operational data. It is a significant reason they are adopting AMS and analytics systems.
Water utilities are reaping tremendous benefits across various activities by implementing an advanced analytics platform for their water utility management. These platforms frequently come with customized dashboards that let managers concentrate on the most critical aspects of their business. Customizable charts and graphs provide visibility into time-series data, facilitating the analysis of trends and correlations. They also offer a variety of geospatial data visualization tools, including maps and spatial distributions of sensor locations.
Predictive analytics is another feature of AMS that delivers substantial value for water utilities. This type of analytics looks into the future and identifies likely outcomes using quantitative analysis, predictive modeling, and machine learning techniques. It is typically considered the most actionable type of advanced analytics, enabling utilities to make data-driven decisions and anticipate issues before they occur. The resulting softer benefits of fact-based information translate into improved decision-making that can ultimately improve company culture and bolster financial results over the long term.
Traditionally, water utilities have relied on reactive strategies to address maintenance issues that arise. This approach is time-consuming and often results in unnecessary inspections of healthy equipment.
ML and AI can replace this reactive approach with targeted preventive maintenance based on machine learning models. These algorithms can predict when a pump will likely fail and recommend proactively replacing it before scheduling or conducting a full inspection. It eliminates the need for teams to be dispatched to a site to open a pit or perform an in-depth examination, saving time and money while ensuring the health of critical assets.
In addition, predictive analytics helps optimize maintenance planning by assessing risk to help prioritize issues.
While water utilities need to meet industry standards and work towards a sustainable infrastructure, they can do it much more efficiently by harnessing analytics. Water utilities can track and prioritize their assets for maintenance using an IoT-powered asset management system. It helps them avoid costly repairs and minimizes nonrevenue water losses, which leads to a better customer experience.
A clear vision of what you want to achieve with data analytics can help create a more compelling business case for your AMS. Utilities prioritizing conservation are more likely to consider analytics functionality to reduce water loss and improve meter accuracy. They are also 24 percent more likely to consider leak detection capabilities than other utilities.
A typical yearly savings of 10 to 20 percent on maintenance operating expenditures and 20 to 30 percent in capital expenditures is achievable for water utilities when they adopt predictive analytics models and implement an efficient maintenance plan.
In addition to reducing water loss, preventing sewer overflows, and managing asset health, data analytics unlocks operational efficiencies across the entire utility. Utility administrators should consider engaging the full spectrum of their organization in business case construction, system design, troubleshooting, and continuous system improvement to maximize utilization and return on investment.
With the right technology, water utilities can transform data into insights that inform all operations. Many of these advanced analytics capabilities are available in the market today, though a few still need to be within reach for utilities. This research highlights gaps in the analytics marketplace that vendors and water utilities should address.