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Data science is a rapidly growing field. A 2017 article by Amar Numanović describes how data science might be used in the public sector. This article recommends using data science for “transparent, accountable, and responsive government.”  Denver and Boston have developed dashboards showing customers the locations of lead service lines. Those utilities committed substantial time and energy to develop and maintain those maps. Denver has committed to lead service line replacement within 15 years and can use the map to prioritize those replacements. Seattle Public Utilities (slides 13-14) uses “data-driven decision making” to plan water main replacement as part of an asset management program. Data is everywhere in utilities. The industry collects data and uses charts and tables to justify many decisions about treatment and customer billing. To think creatively about using data, utility managers need to understand what data is available.

What data might a utility analyze?

Data is collected in paper form or through enterprise data systems. For the purposes of this blog post, an enterprise system is a system capturing and collecting business data. Enterprise systems may contain different modules with overlapping functions. They manage one or more elements of a utility’s business and likely incorporate data entry and data reporting. They are typically complex and are either cloud-based or are installed and maintained on local servers by the utility’s IT department. Some general examples of enterprise data are shown in Figure 1.


Figure 1. Common business areas covered by enterprise data systems. This list is far from complete.


Utilities typically gather data in response to specific business needs. To send bills to the right customers, you must collect and store customer data. A work order system helps a utility track maintenance needs, prioritize, and report back on repairs. 

How should utilities analyze data?

There are many questions that cut across the subject areas shown in Figure 1, such as: 

  1. Which customers are using the most water?
  2. How much are those customers paying for water?
  3. What water pressures do those customers typically experience?  

The information to answer questions 1 and 2 would come from “billing and financials” and an answer to question 3 would potentially come from “operations and monitoring.” Information related to question 3 might also come from customer complaints about low pressure. Finance, operations, and/or customer service staff would need to understand how to pull the data in question out of different modules and how to match the different data fields together. 

The audience for data analysis may be an individual or a group of people. If the audience is finance staff, a query or table may produce the necessary results. Tables give staff the chance to review individual numbers and do their own analysis. If the audience is a utility board, a chart may be a better way to convey information.

How might a utility expand its use of data?

Many utility business questions involve different modules like the examples shown in Figure 1. To really understand the data and make decisions, the utility should involve different staff. A periodic workgroup or steering committee could be tasked with identifying questions to answer. Data analysts would be wellpositioned to gather the pieces together and to coordinate with different business units.

Why would a utility want to become a datadriven organization?

The data projects described at the beginning of this post are major efforts by utilities meeting specific business needs. Identifying business needs and analyzing data to support those needs helps a utility understand its own data better. Operations and planning can be more effectively targeted with greater transparency both inside and outside of the organization.

One Response to “How can Water and Wastewater Utilities Become Data-Driven Organizations?”

  1. Jonathan Leiman

    This is a great idea, but I believe the incentive is to protect against legal liability. Transparency usually leads to increased liability from outside organizations and entities targeting the host organization’s integrity. If a data collection program is designed to deflect/protect against increased legal liability, then the inability to treat source water (whether for price or technical issues) will fall back on load and wasteload allocations of TMDLs.

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