Monetizing Data in Water/Wastewater
Updated: Aug 3
We are of the opinion that monetizing data in water/wastewater is straightforward, and can essentially be thought of as 5 areas of opportunity:
Opportunity for new offerings to help manage the “silver tsunami”
Machine learning and AI are enablers that allow less skilled staff to per-form more advanced tasks as experienced staff retires. Reduced cost of sensors, circuitry and networking, and advancements in Data science, Computer engineering and Inf. & Comm. Tech is enabling the creation of “smart” solutions, systems and tools at attractive price points.
Providing tools that enable customers to focus on their core competencies and on revenue generation
Customers want to focus on the revenue generating parts of their busi-ness. Water and wastewater are rarely seen as revenue generators. Offerings that incorporate decision-making support and provides opera-tional guidance enables customers to “outsource” much of the higher-level work around water / wastewater, and in return, once established, these offerings are hard for competitors to displace.
“Smart” offerings can add revenue and help gain market share
Incorporating advanced analytics into existing offerings creates a new differentiated “product” with a new value proposition. For example, a “smart” pump can alert a remote service provider before it fails, elimi-nating the need for an emergency visit and potentially reducing the scope of the repairs. Furthermore, providing proactive service strength-ens the customer relationship, makes it harder for competitors to get a foothold, and increases aftermarket service revenue.
Lowering the cost to serve / improving margins by predicting failure
Advanced analytics can lower the cost to deliver a superior offering. By collecting operating data, and using this data to predict failure, non-emergency corrective action can be taken. Furthermore, the cost of providing corrective action may be lower as the failure has not yet oc-curred.
Value is created by generating new knowledge
By combining disparate pieces of information, from various data sources, it may now be possible to better understand a complex system (e.g. a water distribution network), and predict with better accuracy where the likelihood of failure is higher. The end user, in this case the utility, is willing to pay for this knowledge, as it reduces the operating cost (i.e. reduction in the number of emergency repairs).
Anders Hallsby, Partner, Mazarine Ventures