By Kroshlen Moodley, GM: Public Sector and Utilities at SAS
On 22 May, the Western Cape was declared a disaster area as the ongoing drought saw dam levels drop below 10%. This prompted the local government to implement level 4 water restrictions amid warnings that the drought could continue well into 2018.
Here’s what we know about the water crisis in the Western Cape:
- The province contributes 24% to SA’s GDP. Agriculture makes up 4% of that figure and provides 18% of employment opportunities in the province. No rain means farmers will not employ as many seasonal workers, which will impact employment and economic growth.
- The Western Cape is South Africa’s most important export province in terms of agricultural products (fruit, wine, wheat). The drought, therefore, impacts food security as well as local and international trade.
- Reasons for the drought include serious water losses, ageing infrastructure, mismanagement, irregular expenditure and a lack of policies for water maintenance.
Building new reservoirs and finding additional water sources requires planning, careful management and, most importantly, time. With the drought expected to continue, time is another resource we have little of.
Fortunately, using advanced data analytics to uncover patterns and anomalies in water usage and loss, the Western Cape government can take immediate steps that will have a positive long-term impact.
Where’s the water?
But before local government can implement steps to save water, it needs to understand where it is losing water. This could be through technical faults such as leaking or burst pipes and contaminated water sources, or through non-technical channels, such as theft and careless usage.
Technical losses are better dealt with at a macro level, through the analytical monitoring of water sources, treatment plants and distribution networks. But immediate benefits can come from the analysis and management of non-technical losses, specifically those related to consumer education, fair pricing and fraud prevention.
Just as the government loses electricity revenue through illegal connections, millions of litres of water bypass the system every day through fraudulent meter connections, which could have been identified by analytics.
Let’s assume that John’s household uses an average of 300 litres of water a day and has done so for the past three years, according to government data. John switches to a prepaid water system and, because he is paying for his water in advance, the government stops monitoring his usage.
That was its first mistake. If it had continued to monitor John’s usage, the municipality would have picked up that, although he was still using 300 litres a day, he has only been paying for 100 litres since installing the prepaid meter. This would require further investigation.
By comparing his prepaid and post-paid usage patterns, the municipality would have detected anomalies in John’s behaviour. Advanced analytics solutions run these models and calculations continuously and automatically, and can alert the municipality to any problems in real time.
By curbing water theft, the government will have more revenue to put towards infrastructure maintenance and development, which is key to managing supply.
Customer intelligence – what’s in it for me?
Low water rates have made South Africans somewhat complacent when it comes to usage. Apart from increasing tariffs, the government can encourage behavioural change through education campaigns and incentives that encourage people to use less water.
Consumers appreciate getting an alert from their mobile service provider when they’re about to run out of data. Even better is if that alert comes with a discounted offer to top up their data balance.
But there are no incentives for South Africans to use water sparingly, apart from threats of restrictions, which are determined by high consumption levels. But it’s difficult for government to offer such incentives as it is not accurately monitoring individual household consumption. Rather, rates are determined by type of usage – residential or commercial – and area – someone living in Clifton will pay more for water than someone living in Khayelitsha, for example.
But consumers shouldn’t be penalised because of where they live – the Clifton resident could use less water than the Khayelitsha resident. By identifying responsible users through advanced analytics, government could reward them with lower rates than excessive users. This gives everyone an incentive to save water. This could also encourage users to install sustainable systems such as grey water collection vessels for garden use, which would save them even more money and take pressure off supply.
Using advanced analytics, municipalities can start to understand usage patterns. If they notice an unusual spike in usage, they could call the customer to warn them. The municipality might find that the customer is filling up a swimming pool, or it could be alerting the customer to an underground leak that he was unaware of, which would prevent him from benefitting from the low usage rate.
This real-time alert and billing model could also solve some municipalities’ billing crises, as they will be able to pinpoint exactly when high usage occurred if a customer were to query his bill. Municipalities would also no longer have to charge customers an estimated usage rate based on the past or predicted consumption.
Without reliable and accurate data, it’s impossible for government to determine what percentage of water losses are due to technical problems or non-technical ones. Advanced analytics can give government a consolidated view of usage versus supply and demand, which will allow it to better manage its resources and better incentivise consumers to use water responsibly.