What the water sector can learn from analytics

Deloitte and the Australian Water Association has released the 2011 State of the Australian Water Sector Report and the most important issue raised by the industry is that of “sustainability” and some differences of opinion on whether environmental or economic sustainability is the more important concern. An analytical approach can help inform the industry on these important sustainability issues in different ways. The “smart grid” is an important factor in this to the extent that it is just the technological enhancements that will come with natural replacement of current infrastructure and therefore nothing particularly mystical. As I have said before, the “smart grid” is really just the slow evolution of old measurement technology and the “smart” part is how we get better at extracting useful insight from the data.

A case in point is climatic variability. I am studiously avoiding use of the term “climate change” because the political debate around this is really not useful in the context of managing climate-sensitive resources. The fact that our climate is highly variable is beyond debate and I will leave it for other forums the debate whether that variation is directional, cyclical or some combination of the two.

One thing that the water industry can learn from the electricity industry is around the work done to understand weather related demand and how to account for that variability. I have said before that while I do not believe the electricity utilities have yet cracked how to properly account for weather-related variability of demand, they have done a lot of work in this respect that could yield insights for water utilities. If this variability can be properly understood then we can isolate underlying growth factors and develop consumption scenarios under different hot dry climatic scenarios. From an analytics point of view, if we can model these down to individual consumers then we can develop incredibly rich scenarios with different cohorts of population responding in different ways. To this extent all utilities would do well to turn to scenario modelling rather than traditional forecasting in order to better understand the underlying growth in demand and provide a solid methodological basis for informing the policy debate (I’ll talk more about the difference between forecasting and scenario modelling in a future blog post).

In terms of economic sustainability, pricing and price setting is the key analytical exercise. Understanding price and demand elasticity is the critical element in developing future economic sustainability of the water industry. This is still a way off for water but is worth considering because it can help target spending on network infrastructure renewal so that the right data is collected for future modelling. Usually, elasticity is expressed as an average for all users. What is far more important to understand is the distribution of elasticity with a given population and whether there are other factors that describe elasticity segments. This can help drive product differentiation and demand management strategies which in turn support the economic sustainability of the network.

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