HIGH FLYERS THINK TANK

Innovative technical solutions for water management in Australia

University of Adelaide, 30 October 2006

Group B – Information technology sciences
Rapporteur: Dr Michelle Bald

I am just going to present a fairly short summary of what our group talked about. There was a whole bunch of other discussion that sits behind the summary points that I will present now, so there probably will be some more discussion after my talk.

We went through the four categories that were in the matrix, and had discussion around each of those points.

The energy tradeoff one I think was similar to Group A in the sorts of things that we were thinking about. The first was needing to take a whole of system approach and taking account of all the costs of particular systems that you may have. One thing we did talk about a bit was the cost of managing your waste, whatever the waste products may be as the result of an energy producing technology, and actually factoring that into your cost-benefit analysis as well.

We talked about an-individual-to-a-system use, so small systems ranging up to quite large systems, and how they may operate differently. We also talked about the dollar value of power versus water, and also started to think about integrating water within that power system structure.

The discussion on that one came about from the example that SA Water operate their system of reservoirs based on optimising for power costs. So where they move water, when they choose to move water through that reservoir system is all based on a modelling approach where they optimise the whole system to electricity costs. So: how can you actually build water into that system more appropriately?

Obviously, we talked about the need for lower-energy technologies and those sorts of things as well.

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The next area that we talked about was water infrastructure. We talked a lot about the fact that there is a lot of money tied up in maintaining current infrastructure, as well as anything you may look at for improving infrastructure into the future. So if we wanted to put in irrigation infrastructure that was more efficient than what was currently there, there would be a dollar cost to that. But there is also a huge cost in actually maintaining current stormwater systems, for example, and other sorts of current infrastructure. So we saw that there was an interesting tradeoff between maintaining what is currently there and identifying opportunities for improving systems, for example stormwater systems that are getting old and need upgrading: how can we plan into the future to optimise those sorts of things?

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That is where we got into the idea of thinking about information technologies being able to really assist us in predicting where future needs may be and where future opportunities exist, but then also helping us to plan how best to do those: how do you most efficiently design new systems? Then we also discussed that in some cases there may be the need to radically redesign infrastructure systems. Will we in the future be irrigating in the way we do now? Will we actually want stormwater systems that look like our current stormwater systems? So we saw that information technologies could really assist in any radical changes we may want to make in the future to meet future objectives, which may look quite different from our current-day objectives.

We also talked about local versus system approach to infrastructure. At the moment we may have some local systems, maybe small stormwater management systems, and then we have very large infrastructure systems like huge irrigation systems for delivery on a large scale. But often they don't work well together; they don't necessarily match. How do we actually get a system that works well at a local scale and then also moves well up into that larger scale, instead of necessarily thinking about these things as separate entities – small local systems and large systems?

We then talked about the category of standards et cetera. For this one we mostly talked about challenges, which was interesting. We didn't necessarily come up with any amazing solutions, but we really did spend a lot of time talking about, 'Well, what are the real challenges that we face in this sort of area with regard to information technology?'

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One of the big ones we talked about was being able to get some sort of harmony between the different data systems that already exist – the different ontologies that are already out there. How do we get these things to talk to each other, to take data from one source and make it compatible and used in another type of system? Getting that harmony we saw as a real challenge and a real issue for the future.

We also talked a lot about data ownership and data sharing, and the privacy issues that go along with that, and again saw that as a real challenge. We have the mechanisms to make data available across a large number of people, to share data. But then you have these issues of privacy, who actually owns that data, how it is going to be used, how we know if it is going to be used in an appropriate manner as compared with what it was collected for.

And there are issues of interoperability of data, and the idea that data can be globally accessible. You can put it on something like the web and anyone can access it, but then they may want to use it on a fairly local scale. So how do you deal with those sorts of issues as well?

We also talked about how you deal with the sheer volume of data: there is an awful lot of data out there, it can be made available, but then what do you do with it?

Then we started talking about the nanosensors and those sorts of technologies that are out there. It relates back a bit, I suppose, to the ownership and sharing of information: who is actually going to be responsible for deploying those sorts of sensors? Is it something where the ownership would be with governments and those sorts of people, where they would say, 'Right, we've got this bunch of sensors we're going to give to the community. Here, we're going to put them out there and collect all this wonderful data,' and therefore the ownership lies with the government or the institution? Or is it preferable to give that sort of ownership to the community, to the users? So there are lots of issues about how you actually go about getting that data collected, who has ownership, how we then share it.

I guess that relates to an unbundling of data ownership, data management and data usage, and how we deal with that. It is quite a different paradigm from what we have currently operated under, where if someone is going to go out and collect data, generally they own it, they manage it and they use it. If we move to a system where data is shared much more widely, then that ownership and management and usage will sit with different people and institutions. So we thought that was an interesting challenge for the future.

The one thing that came up over and over again through all these issues was the idea of trust, and trust at a number of different levels, from institutions to community members and across a number of different levels.

In talking about risk, we started with how you actually assess risk, through frameworks like ecological risk assessment and those types of things. Then we talked about the need to give people sufficient information for them to be able to make their own assessments of risk, and risk from their perspective. So it is the idea of empowering people to make meaningful risk assessments for themselves, based on their own values and ethics and those sorts of things.

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And then we talked about the risks associated with having data available on a system like the web, where everyone can access it, it is freely available, people can share information, and the idea of going out and collecting lots of information with things like nanosensors and having this great network out there collecting all this information.

We talked a bit about the purpose of collecting information, and the fact that we can go out and collect all the information under the sun and really be driven by the 'need' to collect information – be very data driven in our approach – or we can take quite a different view and have a series of outcomes, or objectives, that we are trying to achieve, and then collect data which is going to achieve those particular outcomes. We talked about the merits and the disadvantages of both those types of things, and that if you are entirely outcome driven and you do not have any long-term, core type data, then you open yourself up to missing information that may be out there – and missing surprises, I suppose. There might be some unexpected things out there that you would miss if you were only even collecting information from an outcome driven perspective.

We also talked about what would happen if the drought broke, and what that would mean in terms of a loss of momentum for the water industry and water reform, and also what would happen if we actually got four years of good rain, and what that would do for the momentum that we currently have in this area. What would happen if water issues started to fall off the agenda, or if the agenda actually changed? And the sorts of systems that we would be looking to set up with the information technology that is out there are obviously long term. They need that long-term vision; they can't operate in very short-term time frames.

Then we talked about the web as a platform for information, and the issues to do with trust in information that actually exists on the web, issues of quality control, and the reliability of the web itself as a platform.