Visualisation is a catch-all term for displaying data, usually via web or mobile, in a graphical, conversational and intuitive way. http://www.igi-global.com/dictionary/visualisation/31943
Nowhere have I seen a more urgent need for visualisation than in utilities. There are tens, or even hundreds of thousands of sensors in a typical water utility company’s network, and the increased need to understand their network better with the ever-closer promise of practical smart metering, means there is going to be an explosion in the number of sensors connected to their systems and consequently, the amount of data they’ll have to deal with.
Water utility control rooms in most large utilities are typically handling well over a million alarms a year and someone is supposed to look at, and make a decision on, every one of them. If we ever get smart water meters, which promise to allow flow, temperature and pressure measurement at every customer, then the problem gets much bigger than it is now. If the water network looks like branches of a tree, then they’ll be measuring down to leaf level. Will all this data help understand the network better? I really don’t think it will until the way network data presentation changes. The benefit of such granular data is clear, as it will enable the water companies to manage their networks in a more pro-active and efficient manner, but it carries the considerable downside of being “Data rich but information poor” and overloading the operations teams.
Better network knowledge
The three main operating costs for water companies are: electrical energy, chemicals, labour. Taking the largest cost item, electrical energy as an example; water is heavy, so water companies consume huge quantities of it to transfer this essential liquid to our homes and industry. If average and peak pipe pressures could be reduced in order to save energy; there’ll also be less leaks, so a double benefit. Treatment of sewage also requires energy intensive processes to clean it to the high standards required before being discharged. This is an area where process improvements and optimisation can again reduce energy costs. If the behaviour of the network and treatment plants can be better understood and optimised in more detail, then the companies can reduce this major cost, and maybe bills will even go down. Everyone will be happy, especially customers and regulators. This is just one example of a concrete business case for visualisation. .
Knowledge is not systemised
However, there is an easy trap to fall into: just measuring more doesn’t make you understand better, in fact it can be quite the opposite, as there can often be quite contradictory measurements in a complex system like a water network. An operations manager will have an innate understanding of the system and how it is behaving – they balance, optimise and cope with emergencies; the model of the network behaviour seems to be hard wired in their brains, but even they cannot do justice to all the information available from the network sensors and optimise the network as a whole for the multitude of parameters required from temperature, pressure, flow, water quality and others.
Single system view
As yet more systems are brought in to monitor an ever-increasing range of parameters, more screens need to be reviewed, maybe with more alarms (certainly the case with IOT). A common cry from both IT and operations is – “Not another dashboard” or “Please, no more alarms”. What’s needed is an integrated, single graphical view that combines graphical information system views, system topography, disaster planning and many others into a single whole. This is not some massive systems integration project that will require huge investment and unable to show any benefit for years, but an over-the-top visualisation that draws data from current systems, but even more importantly, has the ability to learn rules from real people (eg operations and maintenance staff) and for itself (machine learning). Such an implementation would reduce the amount of alarms by a large factor. Couple this with predictive analytics, where, for example, weather predictions could be used to set the network up for maximum resilience to flooding, for instance pumping out all the wet wells (small reservoirs) in the affected area in advance of a major weather event, or to predict when garden sprinklers would be used and for how long.
More systems equals more silos
It’s a fact of life that information systems will be replaced and more of them will appear in the operations environment; each one of course adds value, but at the same time adds workload and another silo of information. But, the value of the whole is much greater than the sum of the parts if they could be seen as a single entity: “The network”. Visualisation of data is a good place to start, I have long held the belief that everyone in a water company should have access to a mobile app that would tell them the health of the network at least at summary level. I can think of a few operations directors who would shake my hand if I could give them an app for their smartphone that informed them, in real time, what was happening in their part of the network; showing outfalls, water quality issues, burst mains, unplanned outages, and other key network parameters.
It’s not so hard to start if you think “Agile”
So, what to do? I recommend starting with the users who have to make the most complex operational decisions and discuss in a workshop environment, how they’d like their dashboard to look. Then build a “wire frame” (non-working prototype), which should be completed in days, not weeks. Constantly iterate the design and build with the users involved every step of the way. This is known as agile development and is the de facto standard for implementing quality visualisation projects. I’d make sure some machine decision making capability, however basic, was also included, as rules development is a key skill. An agile approach to IT implementations is novel in the utilities space, but where I have helped implement it, it has created users who embrace these new systems because they feel ownership, want to make it work and know how to get it improved quickly. With some help, most utilities have the internal resources to be able to do this themselves, they just need to be shown how.