Sinking or Swimming in a Data-Enabled Water Utility: Q&A with IWA Expert Oliver Grievson
Data and digital technology are increasing priorities for the water industry, helping utilities solve real problems. But while there are many paths to digitalization, there are also pitfalls.
Making Waves spoke to water expert Oliver Grievson, Chair of the IWA Digital Water Program, who has vast experience working with utilities. Grievson outlines how utilities can ride the wave of digital technology and avoid being wiped out by too much information.
There is a lot of discussion in the water industry about digital transformation. What do you think is its role for the sector?
If you ask 100 people for a definition of digital transformation, you will get different answers. There is no set path, no set language, for digital transformation. It is not a silver bullet. It is a collection of tools that can get the best out of a dataset to glean insight into what is happening in the real world.
At times the water industry has suffered from being data-rich but information poor. Water companies collect reams of data, millions upon millions of pieces a day. It costs money, but maybe 99.9% is not used effectively. In my experience, the trick is translating that data into something useful for stakeholders.
You mention that there is no set roadmap for digital transformation. What steps can water companies take to optimize their digital journey?
For me, the first step of digital is stakeholder engagement. Understanding the informational needs of everyone inside an organization, from the CEO to the operators on the ground. Taking that and understanding what we need to deliver that information. It is truly about people, process, and technology - in that order.
Speak to the operators on the ground, the person working inside the utility, and ask them what information they want to see. Speak to the chief executive to find out what information they want to see on their mobile device first thing in the morning. Then filter that approach across the organization.
People will consume data differently, depending on their role. An alarm handler in a control room needs fast, accurate data that improves situational awareness rather than flooding them with numbers.
An asset manager or planner needs something different. They need information that breaks down data silos and pulls from various sources such as GIS, SCADA, and financial models. The result you want is something that can help you make investment decisions and ultimately improve the overall operations. That data doesn’t need to be instantaneous, but it does need to inform broader decision-making.
Each might be working off different parts of the same data set, but translating it into actionable information tailored to them makes it particularly valuable.
You mention the need to have accurate data that you can rely on, how important is that?
Data quality is one of the main elements that could hold the industry back. The right data will get you the right results. That is why you must prioritize data quality and avoid drowning in numbers.
When you start modeling data, it can expose weaknesses in the model and the data collection instrumentation. An effective system of improvement is vital. Look at what you want to achieve and iterate through the models and instrumentation. Then you can start increasing the complexity of the model.
We have seen reported spills because of poor instrumentation installation or the wrong applications in the wrong place. That is a real warning sign. Once installed, instruments also need to be well maintained. When it comes to data, details matter.
As you said earlier, digital transformation is “not a silver bullet.” How can the industry evolve its approach?
We often hear about legacy issues holding us up. Or we hear about a lack of resources or people. There is always a reason not to do it.
That is why highlighting good examples that show value and help utilities build a case is so important. Irrespective of the size of the investment, someone will need to stand in front of an investment group and justify spending money on new ways of doing things. Getting it over the line will be a struggle if the benefits are not communicated well or understood. A lot of this comes down to communication.
There is great value in looking at common data paths, such as what is being developed by the Smart Water Networks Forum (SWAN). To me, there is a framework – you need to have the instruments, the communication strategy, and the processing power. Then, you need to have the areas of interest – leakage could be one or pollution could be another.
Where have you seen this approach work?
Look at what has been achieved in Valencia, Spain, with one of the most advanced uses of a digital twin in the industry.
Drinking water in the area is supplied from two treatment plants and distributed through a 200 km-long mains system. The grid network is complex and requires in-depth knowledge of the system in real-time, which is something the local utility worked on with Idrica – one of Xylem’s partner companies.
The system succeeded because the model and monitoring system was tested, iterated, and used correctly. Once it was put into practice, the system was fine-tuned. As more functionality was added, so was the complexity. Eventually, it delivered real-time insights and forecasts into the performance of the water distribution network, saving around 1 billion gallons of water.
As I mentioned earlier, once you cut through the noise the truth about digital water is it is a collection of tools utilized to get the best out of a dataset and give useful insights. With those tools, there are many untapped efficiencies available to operators. The potential is vast, the key is getting the right building blocks in place.