Comment: Water's digital future
The opportunities and challenges for the water sector around the use of big data and analytics give rise to fundamental questions over the perception of the operator, writes Mark Kaney
by Mark Kaney, Director of Asset Management, Sweco UK
The water sector is not alone in driving productivity and efficiency through big data. Like many others, our industry is really starting to embrace the era of connectivity, exploiting large volumes of data and analytics which enable operators, manufacturers and service providers alike to realise savings through the asset lifecycle.
Of course, this digital shift is not without its challenges and considerations. Water companies across the UK and beyond are grappling with the balance of opportunity and effectiveness, making critical decisions around the implementation of new digital technologies to ensure they can improve results without shifting their balance of resources too far ahead of their own strategy and capabilities.
With a host of technologies now available, we could invest at many stages of the asset lifecycle and expect to see improved efficiency. So, in this relatively early stage, where should the balance of effort be?
To help understand this, it is important for any organisation to ask themselves, ‘who is the operator?’
Is it the individual working on the specific asset at the treatment works, or is it his colleague analysing remote information on SCADA software back at the depot? You could argue it is the employee at the centralised command centre, analysing and acting on data from all assets to deliver a complete picture. Or is it even further upstream, where asset strategy teams will be testing theory and performing calculations to drive the strategy through which assets are operated remotely?
For some, the operator is no longer a defined employee or team of people. Like the stocks and shares market, it could actually be a series of automated algorithms that make calculated operational decisions based on a huge amount of data, collected remotely and analysed in seconds by a computer.
The reality is that it is a combination of all these things. In this digital age, our perception of the operator is changing.
Of course, one thing that is not in question is how effective analytics and data can improve efficiency.
The possibilities seem almost endless; from augmented reality interfaces on site showing actual and optimum performance information of assets, to the use of analytics to refine data collection and inform alarm handling at the facility control centre. Regional centres can take advantage of multi-site performance trending and remote optimised use of network storage, while the integrated control centre can implement agile asset strategy and planning functions, using modelled response scenarios.
This is just a small selection of technologies available today in the water sector and the results are demonstrable, whether earlier, more proactive interventions, better batching of work, or enhanced modelling of future trends and expected performance. However, integration of data in this way does not come without its challenges.
As we integrate the asset impact points, it becomes clear how we can get more value out of analysing the individual components as a whole; the asset, the site/facility, the command centre, the regional centre and the planning/strategy function.
However, with greater opportunity comes greater risk. Make an error on one data set and the resulting impact may be relatively contained. If we move towards one connected data store, the ‘ripple effect’ could be far reaching with more severe consequences. It is clear that with larger scale, more connected information, we must be even more confident that data is reliable and the analysis is sufficient to give an accurate result.
Ultimately, the topic of data and analytics in the water sector will always come back to one question: ‘how far should we go?’
If we intervene at the asset, we can get a more immediate, on-the-ground picture informed by descriptive and predictive analytics. If we do so from the control room, it becomes far easier to get the whole picture, standardise the approach and ensure decisions can be made based upon the same information and assumptions.
What is clear is that there is no ‘one size fits all’ approach and that companies must decide the best approach for them. There is no single recipe for success, but better integration of data and analytics can improve efficiency. The question is, how far do we take that integration?
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