Prior to European Utility Week 2017, I discussed the evolution of strategic analytics at scale for utilities. In line with the premise of that blog, the audience I speak to continually expands as news of what data can do in the sector continues to spread. This year the number of conversations I am having on the engineering side of power distribution networks is very much on the rise as part of this evolution. But why them now?
Firstly, and simplistically – we have more concrete examples of what can be done with data today to deliver on regulation and innovation, whilst taking cost out of those businesses. Previous hype in areas such as maintenance and supply chain of key assets from transformers and circuit breakers, to those assets you would consider as truly dumb with zero data to work with such as wooden poles has been replaced by tangible, delivered projects that can be lifted and shifted.
The future of utilities is intelligent and data driven, requiring this more strategic and companywide approach to data.
This first point I guess is obvious. What might be less obvious however is how these proof points are also helping that audience look at data from a more strategic perspective. Transformation to Distribution System Operator (DSO) is a fine example of something for which the ultimate utopia is generally agreed, and it is also agreed that data is key. This all starts to unravel somewhat though, when you get into the how – all those different ways to work with data as a DSO. Even more importantly in the realms of engineering where things are generally well defined the what – what do we need to buy, and in what order?
The great thing about taking a strategic approach to data is that you can deliver new revenues and cost savings quickly by executing proven analytic use cases today, whilst also building scalable data integration capabilities, plus wider organisational skills sets and processes to deliver all future analytics. This drives these ever more compelling discussion in networks.
Ultimately, the DSO vision will never be 100% delivered and there will always be new things to plan for and to optimise. This makes the argument for strategic data capabilities on which you can run all future analytics without needing to know exactly what those analytics will be today very powerful.