In a matter of years, data has transformed from an intermittent input to a real-time resource — and utilities are still nowhere close to taking full advantage of it.

The ever-widening torrent of data, made possible by the Internet of Things, machine learning and smart meters, has created a gap. This gap lies between the volume of information available to decision makers, and the systems they have in place to analyse and put it to good use.

Think of it this way:

As competition in the industry increases, so does the importance of narrowing the gap — that is, increasing the overlap of data volume and processing abilities — for all utilities who want to stay competitive.

So, what’s the strategy here?

The first step is to understand that a key element of the gap is an incompatibility between old models and new data sources.

The logic and processing used in traditional decision-making systems are based on fixed rules. Meanwhile, modern developments like machine learning and advanced metering technology have turned data into smart data: dynamically-sourced information in a self-improving network.

The takeaway? To fully capitalise on smart data, utilities have to employ smart models to process it.

That means phasing out one-size-fits-all rules and opaque algorithms in favor of performance-monitored intelligent models that can adapt to changing conditions.

The best part about models with performance and accuracy-monitoring built in is that they grant real confidence in decision-making.

No more relying on thousands of algorithms to drive alerts and choices without any way of knowing their individual effectiveness; if an algorithm isn’t achieving a desired outcome, the performance report will show it. You’ll then have the option to tweak the formula or get rid of it altogether.

Acquiring your toolbox

For many in the industry, the idea of having a suite of invaluable tools like this at their disposable still seems like a fantasy. A common assumption is that integration costs would be too high, and current systems too entrenched, for an average utility to realistically throw out its old decision-making models and replace them with new ones.

There’s some truth in that. Organisational change is difficult and expensive, especially when it involves adjustments as comprehensive as these.

While committing to an internal system overhaul has its benefits, there’s a case to be made about a superior way to achieve the same results: partnering with an analytical services organisation.

Services like SAS Intelligent Decisioning from analytics software provider SAS, offer utilities the tools to rapidly turn their reams of asset data into high-quality decisions — without the arduous, trial-and-error process of revamping the back office.

Grant Dyer, SAS“In addition to the cost and time benefits, partnering with an analytics leader offers a significant advantage over in-house analytics in that you don’t have to worry about constantly upgrading your systems,” said Grant Dyer, Energy, Utilities & Telecommunications Industry Lead for SAS Australia & New Zealand.

“Instead, the provision of strong governance and model management lineage helps to stay on top of new technology and trends, supporting connected analytics (via API) which is crucial to decisioning.

“You can rely on a quality service provider like SAS to have that covered from inception to production and operations.”

To get more specific, an effective decision management service should empower utilities to:

  • Detect potential failures of assets before they happen, saving money and resources
  • Improve trading, hedging and dispatch decisions to optimise wholesale margins
  • Make relevant and personalised offers to customers

SAS Intelligent Decisioning delivers on those targets and more with transparent, automated and governed processes to address the challenges of high-level decision-making.

These decision flows drive automated operational actions in business applications ranging from customer intelligence and predictive maintenance, to risk management and fraud detection.

To find out more about how SAS Intelligent Decisioning can automate and improve your utility’s analytical lifecycle, visit

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