Subject to an increasing number of rules and regulations, and a proliferation of data from the roll-out of smart meters across Australia’s eastern states, utilities are being forced to strengthen their data management systems and processes to avoid costly penalties — and even more catastrophic outcomes.
Data integrity threatened by under-management
Effective data management is critical for all utilities and one of the processes most affected by poor quality data is the “meter to cash” process, the important function of turning meter readings into customer invoices.
If a utility mismanages its data, the impact will not only be felt on the “cash” side of the ledger, in the form of revenue leakage, but also in operational inefficiencies and degradation of customer service.
When combined with a failure of business processes, the consequences of poor data management can even be fatal — as in the case of the accidental disconnection of a life support customer’s power, an extreme but rare example.
On 28 February 2017, the Australian Energy Regulator (AER) submitted a rule change request to strengthen protections for customers that have a person requiring life support equipment residing at their premises.
The final rule made by the Australian Energy Market Commission (AEMC) on 19 December 2017 provides better protection for life support customers, allocates responsibilities clearly and appropriately between retailers and distributors, and improves the accuracy of life support registers. This rule will come into effect from 1 February 2019, but transitional arrangements began on 1 February 2018.
A less extreme but also damaging outcome is the $20,000 penalty that can be imposed by the AER for a breach of the National Energy Retail Law (Retail Law), and the negative brand image that results from the inevitable reporting of these breaches in the public press.
Preventing common data management mistakes
Trent Jenkins, Chief Technology Officer at Brave Energy Systems, said he has observed several common mistakes when it comes to data management in utilities.
“The first failure point is the lack of a clear definition of which systems own which data attributes, and we often see blurred boundaries on which systems manage what data.
“Where this is not clearly defined, the associated processes that maintain and synchronise this data between systems also become ill-defined. Different data in different systems leads to operational confusion and a lack of trust in the systems that support the business.”
In some cases, a utility may have clear definitions of its data ownership boundaries, however, another issue often emerges.
“The systems landscape for utilities is and has been, changing rapidly since the introduction of contestability in the late 90s and early 2000s. In the early stages of this change, there was often just one system (e.g the monolithic billing system). This system would also manage premise and meter data, but as the industry has changed, so too have the needs of this data by other parts of the system,” Mr Jenkins said.
“By way of example, managing meter data has typically been the sole responsibility of the billing engine. This data, once loaded into the billing system is subjected to the rules of the billing engine – this could mean the data is rejected, adjusted or manipulated in order to allow the billing engine to complete its function.
“The use of meter data within a utility has changed significantly over the last decade, however. Additional processes need this information – settlement processes, trading systems, analytics and NUOS charge reconciliation to name a few.
“The question now is ‘how do the rules applied by the billing engine affect these other system processes?’. If I’m adjusting meter data to satisfy a customer complaint on an estimate that is too high, do I still expect my settlement and NUOS reconciliation processes to be accurate? The short answer is no. The needs of these upstream processes are different to those of the billing system and so the default architecture of having the billing system act as the meter data repository needs to be revisited.”
Another common issue is when data consistency is not maintained across all systems, resulting in large, one-off data rectification projects which rarely deal with the source of the data drift and focus on uplifting the data quality at that point in time.
“A common example of this is keeping the billing system aligned with the standing data information within MSATS. It is a complex process to maintain 100 per cent accuracy, but, unfortunately, the failure to do so leads to issues such as operational efficiency, customer dissatisfaction and revenue impacts,” Mr Jenkins said.
“Brave offers standing data reconciliation software that ensures you can regularly identify the differences between your billing platform and MSATS early enough to avoid these negative outcomes.”
Preparing for huge data volumes
The introduction of smart meters has significantly increased the data storage and processing needs of market participants’ systems.
Modern IT infrastructure and its corresponding processing power are becoming even more essential in ensuring utilities can effectively manage their data.
“The move to on-demand scaling and processing that is provided by cloud providers such as AWS and Azure will, in Brave’s view, become core components of the future utility data management strategy,” Mr Jenkins said.
“Brave’s strategy is to ensure our products continue to meet and exceed the processing throughput of our largest customers and we are therefore currently porting our flagship product, the Bravo retail, metering services and distribution solutions, to the Azure Cloud in a Platform as a Service model.”