Big Data Analytics-Potential for Utilities to Profit

Uilities can gain insight into customer trends by analyzing data generated by smart technology. But integrating & utilizing it effectively takes time.
Published: Thu 20 Feb 2014

Utilities are keen to analyze the data which is being generated by smart meters, smart grids, and enhanced customer relationship management systems. Significant opportunities are now within reach as they gain more access to these significant data sets.

Around Europe utilities are exploring how 'big data' analytics can process the vast amounts of information which are either already available, or are shortly to come online from extensive smart-meter rollouts in process or being planned.

Market analyst GTM Research predicts global utility company expenditure on data analytics will grow from US$700million in 2012 to US$3.8bn in 2020, with gas, electricity, and water suppliers in all regions of the world increasing their investment.

Reinvention of utility business models

The new emphasis on data analytics software allows utilities to track, visualise, and predict usage. As a result, traditional business models will be completely reinvented as utilities adopt data management platforms.

To date, the most common use of analytics applications hasn’t really extended beyond traditional business intelligence and data warehouse tools used for marketing purposes.

However, this will change as dedicated hardware and big-data processing platforms begin to enhance processing capabilities to filter, analyse, and condense a wide range of larger data sets into meaningful insight at a quicker rate.

Customer churn

Customer churn is a major concern for many utility companies, especially in deregulated markets where there are more competitors offering discounted deals to potential customers.

Now that utilities have access to consumer usage and profiles, price packages and special deals, optimised to suit individual residential or business consumption patterns, are easier for suppliers to compile. As a result, consumer churn is reduced dramatically.

Social media also gives the utility information on consumer dissatisfaction with price levels and customer service. Although utilities had access to this type of data before, it is now more detailed. Utilities now have the ability to respond effectively to this detailed data.

Smart meters –benefits for the consumer and the utility

Utilities have been focusing publicly on the benefits of smart meters for customers. This is mostly to justify the cost of smart-meter upgrades. However, the utility is privately concerned about using the insight that big-data analytics can provide to improve their own operations. This is especially relevant when it comes to forecasting demand in order to optimise supply, predicting possible outages, and identifying leaks and/or fraud.

Many are now planning to deploy extensive machine-to-machine (M2M) networks which connect a wide range of industrial devices and sensors across their infrastructure, and which throw information from control centres, virtual power plants, and computerised logs into the mix.

The utility wants to obtain more data to make the grid increasingly efficient. They want data about transformers, overhead lines, cables, substations, and engineers. This information is collated and used effectively by the utility’s decision-makers.

Matching demand against supply

Many utilities have ageing grids which fail to deliver on high efficiency levels. To improve efficiency, utilities need to adopt innovative strategies when it comes to delivering performance-based metrics back to decision-makers.

Efficient capacity management involves being able to match supply more closely to demand. This can be a problem in the electricity industry where the commodity in question is hard to store.

Traditional capacity management and planning is changing and utilities have to be more responsive in predicting uncertainty. The big challenge is prediction. While real-time is good, data about 10 or 12 days' time is key.

Consumption of electricity is greatly affected by the weather. Analytics platforms are able to process and analyse meteorological information from a diverse set of sources. IBM, for instance, has developed a weather modelling and power-grid management system designed to optimise the supply of wind and solar power as part of its Smarter Planet initiative, dubbed Hybrid Renewable Energy Forecasting (HyRef) developed in conjunction with Danish wind-turbine manufacturer Vestas Wind Systems.

Being able to predict a cold front and analyse what impact it will have on demand and supply for electricity will allow utilities to be much more proactive in how they allocate supply or gear-up for additional storage or distribution. For instance, rerouting excess energy in one region into others will save on generation costs.

Slow rate of adoption

Despite the advances being made on the technology side, there is no guarantee that big data and predictive analytics will find their way into the utility’s infrastructure any time soon.

Difficulties in integrating existing systems, capital investment requirements and a lack of familiarity with the technology may stand in the way of adoption-at least in the short term. We cover these challenges and others in our article Data Analytics Still Faces Obstacles.

However, there are some compelling factors that may accelerate the process, such as pressure from shareholders and customer groups insistent that utilities from all sectors should do as much as they can to derive value from data assets.

SAP's James McLelland talks from experience, “We have had user groups where all those present nod vigorously [when we explain the technology capabilities] but when we ask what they are going to do with it, they are not so sure."