The amount of data available to utilities continues to grow exponentially, compounded by the internet, social media, cloud computing and mobile devices. The energy industry already stores above-average levels of data per firm and this is set to further increase as greater volumes of consumption data are collected with the roll out of smart meters. While this clearly poses a challenge for companies, it also offers unprecedented opportunities.
A study by the Centre for Economics and Business Research for SAS UK found that “data equity” – the economic value of data – could be worth £5.4 billion to utilities over the next five years. Most of this value takes the form of potential efficiency savings, worth £4.7 billion. This is made up of improvements in customer intelligence (£1.9 billion), supply chain management (£1.5 billion) and better quality management (£1.3 billion). The industry will have to employ “big data” analytics to realise such savings. Here’s how:
Customer intelligence.
· the ability to profile and segment customers based on socioeconomic characteristics lets utilities market to different segments based on their profiles; increasing satisfaction and improving customer retention rates;
· online social network analysis enables businesses to monitor consumer sentiments towards their brands, react to trends as they develop, and identify influential individuals within networks for direct marketing;
· using big data to construct predictive models for customer behaviour and purchasing patterns helps appraise each customer’s lifetime value to an energy supplier, increasing understanding of when and why they are likely to switch. This allows resources to be devoted to acquiring and retaining profitable clients, thereby raising overall profitability.
Spanish energy company Endesa, for example, segments its customers according to their environmental sensitivity, geographic location, customer lifetime value and propensity to churn. The idea is to extract more and more value from them by offering new services that keep them satisfied. Analytics also help Endesa cross-sell – for instance, by bundling gas sales with maintenance services. Gas sales in Andalucía are now five times their previous level, while sales have doubled in Cataluña.
Supply chain management.
· because supply chains are complex, producing much data from various sources, utilities using analytics to forecast demand changes can better match their supply to these anticipated levels, maximising profitability.
· the sharing of big data with upstream and downstream units in the supply chain helps avoid inefficiencies arising from incomplete information, helping to achieve demand-driven supply and just-in-time delivery processes.
Quality management.
· predictive data analytics can be used to minimise performance variability and pre-empt quality issues via early warning alerts;
· real-time monitoring enables managers to make swifter decisions to address quality, which ultimately reduces customer attrition, supports brand equity and increases profitability over the medium to long term.
As the volume of data created increases and big data’s value is unlocked to greater effect by technological advances, we expect data to start appearing on the balance sheets of companies that realise its financial value. Furthermore, we expect the efficiency and innovation gains of data-driven technologies to play a vital role in ensuring utilities maintain a competitive edge in increasingly tough times. Analytics have the power to generate growth, reduce debt, create jobs, develop new innovations and deliver greater operational efficiencies. Utilities must get to grips with the big data challenge to reap the opportunities it presents.
Cindy Etsell, sector lead, utilities, SAS UK & Ireland
Proving the need for data governance
Good data will make utilities smarter and more efficient. The challenge is how to demonstrate there will be a higher rate of return from investment in data governance and data management than from using the same budget for other purposes.
One of the most successful ways is to identify real-world scenarios that reveal hidden costs. Take reading an electricity meter, for example. If 2001 units are logged when fewer than 2000 units were expected, the operator may be asked to re-enter the readings to ensure a keying error was not to blame. This will incur unnecessary cost and complexity: exception teams have to be employed to spot data items that are outside of expectations, leading to manual reviews, double-checking and even repeated field visits.
Data is fundamental to the operation of utilities but the processes that use data often evolve and expand without any unifying oversight. By applying business analysis to the way data is flowing through these processes and looking closely at where such duplications and redundancies are arising, it becomes possible to prove that investing in a data project would yield significant returns. Lower operating costs, better customer service and more intelligent decision-making can all result from simple changes and improvements in data.
Water suppliers have a unique advantage in building the business case for data management through the data quality standards mandated by Ofwat. These put appropriate data management at the heart of the organisation with a built-in downside risk of penalties if those standards are not met. Even so, arguing the case for a new project, such as a master data management (MDM) initiative, requires a rigorous examination of where data is being handled, how it affects processes and what the return from any improvements might be.
MDM is a prime example of how this value might be delivered. Water companies typically hold mappable asset location information for planning of field service visits. A master data project that prevents duplicated or inaccurate entries will increase field service performance.
According to an Aberdeen Group field force report, the average level of first-time fix across the companies it surveyed was 71 per cent, but for best-in-class companies it was 88 per cent. Even more significantly, overall level of workforce utilisation was 62 per cent, but rose to 83 per cent for best-in-class. If a single field service visit costs £50 and better data could deliver a 17 per cent improvement in first-time fixes and a 21 per cent rise in workforce efficiency, the business case writes itself.
David Reed, founder, The Data Governance Forum
This article first appeared in Utility Week’s print edition of 12th October 2012.
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