Water & Wastewater Treatment

38 | march 2015 | WWT | www.wwtonline.co.uk In the know Technically speaking: data analytics O fwat's Periodic Review 2014 determinations have made one thing clear: water companies must become significantly more ef- ficient, providing better service with less expenditure. Current working practices are mature and further improvements are unlikely to realise the efficiency savings required; a step change is needed. In many industries there has been much talk about the benefits of Busi- ness Analytics and how competitive advantage can be gained by embrac- ing rapid fact-based decision mak- ing. The growth of many of the most successful commercial organisations has been underpinned by Business Analytics with exemplar firms ranging from Google, Netflix and Amazon to Tesco and Wal-Mart. In the retail sec- tor, offers can be targeted at customers individually based on tracked buying habits or stated likes and dislikes. Pur- chasing analytics can tell supermar- kets how many extra burgers to order when the temperature is forecast to rise to 3oC on a weekend in summer. The success of predictive crime analyt- ics has grabbed headlines and shown Analytically Thinking? advanced analytics have the potential to transform the water sector, if they are used across the business and are backed by leadership and the right expertise that advanced analytical principles are well established and can bring significant value in many different sectors. So could advanced analytics enable the efficiency gains required in AMP6 and is the water industry ready to embrace such techniques? To investigate the potential benefits of advanced analytics to the water industry it is first necessary to understand the three different types of advanced analytics. Descriptive: Analytical techniques are used to look at historical data to provide insights. Predictive: Relationships from his- toric and / or current situational data is used to describe what could happen in the future. Prescriptive: Analytics take predictive outputs and simulate and optimise intervention options to either directly make interventions or to tell operators exactly what to do in response to evolving situations. There is a common misconcep- tion that advanced analytics relies on sophisticated and novel analytical MArk SMiTh managing DirecTor Wrc techniques. The reality is that the analytical techniques are not neces- sarily novel but that they can be im- plemented in a business context. The application of the Capital Maintenance Common Framework in business planning means that water companies are familiar with a range of statistical techniques, as highlighted in the table (below le˜). This range of statistical methods can readily be used for advanced ana- lytics alongside additional methods. The advanced aspect relates not only to the statistical methods used but to the ability to integrate these tech- niques with corporate datasets and make the results available in sufficient time to make an on-going difference to the operation of the business. In recent years the water industry as a whole has invested in corporate IT systems. This has resulted in the crea- tion of large corporate datasets that include customer contacts, work order data, asset inventory, and telemetry data. The quality of these datasets may not be ideal but experience has shown that valuable insights can still be gained. The more data is used, the more its value is realised resulting in improved data quality. This data was created to manage many different business functions ranging from responding to incidents, paying contractors, business planning and monitoring levels of customer satisfaction. However there are signifi- cant opportunities to use advanced analytics to gain business insights that can improve efficiency thus increasing the return in the existing investment in corporate IT systems. For network operations, there is im- Asset Group Methods in common use infrastructure Generalised Linear Modelling, survival modelling; Markov chains Non-infrastructure reliability modelling, FMEA, Fault trees, survival analysis; regression analysis iAN DAwES Senior BuSineSS analyST Wrc

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