Data‐Driven ValueJames Dunning, Chief ExecutiveSWAN Forum Conference, Utrecht, May 2012

9th May, 2012

• The key to value realisation using smart water networksis data– data deployed to overcome the barriers to

d i dadoption; and– data deployed to realise value.

This presentation was delivered at the SWAN 2012 Conference, Utrecht, Netherlands in the Conference Session “Show Me the Money”  assessing the Return on Investment potential of smart network technology in the water sector.  Notes have been added 

in line with the presentation delivered on the day.

• Following the Hollywood theme of “Show Me theg yMoney”, if “smart water networks” was a moviecharacter it would be the cool one in the room, slightlymaverick, a good haircut and top of the class on thedance floor too.dance floor too.

• But despite that, it is also the case that– barriers to the adoption of smart network

technologies remain pretty high; andtechnologies remain pretty high; and– that there’s no doubting many of technology

providers would love to “show the money” towater utilities across the globe.

• But are those utilities ready? Are they listening?

• If the utilities are not yet listening, then smart waternetwork technology providers have a choicenetwork technology providers have a choice…

• One option is to– rail against the injustices of utilities not embracing

smart water technologies across the globe;– challenge regulators to do more to support innovation;

or– cajole water utilities to embrace this modern era

• Alternatively– technology providers can try to understand better the

barriers the water utilities face– they can work together, as shown by this SWAN 2012

C f i i th t i2O’ iConference, in ensuring that i2O’s progress inMalaysia, the adoption by Thames Water of Syrinix’sTrunkMinder technology, Takadu’s success in Portugalare not individual successes but instead illustrative ofthe general adoption of smart technologies.

• But how can that be done?– by identifying and understanding barriers to adoption;

and– overcoming those barriers by showing the power of

data at a macro‐ level an individual utility level and atdata at a macro‐ level, an individual utility level and ata product level.

• At a macro level, there cannot be many who these daysdispute the multiple water‐related challenges bearingdown upon the industry and population at speed

i th Middl E t d N th Af i l ti– in the Middle East and North Africa populationshave increased by 140% since 1970 whilst beinghome to the top 16 water stressed nationsaccording to the Maplecroft Water Stress Index;

– at $1 per cubic metre, traditional waterproductivity gains from the likes of desalinationare prohibitively expensive in a global economythat rarely prices water accurately;

– non‐revenue water losses meanwhile remainstubbornly high – at 38% in Sao Paolo and 40% instubbornly high at 38% in Sao Paolo and 40% inMontreal to name but two, whilst by 2050 70% ofthe world’s population are expected to live incities.

• There is also, of course, the report by the 2030 WaterResources Group which identified

– that ignoring efficiency gains, global demand isexpected to increase from 4500 billion cubicmetres to 6900;h f f 40% l b l l d fi i– that of a forecast 40% global supply deficit, or50% for a third of the population, business asusual approaches will not get anywhere nearclosing that gap.

• As evidence of this pending crisis being recognised, inthe SWAN survey from late last year 84% of respondentsrated real‐time monitoring as important and not just forone or two reasons but across a broad spectrum ofmeasuresmeasures.

• But that survey does concede itself that it was in partself‐selecting, that only those paying attention to smartnetwork management are likely to respond to a surveyabout it.

• But that in itself highlights the challenge technologyproviders in the smart water network sector face. Fewdispute the macro‐level challenges the sector faces but,p g ,at both individual utility and policy levels, barriers tosmart network adoption exist and it is the challenge forsmart water network technology providers to overcomethose barriers.

• But what are these barriers?

• As the 2030 Water Resources Group report identifies, alack of detailed data is no doubt part of the problem fordeveloping and developed economies alike.

• But the barriers go beyond that.

• Cost is certainly often cited as a barrier though it is noticeable howquickly money can be found when pressure mounts as shown bysome of the measures being taken in the UK now a drought hasbeen announced.

• Understandably, other barriers include– “if it ain’t broke why fix it?”– “it would cost a fortune to collect all that data and we

wouldn’t use it anyway, we don’t have the time”

• Ignoring the role of innovation, of data deployment all aroundthem, others query if there really is a need noting that flooding ahouse is acceptable once or twice though a third time is probablyunacceptable particularly if with sewage.

• Another example of a barrier to smart water network technology• Another example of a barrier to smart water network technologyadoption is “we’ve got 100 men out looking for leaks every day” .Though it is unclear what that means in terms of effectiveness itnonetheless sounds like action is being taken, so relieving pressure.

• Perhaps the most important barrier is that there is not always thePerhaps the most important barrier is that there is not always thedetailed understanding of the costs of events. Whilst it is knownthat a burst may have necessitated the payment of £x million incompensation there is less clarity about the related costs due toincreased call centre volumes, management time in reroutingsupplies, dealing with the press, managing the emergency responseteams etc.

• As seen by that, there is a gap– at a macro‐level it is understood that water is an

increasingly precious resource and that thepressures on supply are already increasing and arepressures on supply are already increasing and aregoing to keep increasing;

– but at a micro‐level, is “smart networkmanagement” seen as even having a major role inalleviating those pressures?

• Certainly it can have a major role and certainly progressis being made. Equally certainly, however, there remainsa long way to go.

• The consequent challenge is to bridge that gap and theway to do that is through data.

• In May 2011, the McKinsey Global Institute published adetailed report on the potential of “big data” and itsfindings were quite illuminating

– by their calculations, retailers could increaseyoperating margins by 60% by mining datacomprehensively (imagine that applied to thewater sector);

– European Governments could save $100bn inadministrationadministration.

• There are millions of sensors already deployed, with thenumber increasing by 30% per year.

• But what are the practical benefits of smarttechnologies? What are the real‐time real life benefitsto encourage water utilities to take a substantial stepforward and embrace smart network applications?

• An immediate benefit is transparency – in the publicsector, McKinsey found that some are spending up to20% of their time looking for data.

• Data also allows empirical evidence to be displaced bymore rigorous assessment, for direction not to be drivenby the opinion of the highest paid person in the room,the HiPPO, but instead by analytics and statisticalthe HiPPO, but instead by analytics and statisticalassessments; for innovation to based not on a hunch ora suspicion but on a foundation of hard facts.

• Data also allows for automation to be applied in place ofhuman decision making, be that in automaticallyspraying de‐icer on a bridge when the temperaturedrops or, as with our TrunkMinder technology,automatically detecting and locating leaks on majorpipelines to the nearest meter.pipelines to the nearest meter.

• In the oil sector, as an illustration of what is possible,using real‐time data to manage oil fields remotely hasled to a 25% reduction in operating and maintenancewaste.

• Data also facilitates operational benefits, for example byallowing staff to be better mapped to events, by directlyrelating operations to variable costs such as energy ‐activities that are dependent on real‐time data.

l f d l h f f h• In California and elsewhere a significant amount of theenergy used, one estimate is well above 10%, is spent onmoving water around the State. Consequently thesavings suggested by using smart water networktechnologies are not savings at the edges but substantialg g gsavings at the heart of utility operations.

• The following examples provide instances of directimpact on utilities in terms of the potential role of dataand smart water networks

– 60% of insurance claims for water utilities relateto water loss;h k b f d– when a trunk main burst on Oxford Street in

London it cost Thames Water over £4m; and– when a trunk main burst in the North of England,

the relevant water utility was at once a YouTubesensation and at once found its branding andsensation and at once found its branding andimage under considerable pressure.

• As relevantly, OFWAT, the UK regulator, has made it clear that itwill be placing an increasing focus on real‐time data ‐ a move thatother regulators are likely to follow. As a result, simply seekingapproval for CapEx will no longer be acceptable ‐ detailed datasubstantiation will be expected and even required.

• In the USA too, hundreds of millions of dollars of investment arerequired in the drinking water infrastructure but where should theutilities begin with that and, if it is not affordable, as is the case formany economies at the moment, what can be deferred and whatcannot be?

• If utilities do not know the answers to those questions, if they donot have the data, and if, rather than replacing pipelines, theutilities are not monitoring their critical pipelines on an informedbasis with technology like TrunkMinder – in that case all thatremains is guesswork and an example of “what you can’t measureremains is guesswork and an example of what you can t measureyou can’t manage”.

• Meanwhile, when major corporates like Nestle and Coca Cola areinvesting in water efficiency around the world and when waterscarcity is rising up the public consciousness, basing major decisions

f d ll b l blon a paucity of data will be increasingly unacceptable.

• But that is not to say there are no practical examples yeteither – far from it.

– in Mission Springs California, reservoir controlsare being automated with consequent reductionsi d i i li bilitin energy use and increases in reliability;

– the Siemens wastewater plant in Warsaw allowsall plant information to be viewed from a centralcontrol room ‐ how many utilities actually havethat level of information visibility?

– in Atlanta, USA, automated data‐driventechnology has contributed to a 13% reduction inconsumption despite a 28% population growth;and

– at Thames Water software is being used toat Thames Water, software is being used toconverge telemetry data, historic rainfallinformation and weather forecasts to provide 6hour ahead projections allowing systems to bedrained down ahead of surges and pumping to beli d ith ithi d t i tialigned with within day energy cost variations.

• Also with Syrinix’s TrunkMinder technology deployed on larger diameter y gy p y gwater pipelines– utilities can receive automated leak detection and location to

within 1 metre – with immediate automated alerts when a leak isfound;

– utilities can receive fully automated burst alerts within seconds ofa burst occurring rather when the next 15 minute wake up cyclestarts on a more basic burst alert system;utilities can receive real‐time information every second of everyday….that you can access real‐time or on an historic basis.

• From a desk, a car, out in the field, anywhere with a web connection, it is possible to log on to the system and see for a pair of installed TrunkMinder p g y punits the real‐time pressure, the real‐time flow and the location of any tiny leaks.

• Imagine that ‐ information at ones fingertips, realising the benefits highlighted by McKinsey, making utility operation more efficient real‐time and opening the door toand opening the door to – fact‐based optimisation;– fact‐based issue identification; and– fact‐based decision making.

• But, whilst McKinsey might therefore give Syrinix a big tick. whilst Syrinix can provide that information on a real‐time and historic basis thecan provide that information on a real time and historic basis, the challenge posed by utilities remains “show me the money”.

• “Wont it be very expensive….surely nothing can beatthat old stalwart…the mahogany listening stick?” is aclaim often made in respect of deploying smart networktechnology.technology.

• In response to that, Syrinix has, in respect ofTrunkMinder, used an external consultant to interview anumber of utilities as well as using externally availabledatadata.

• In doing so, consideration has been given to the actualcosts of TrunkMinder equipment on an entire networkbasis and applying a variety of scenarios of leaks andbursts, as well as taking into account installation costsand the alternatives of physical surveys and leaklocation teams.

• The data substantiated result of that analysis is clearnamely that applying TrunkMinder in critical locationshas a payback period of less than three years and that isbefore account is taken of the impact of loweredinsurance premiums, the benefit of real‐time data forregulatory negotiations and the savings achievablethrough energy‐use optimisation.g gy p

• Indeed even extending TrunkMinder technology acrossan entire network, encompassing non‐critical trunkmains too, the expectation is that the payback periodwill not be significantly higher given those additionalbenefits.

• As has been seen then, barriers remain to deploying smart network technologies, barriers that are certainty understandable but which are also surmountable.

• In summary– the importance of water as a precious asset is

increasingly recognised at a macro‐level;– at a utility‐level, benefits are achievable in terms of

automation, transparency, innovation and operationalautomation, transparency, innovation and operationalbenefits;

– at a product‐specific level, using Syrinix’s ownTrunkMinder technology as an example, the benefitsare again clear not on a “we would say that” basis buton the basis of substantiated externally derived data.y

• So the message to water utilities about embracing smart network technology is  consequently clear ‐ give technology providers the opportunity and they will indeed show you the moneymoney.