Eneco roy muller

Eneco’s Data Science programma: nieuws uit de front linieSeptember 2016

Roy Muller

Weer even ophalen van vorige keer..Wat betekent Big Data voor Eneco?

Eneco in een paar getallen

2,2 million customers in the Netherlands and Belgium

100 years of Energy Experience 230.000 Toon’s installed

Active in the Netherlands, Belgium, the UK, Germany and France

One of the cleanest energyenterprises in north-west Europe, and the first

energy company in the world to become a WWF Climate Savers Partner

Independent energy company with 53 shareholding municipalities

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Waar gaan en staan we voor?Duurzaam, Decentraal, Samen (since 2007)

We staan niet meer alleen in ons streven!Tijdsdruk en hectiek nemen toe

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Eneco strategie: geen route, maar een kompasBottom up ondernemerschap in goede banen geleid

Performance improvements

Asset management

Trading & Flex

Smart products and services

Customer Intelligence

Prachtig, maar hoe past Big Data hier in?

Stand van zakenHoe gaat het met Big Data binnen Eneco?

We have lift off!

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(From Board update, Jan 2015)The Data Science program followed an agile approach in the start-up phase, Aiming to quickly establish the basic necessities, insights and tractionHigh lights are summarized below:

Data Science ‘tribe’ expandedData Science started out by leaning on the available quants within Trading and I&V. This obviously limited available capacity. Therefore, we followed up by attracting top talent from the market through an event & network based recruitment strategy. Take away: we expected having trouble recruiting Data Scientists. This is not the case. Supply exceeds demand.

Innovation process organized: fast lane for Data Science createdWe now have a controlled & supported fast lane for Data Science based innovation within the Eneco group.An A-team of experts (X-BU) manage the funnel from opportunity idea selection exploration solution launch

Privacy assured (governance & infrastructure)

Data Lakes in place and being filled

Last but certainly not least: significant business results, program more than pays for it self** Mln of recurring PnL (** mln Trading, ** mln Network, ** mln Consumer), defensive estimatesA variety of other important milestones achieved (strategically relevant apps, features and models brought live).

Lessons learned (1) – think big, start small

Lessons learned (2) – business is leadingAt some point, local “Python and R-jungles” need to be organized and need for e.g. data lake becomes urgent

Source: CapGemini

Lessons learned (3) – beware of the hype

Gartner Hype Cycle - July 2016

Toon

Predictive: Toon® Ketel IQ24/7 boiler monitoring: carefree!

in 2 years systemlost 0.5 bar

in 3 years systemclose to critical

Key Benefit• Know directly what to do with mailfunctions

and maintenance

Simulation: InSolarGet the most out of your sun

Potential> 2,000,000 roofs < 50% monitored

Application iteratively developed with customers

Engine based on Sandia national Laboratories model

Machine Learning: Toon® Kachel CoachYour thermostat knows you better