Navigant Research Blog

C3 Wins Big with Italy’s Enel

— October 14, 2014

While the details are slim, C3 Energy has revealed that it has been selected for a $64.4 million deal to provide professional support services and software as a service (SaaS) solutions to Italy’s largest electric utility, Enel.  Of that sum, $18.3 million will be subcontracted, leaving about $46 million in deal value for C3.  The news was published in the EU’s Tenders Electronic Daily (TED).

C3 is a Redwood City, California-based utility analytics solutions provider started by Tom Siebel, founder of Siebel Systems.  Enel has some 32 million meters, implying a deal value of around $2 per endpoint, assuming the company’s entire grid is included.

The news follows on C3’s May win with Baltimore Gas and Electric, which is deploying the company’s revenue protection and advanced metering infrastructure (AMI) operations analytics solutions across its 2 million meters.  In June, Northeast Utilities contracted with C3 to deploy its Energy Customer Analytics platform.  The customer engagement platform will inform customers about the specifics of their energy use and provide custom recommendations for energy conservation.  Northeast Utilities has 3.6 million customers across Connecticut, Massachusetts, and New Hampshire.

Second Act

The Enel deal, however, dwarfs these stateside contracts, and should go a long way toward establishing C3 in the European utility market.  Enel’s was the first major nationwide AMI program worldwide; the rollout began in 2001 and deployment was completed in 2011.  This new contract provides further validation of the C3 solution, which was only launched in 2009.

C3’s solution is notable for its SaaS model, which hasn’t been fully embraced by utilities.  Over the past year or so, however, the model has been adopted by a number of big utility vendors, such as Itron, with its Itron TOTAL solution.  AT&T is also promoting a managed service offering for AMI.  C3 says that utility silos are breaking down and that its solution’s machine-to-machine learning capabilities are ideal for applications like predictive maintenance.  The company also offers analytics solutions for asset management, cyber security, and demand response, among others.

Perhaps the larger lesson from the Enel news is that Tom Siebel – who became a billionaire after selling his Siebel Systems to Oracle – is no one-hit wonder.  Anyone who can survive being stomped and gored by an elephant is not to be taken lightly.

 

CGI Wins U.K. Data Services Deal

— August 14, 2013

There’s a line in Driving Miss Daisy where Idella says to Hoke, “I wouldn’t be in your shoes if the Sweet Lord Jesus his-self came down and asked me.”   That’s about how I view the data service provider (DSP) role in Great Britain’s nationwide smart metering project.  This morning, the U.K. Department of Energy and Climate Change (DECC) announced that its preferred vendor for the DSP role is CGI, which acquired bidder Logica last year – that is, during the bidding process.  The DECC vendor selections have a strong Buy British flavor – which is logical considering the funding source.

The DSP will be massive and complex, given Britain’s unique approach to smart metering.  Unlike most countries, where the distribution network operators own and operate the smart meters, Britain has chosen to make energy retailers accountable for smart meter operations.  Given the deregulated retail environment, this has the potential to bring serious chaos.

One bidder told me that, while many customers never change their energy supplier, there is a subset of consumers that change energy suppliers up to 5 times per year.  This does not mean 5 meter swap-outs per year.  Rather, meter management specialists will install and operate meters on behalf of the energy suppliers, so the meter stays put while the customer switches retailers.  However, the use cases regarding delivery of consumption data to the appropriate energy retailers can become complex.

Mind Boggling

Perhaps more challenging will be the ability to run energy conservation programs such as demand response.  The permutations boggle the mind.  Will all energy retailers offer the same dynamic rate programs?  That’s hard to believe, given the potential of such offerings as a competitive differentiator among retailers.  And when a consumer swaps from one retailer to another, will that consumer be automatically enrolled in the new retailer’s dynamic rates program – will the opt-in carry over from one service contract to another?  The legal issues surrounding change of retailer could prove challenging.

None of those problems are newly created by this morning’s vendor announcements.  And the bidders – including CGI – will understand the use cases as well as anyone.   This does not necessarily mean that all the use cases have been identified, given the creative nature of retail customers in general.  Still, this is an 8-year deal estimated at £75million ($116 million).  $14 million a year seems pretty cheap to manage this potential chaos.

One wonders if some DECC administrators are walking around their Whitehall office this morning saying, “Bargain, innit?”

 

Data Analytics for the Distractible

— August 28, 2012

The massive rivers of data streaming off of the smart grid can be used for multiple purposes.  They can lead to more effective business, customer, and operational decision-making.  But information graphics are often misused by visual enthusiasts who combine complex data with ornamentation.   If those tendencies penetrate the utility industry, we may be in for infrastructure challenges of epic proportions.

In his seminal work, The Visual Display of Quantitative Information, Edward Tufte described what he called “chartjunk”:

The interior decoration of graphics generates a lot of ink that does not tell the viewer anything new.  The purpose of decoration varies — to make the graphic appear more scientific and precise, to enliven the display, to give the designer an opportunity to exercise artistic skills.  Regardless of its cause, it is all non-data-ink or redundant data-ink, and it is often “chartjunk.”

The biggest danger with chartjunk is not only that it is often downright silly, but that what is trying to be informative may instead be misleading or totally devoid of meaning.

In an earlier blog on SmartGridNews.com, titled “Smart grid data analytics in the real world,” I talked about the dangers for operational decision-making and fatigue if mental models don’t align with the information that is being conveyed.  But smart grid data analytics holds hazards for business stakeholders as well.  In a related example, the Harvard Business Review recently described marketing-oriented “data hounds” as dangerously distractible.

Helping consumers save energy, with targeted programs based on consumption information combined with detailed marketing information, could be among the biggest wins for utilities in driving ROI from their smart meter investments.  With consumer behavior changing so quickly, though, manipulating dials and chasing bright shiny lights could divert utilities from their key strategic goals.   The analyst who buries her head in a data dashboard is likely to miss the big picture, erratically changing direction and wreaking havoc in the organization by zigzagging from decision point to decision point.

Utilities have new opportunities to use advanced analytics to help secure our energy supply by personalizing the delivery of energy.  But it’s critical to remember that the people who use the systems matter as much, if not more, than the systems themselves.  New opportunities in data analysis will drive better decision making, but the business goals must be paramount.  In fact, most program managers will likely underuse data in creating new programs in the initial stages of data availability.  Utilities looking to improve their data analytics capabilities are advised to consider that top performers are those who not only own a statistics book, but are able to filter out the noise that might prevent them from accomplishing their strategic goals.

 

Turning Point for MDM

— July 30, 2012

“When you come to a fork in the road, take it.”

That sublime Yogi-ism mirrors where meter data management (MDM) vendors find themselves.  Will MDM become the data analytics of choice for utilities, or will it become a piece of critical yet mundane middleware, relegated to managing meters?  And will MDM vendors have any say in their destiny?  Understandably, they would like to believe that they do.  So they are not hanging about, waiting to see what happens.

One year ago, Pike Research published its report, Meter Data Management.  In researching the forthcoming 2012 version of this report, I’ve found quite a bit of movement since last year – much of it aimed toward the vendors’ continued existence.  The two most obvious trends are:

  • Acquisition: Nearly all of the major MDM vendors have been acquired.  Headlines include Siemens acquiring eMeter and Landis+Gyr acquiring Ecologic Analytics.
  • Data Analytics: Everywhere one looks, MDM vendors have repositioned themselves as data analytics vendors.  Some have stopped talking about MDM and instead offer MDA – Meter Data Analytics.  A year ago this was a green shoot; now it is in full bloom.

This near-unanimous repositioning of MDM vendors as data analytics vendors is understandable.  My favorite hype indicator is webinar announcements.  Today nearly every webinar invite talks about some version of “unlocking value through data analytics.”  If you’re an MDM vendor and your alternatives are to stay the course or to surf the analytics wave, the decision is obvious.

Unfortunately, the path from transaction-based MDM to analytics engine is much less obvious.  In extreme situations it may not be possible at all.  My colleague Carol Stimmel has recently blogged that utilities may make use of a great deal of data – some quite lacking in structure – to understand and influence markets.  (For more detail see her forthcoming report, Smart Grid Data Analytics).  It’s tough to imagine how an MDM system could digest unstructured data such as demographics and weather.

On the other hand, there is a middle ground where clever analysis creatively correlates transactional data only.  Survey results often do this.  Either MDM or an analytics engine could make those correlations.  But which will prevail?  I suspect that MDM will get those tasks, for two obvious reasons.  First, utilities are notoriously conservative and are likely to prefer the existing known quantity called MDM to something new and unknown.  Second, using MDM requires no new, expensive, and possibly disruptive software deployments.  However, MDM vendors may overreach into the world of unstructured data analysis.  Such a move could consume enormous amounts of resources for a low probability of success.

No matter what happens, there are certain features of MDM that will remain indispensable.  Validation, estimation, and editing (VEE) is essential for creating a high quality system of record for metering data.  VEE must process complex business rules over a massive data set, quickly.  This is where the transactional orientation of MDM remains an asset.  Likewise, utilities depend upon MDM to create ever-more complex billing determinants.  Such computations are not appropriate for an analytical engine.  No matter how much turf analytics ultimately capture, MDM will remain essential at every utility.

Perhaps the most logical summary of where MDM will end up vis-à-vis analytics is “horses for courses.”  MDM systems do lots of things that analytics engines cannot do, and vice versa.   And regardless, MDM vendors have survived years of being thrown into AMI deals as a no-cost extra.  Fighting off upstart analytical engines should be child’s play!

 

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