Data analytics buying guide part 3: Where do you want analytics to take you?

Tools

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Elsewhere in this buying guide, we've explained:

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In the electric utility world, Big Data is a Big Deal. Attend just about any industry conference and you’ll hear folks talking about the data deluge swamping utilities as smart grid technologies like smart meters and sensors and Phasor Measurement Units (PMUs) go live.

 

And here's why it's such a big deal: Utilities are starting to use advanced analytics software to turn that data into actionable business intelligence. If market forecasters have it right, the industry is just getting started down that path. Utility spend on data analytics – less than half a billion dollars in North American in 2011 – is expected to swell 29% a year to $2 billion in 2016, according to the Utility Analytics Institute. Worldwide, Pike Research expects cumulative spending on smart grid data analytics to total over $34 billion by 2020.

 

So if exploring data analytics options is on your agenda, this five-part buying guide – created with valuable input from analytics experts at Origin, BRIDGE Energy Group, Space-Time Insight and Opower â€“ is designed to help you get started and know the right questions to ask.

·         What Big Data means for utilities

·         How to prepare for analytics

·         How to manage the procurement process

·         How to measure your progress

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In this article, we will explain an important but often overlooked aspect -- namely, the need to carefully plot out your destination before you start on the journey.

 

Not every utility arrives at the analytics doorstep with the same intentions. Some may be looking for customer analytics, others want to start with grid analytics and still others may be interested in meter data analytics.  We asked the experts at BRIDGE Energy Group to provide a glimpse at some of the parameters involved in all three to help you develop your analytics roadmap.

 

CUSTOMER ANALYTICS

 

How do advanced analytics enable better customer engagement?

Once the data is available, utilities can (to name just one example) let users compare themselves to others with similar-sized homes in the same area. That is, they can (anonymously) share consumption with others in zip code/geographic location and offer incentives or rewards for reduction in usage.  They can also recommend potential energy efficiency gains. Or they can use carefully targeted campaigns to  educate the customer on ways to lower their monthly bills through demand side management.

 

With targeting, you send each customer recommendations that are customized for their particular needs and preferences. Some large utilities can save millions of dollars in marketing costs each year by sending offers only to those likely to want them, rather than the "shotgun" approach of sending every offer to every customer.

 

What should utilities ask for if they want analytics to optimize the cash flow, including billing, collections, and low-income customer programs?

Utilities should consider developing a scoring process based on something other than the credit agency score -- credit score is not always an indicator of a customer’s ability to pay.  If utilities had the ability to conduct additional customer profiling, they would get a more accurate sense of credit history.  Once a more accurate profile is established, the utilities could ask for additional deposit money or conduct an additional background check to avoid theft cases.

 

As part of a fraud detection program, utilities could leverage smart grid/AMI data for trapping immediate theft cases or look at many years of usage data for existing customers and identify exceptions.  This may help to mitigate the increase in number of commercial and residential customers that understand how to install a jumper device and reduce monthly usage according to the meter reading.

 

What should utilities ask for if they are interested in segmenting customers into discrete groups with similar characteristics to more effectively serve and interact with those groups?

To segment customers, utilities may need to purchase data from several vendors that focus on effective marketing solutions in order to develop quartiles of C&I and residential segments.  Then they could research and understand each quartile’s opportunity. 

 

GRID ANALYTICS

 

What should utilities look for in situational intelligence tools to obtain unified operational views that span systems, disciplines and geographies?

Utilities should look for ease of implementation, including customization capabilities; an open interface for connecting common CIM messages including near real-time data streams; and the availability of skilled/experience resources with the product.  Utilities should also seek to answer questions such as whether the product will require an analytics engine and a columnar database for analytics; if the product can support event playback; and if the product has “what if” capabilities (example: what if we balanced out our overloaded circuits, what would be the impact?).

 

What should utilities seek in real-time operational analysis and optimization for distribution?

Utilities should seek asset maintenance windows and prioritization of asset maintenance based on equipment optimization.  They can then predict maintenance plans by understanding equipment health.  They should also seek to avoid unplanned outages; provide feedback to transmission to avoid transmission congestion; leverage automated decision/event management and employ “what if” capabilities. 

 

What should utilities seek if they want analytics to help with distributed resources including distributed generation, storage and electric vehicles?

First they need to seek out business opportunities that provide clear value and ROI.  They can partner with the business to develop an analytics roadmap for where they ultimately want to be in the “end state.”  It takes time to adapt to analytics with so much data available.  To start, they could identify an opportunity that could be used as a pilot and demonstrate the value of running on analytics. 

 

What should utilities seek to achieve in asset optimization analytics that improve the performance and reliability of grid assets?

The following underlying systems must be in place first:

·         OMS, DMS, GIS

·         Information strategy

·         High velocity, high volume, high volatility data integration capabilities

·         Analytical engine, columnar database – GreenPlum, Vertica, Hadoop, etc

·         Data visualization software

 

METER DATA ANALYTICS

 

What should utilities ask for to make best use of their meter data? How can it best be used over and above the meter-to-cash billing process.

To make the best use of their meter data, utilities should further implement fraud detection revenue recovery, theft prevention, demand reduction strategies, energy efficiency marketing program creation/management, energy efficiency services and asset optimization.

 

Now that you've seen some of the places analytics can take you, our analytics experts will share some tips to traverse the procurement process.

ADDITIONAL RESOURCES

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From industry sources…

2012 Utility Industry Survey on BI, Analytics and Big Data– BRIDGE Energy Group

California ISO: Bringing State-of-the-art to California's Grid– Space-Time Insight

An Introduction to Situational Intelligence– Space-Time Insight

Managing big data for smart grids and smart meters- IBM

Data validation for smart grid analytics– Utilicase

6 Big Data Lies– InformationWeek

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From Smart Grid News…

Strategic Asset Management: A Primer for Electric Utilities

Everybody's talking Big Data, but Echelon gets to the point

Smart grid data analytics: Looming gap will force major expenditures

Smart grid data analytics in the real world

Smart grid Big Data: Survey confirms utility opportunities, but spots blunders as well

Top 5 signs you need Operational Intelligence for smart grids

Just 3 simple steps to drive business value from smart grid data?

Smart grid analytics: Why you should fire your coders (and hire solvers instead)

Smart meters and Big Data: A clear case for governance best practices