The novelist William Gibson is widely credited for noting that “the future is here, it’s just not evenly distributed.” That was back in the 1990s.

Since then, changes in how things are distributed in our society have been cause for both hope and concern.

On the one hand, improvements in semiconductor technology for things like computer chips and solar panels have made ideas like “distributed computing” and “distributed energy” a practical reality. On the other hand, recent economic turmoil has brought America’s uneven distribution of wealth and income under scrutiny.

With both of those threads in mind, we thought it would be interesting to consider how evenly energy consumption is distributed, and what that means for the viability of energy efficiency as a distributed resource.

So we’ve drawn upon our vast dataset of US electricity consumption to examine some key distribution-related questions:

• Among American households, what’s the breakdown between high, medium, and low electricity users? 

• What drives differences in electricity consumption across households?

• Is electricity consumption as unequally distributed as the nation’s income?

• What does the nation’s electricity distribution suggest about how to go about saving energy at scale?

Starting with 25.8 million homes for which we had 2011 electricity usage information, we narrowed the dataset to 8.57 million American homes that we confirmed have natural-gas heating systems (the prevalent heating fuel across the US).  In this way, we could make an apples-to-apples statistical comparison of different homes’ energy use.

We discovered that the top 1% of homes consume a full four times more electricity than average. Still, residential electricity usage ends up being much more evenly distributed than income. We’ll draw upon a couple key economic principles to explain why that is, and describe how the shape of the nation’s electricity distribution is a critical consideration in any large-scale effort to make America more energy efficient.

The Top 1% of households (by usage) consume 4% of residential electricity

Put another way, for every unit of electricity the average American home consumes, the top 1% of homes (by usage) are consuming four units.  And the heaviest 10% percent of users are responsible for nearly a quarter of all residential electricity use.

What else does this mean?

• The top 1% of households (by usage) spend approximately $4,000 per year on electricity, while the average household’s yearly electric bill is around $1,000.

• Supplying electricity to each household in the top 1% entails greenhouse gas pollution from power plants equivalent to driving 5 gasoline-powered cars for a year. In comparison, the average household’s electric usage contributes pollution equivalent to 1.25 cars.

• 1 day of combined residential electricity usage across the top 1% of US households (comprising approximately 3.1 million people) is roughly equal to 1 year of total electricity consumption in the African country of Sierra Leone (a nation of 5.5 million people).

What’s driving the disparity in how Americans consume electricity?

Mega-Homes Mean Mega Usage (Usually)

Generally, the prime suspect in the search for causes of high energy use is large home size (i.e. square footage).

To investigate, we evaluated the relationship between electricity consumption and home size across more than 4.3 million residences in our dataset. We wanted to see how electricity usage of a mega home (i.e. among the largest 1% of homes) compares to that of an average-sized home in our dataset (approximately 1,600 square feet). 

Our findings reveal a marked difference: an average mega-home uses 2.5 times more electricity each year than a typical home.

In this light, the chart below displays a predictable correlation: the larger the home, the higher the electric bill.

But while a general correlation between home size and energy consumption makes intuitive sense (e.g. more space to cool, more rooms with TVs), a deeper examination of the data reveals a complication: there can be substantial variation in electricity use among homes that have the same square footage.

To see how this is true, take a look at the line graph below: it shows that among households of the same square footage, it is not uncommon for energy usage to vary by as much as six times. This wide degree of variation suggests that while home size can serve as a rough predictor for usage, other factors – such as income, occupancy, climate, construction features, and especially behavior – are also important drivers.

Electricity usage is much more equally distributed than income

We’ve seen that the top 1% of electricity users consume 4% of the nation’s residential electricity. How should we view this in relation to income distribution in the US, where the top 1% of households take home nearly 20% of national income?

We shouldn’t be super surprised that household electricity consumption is more equally distributed than income. That’s because as a family’s income increases, their electricity consumption is likely to grow less than proportionally.

The principle at work here is a straightforward concept from Economics 101, called “diminishing marginal utility.”  Basically, as we obtain more of a good, we value each additional unit less. For example, there’s a big difference between having no fridge and one fridge in a home. But there is much less incremental value of going from four fridges to five fridges.  In other words, people’s demand for electricity has its limits, even as their income may grow.

Another reason that the distribution of electricity is more equal than income is that, although wealthier Americans are likely to live in larger homes, they are also more able and likely to invest in energy-efficiency improvements like insulation and triple-pane windows.

A nifty way to compare the distribution of electricity and income is to use a statistical measure called a Gini coefficient (named after the Italian sociologist who created it), which is a number that ranges between zero and one. A Gini coefficient of 1 indicates a totally unequal situation (e.g. a single household using all the electricity in the country), whereas lower values (i.e. closer to 0) represent a more equal distribution of resources.

For instance, the Gini coefficient for land ownership in the Middle-Eastern country Qatar is equal to 0.9, as the Emir of Qatar owns almost all the land in that country. By contrast, land ownership in Norway has a Gini coefficient of 0.18, reflecting greater equality in the distribution of land there.

The Gini coefficient for income in the US is around 0.47.  Based on our dataset, the Gini for residential electricity consumption is 0.34 (see Methodology), further suggesting that it is much more equally distributed than income. This finding is consistent with other studies that have statistically examined the distribution of energy and utilities.

Why understanding energy distribution is important for realizing efficiency opportunities

We’ve seen that, at least when compared to income, residential electricity consumption in the US is relatively evenly distributed. The top 1% of US homes, although they use 4 times more electricity than average, only account for a sliver of overall national consumption.

Therein lies an important implication for how to go about reducing residential energy consumption: large-scale energy efficiency efforts (e.g. cutting energy waste in half by 2030) can’t exclusively focus on the very highest users, for the simple reason that such homes are in limited supply (e.g. only 4% of homes).

Instead, saving energy at scale requires a broad-based approach that works well for homes across the usage spectrum. And such approaches do exist, as evidenced by Opower’s own behavioral efficiency programs – which have enabled millions of households to save energy, regardless of their geographic location, home size, income segment, age, and initial level of consumption.

Energy efficiency initiatives that successfully reach large swathes of the population are likely to do more than save a lot of energy: they may also provide certain groups — such as seniors and low-income families — with much-needed relief from burdensome energy costs. For example, recent statistics show that elderly and needy American families routinely see 19-26% of their paycheck go toward utility bills, compared to just 4% for the median American household. This suggests that effective broad-based energy efficiency programs like Opower’s can be beneficial along multiple dimensions — environmental, social, and monetary. 

While there are differences in how American homes use energy, there are often similarities in their ability and reasons to use less.  To explore which savings opportunities are most relevant to you, your utility’s website or the new EnergySavers portal from the US Department of Energy are great places to start.  Because the future is here…and it’s full of potential for energy efficiency. 

Special thanks to David Moore, Jon Margolick, Chris Corcoran, Katie Dewitt, Jillian Cairns, Efrat Levush, Ashley Sudney, Tyler Curtis, and Arhan Gunel.

Follow @OpowerOutlier on Twitter

Appendix: Questions for the curious reader to consider

The foregoing analysis has left a few questions lingering in our heads:

1. Are we underestimating energy usage inequality by not accounting for multiple-home ownership?

We evaluated the distribution of electricity by treating every household as a distinct energy-consuming unit. Given that some families consume electricity across multiple homes, our analysis may be understating inequality. In some cases, multiple-home ownership may be nontrivial. The 2010 Census found that 3.5% of homes nationwide are for seasonal, recreational, or occasional use.  And in some states, that fraction exceeds 10%.

 2. Are the heaviest electricity users necessarily less energy efficient?

No. From an environmental and energy-efficiency perspective, high electricity consumption may not always be a bad thing. For example, a large multi-generational family living under one roof is likely to face a high electricity bill, but compared to a scenario where all family members live in separate homes, per capita energy consumption may be quite low.

Similarly, if you own a plug-in electric car, your annual electricity consumption will increase significantly.  Consider an all-electric Nissan Leaf vehicle driven 12,000 miles per year: at a fuel economy of 34 kilowatt-hours (kWh) per 100 miles, it will require 4,080 kWh of electric charging— increasing an average home’s annual electricity usage by 40-50%.  But, relative to driving an average gasoline-powered car, your environmental impact and overall energy costs will decrease.

3. How does the distribution of residential electricity compare to other measures of energy inequality?

To fully assess Americans’ relative energy and carbon footprints, it’s necessary to look beyond household electricity usage. A peek at the transportation sector suggests that Americans’ energy usage in the air and on the roads may be more unequal than in their homes.

For example, market research from the airline industry suggests that 17 million Americans (less than 6% of us) account for 58% of all flights taken by Americans.  And the energy-related carbon emissions from flying are disproportionately large: the global warming pollution from one round-trip flight between San Francisco and New York (for a single customer) is equivalent to ~1 month of an average home’s electricity use. A recent New York Times analysis suggests that if you take five long flights a year, they may well account for three-quarters of the total pollution you create.

Day-to-day, the energy consumed for getting around town may exhibit a similarly unequal distribution, especially through a suburban versus urban lens: the EPA has calculated that the transportation energy use of a household in a typical suburban area is more than double that of a household in a transit-accessible area.

Methodology:

For the purposes of comparability, we narrowed our dataset to 8.57 million homes that have natural-gas heating systems.  Analyzing gas-heat homes helps reduce the effect of exogenous/climate-related factors on our analysis.  For example, Minnesota’s winter is much colder than northern California’s winter, but our analysis is to a significant degree insulated from this variation because the homes we considered do their heating with natural gas rather than electricity. This approach is especially important because heating represents a large fraction (42%) of home energy use.

Estimates for annual electricity costs are based on the average 2011 US retail electricity rate of $0.118/kWh. Note that average annual US household consumption is estimated at 11,496 kWh.  Our average value (8,548 kWh) is lower largely because we have intentionally restricted our dataset to gas-heat households.

To compute a Gini coefficient for residential electricity consumption, we divided the 8.57 million households in our dataset into 100 groups of equal size, to determine percentiles of consumption. We computed each percentile group’s share of total electricity consumption, and then determined the cumulative share of consumption up to each percentile level. This data series allows for the construction of a Lorenz curve equation, L(X), which we integrated between 0 and 1 using a Riemann-sum approach across the 100 subintervals.

The resulting Gini coefficient (G) for residential electricity consumption, G = 1- 2 ∫ L(X) dx, was 0.34. This result parallels the 0.37 Gini coefficient for US residential electricity consumption computed by Jacobson, Milman, and Kammen (2004). It makes sense that our Gini coefficient is slightly lower (i.e. reflecting a more even distribution) than the literature’s existing estimate, as our analysis controls for heating type while Jacobson et al. appears not to.

Households in our dataset for this analysis are distributed across 23 states.  Although geographically diverse, this dataset is not a perfectly representative sample of American households. However, we are confident that it is the largest dataset ever analyzed for the purpose of examining the distribution of US residential energy consumption, and that our analytical results are validly indicative of a national phenomenon.

Sierra Leone’s national electricity consumption was 111,600 MWh in 2009. The average annual electricity usage of a top-1% household in our data set is 33,654 kWh. Extrapolative multiplication of this usage by 1,147,614 households (i.e. 1% of the US’ 114,761,359 households) yields 38,621,802 MWh/year, or (dividing by 365) 105,813 MWh per day. This amount of electricity is approximately equivalent to Sierra Leone’s total annual consumption across all sectors. 

The greenhouse gas pollution from one round-trip flight between San Francisco and New York is 675 kg CO2, according to the International Civil Aviation Organization’s carbon emissions calculator. 675 kg CO2 is tantamount to approximately 1 month of an average home’s electricity use, according to the EPA’s Greenhouse Gas Equivalencies Calculator.

Data Privacy: All data analyzed here are anonymous and treated in strict adherence to Opower’s Data Principles.

Author’s note: The analysis and commentary presented above solely reflect the views of the author(s) and do not reflect the views of Opower’s utility partners.

The 2012 Super Bowl was the most watched television broadcast in US history.  An estimated 111.3 million viewers tuned in – more than a third of the country’s population.

That’s a lot of TVs aglow at one time.  The amount of electricity collectively consumed by TVs during the big game is reckoned by General Electric to exceed 11 million kilowatt-hours – equivalent to the power generated by ten medium-sized coal-fired power plants over the course of the event.

But here’s the real kicker: all those TVs illuminated during the Super Bowl are actually a force for dramatically lower overall energy consumption.

Turning back the clock to February 5^th 2012, we analyzed electricity consumption data across 145,000 households on last year’s Super Bowl Sunday. And our analysis revealed a remarkably consistent pattern: when the game kicks off, electricity usage plummets.

Let’s examine the game-day details and explore how a major televised sporting event can affect home energy use…

When Super Bowl XLVI kicked off, home electricity usage dropped off

First, let’s take a look at game-day energy usage in the western part of the country, for which we analyzed data from 91,000 anonymous households.

The chart below shows how average residential electricity consumption on 2012’s Super Bowl Sunday (broken up into 15-minute intervals) differed from a typical midwinter Sunday. From morning through midafternoon, Super Bowl Sunday was a fairly normal energy day in the West.  Between 10am and 3:30pm, electricity usage was slightly above typical – possibly as party hosts were doing preparatory tasks like washing dishes, and soon-to-be party guests were firing up their ovens to bake jalapeño poppers. 

But then something special happened around 3:30pm PST: the Giants and Patriots ran onto the field at Indianapolis’ Lucas Oil Stadium. Immediately, electricity consumption began to tumble downwards. As the game’s suspense escalated, usage levels descended –dropping to around 7% below what is normal for a Sunday afternoon/evening.

During the halftime show, the game’s viewership surged to 114 million people (even higher than the 111.3 million that the game averaged), who were treated to performances by Madonna, the dance duo LMFAO, and the English singer M.I.A.

But something else also seemed to be M.I.A. during halftime: home electricity usage. It faded to a full 7.7% below typical levels.

And so while in the hours before the game, home electricity usage had been just slightly above (+2.4%) what is typical, things changed radically after kickoff. Over the course of the game, consumption decisively decreased to 5% below normal.

And usage remained well below average (-3.7%), even after the game was over.

Why do homes use significantly less electricity during the Super Bowl?

We think there are a couple key reasons for the sharp reduction in usage:

1) Many households are not using appliances other than their TV during the game. On a typical Sunday in the early evening, many people would have been at home using multiple different kinds of electricity-consuming appliances (e.g. laundry machine, kitchen appliances, vacuum cleaner, TV) around the house—as suggested below by the relatively high evening usage that normally takes place on a Sunday.

But the Super Bowl changes this pattern: it concentrates activity around the TV. With so many people glued to the couch during the game, fewer households are using electricity for cooking, cleaning, or anything else other than watching the tube. Abstaining from other forms of energy usage naturally causes game-time electricity consumption to decline below typical levels.

And what explains the persistence of below-average consumption levels even after the conclusion of the game? One factor (another is described below) may simply be sustained fixation on the TV, which continues to divert people away from their typical Sunday night uses of electricity.  For example, after the game ended in 2012, tens of millions Americans didn’t leave the couch: 37.6 million viewers stayed huddled around the TV to watch the season premiere of the musical reality-TV show “The Voice.”

2) Many people watch the game at houses of friends and family. For many fans, watching football is a community event — meaning that people leave their homes to watch the game with other fans. In 2011, an extensive poll by Nielsen found that 45% of Super Bowl viewers planned to watch the game with friends or relatives.

A mass movement toward collective TV-watching at friends’ houses (or at a bar) on a Sunday night will result in significantly lower-than-average electricity use. Just as carpooling reduces transportation energy use, gathering together to watch televised sports—let’s call it “TV-Pooling”—decreases home electricity use. Twenty people watching one large TV at a friend’s house requires much less energy than 20 people watching 20 TVs in their individual homes.

And because for West coast audiences the Super Bowl concludes fairly early (around 7pm), the communality of the game-watching is likely to segue into additional away-from-home social revelry that persists longer into the night…as is suggested by the sustained lower-than-average usage through the very end of Super Bowl Sunday.

The numbers and analysis above correspond specifically to the Western region, but what about elsewhere? To test whether the Super Bowl’s energy-saving effect truly represents a broader nationwide phenomenon, let’s check to see if the pattern we’ve identified also materializes on the other side of the country.

Chips and (Electricity Usage) Dips: across the country, home energy consumption declines during the Super Bowl

Now we know what to look for, as we shift our attention to 54,000 anonymous households located in the eastern part of the country…

When last year’s Super Bowl kicked off (at 6:30pm EST), did the region’s average home electricity usage substantially decrease like it did in the West?

Indeed it did: game-time consumption in the East dropped to as much as 5% below typical levels.

Up until game-time, usage in the Eastern region was slightly below normal — likely due to the relatively warm weather that day, which may have given people an incentive to spend more time outside.  But then the Giants and Patriots took the field in Indianapolis, and poof: electricity consumption decreased significantly. Each 15-minute interval of usage during the heart of the game was more anomalously low than any other interval that day.

Consolidating the 15-minute usage intervals into wider time periods, we see that home electricity usage during the game was, in total, nearly 4% lower than we would expect on a typical midwinter Sunday evening – and represented the day’s most significant deviation from normal.

The pronounced decline in game-time electricity usage in the East is, similar to what we saw in the West, most likely a consequence of two related behavioral phenomena: exclusive focus on the TV and communal game-watching.

Importantly, these two subconscious energy-saving actions take place during a time that is normally characterized by peak residential consumption — i.e. when people are typically at home doing their Sunday evening routine (cooking, washing, etc.).  It’s for this reason that the Super Bowl produces a prominent energy-conservation effect: relative to a run-of-the-mill midwinter Sunday evening, the Super Bowl catalyzes an extensive cutback in household energy use.

And why did eastern residential electricity consumption spike to above-average levels after the game, given that the entire rest of the day had been below average? Quite simply, the Super Bowl ends on the late side in the Eastern time zone. That means that immediately after the game, many people (at least those planning to report to work the next day) were probably returning home en masse from parties.  One can imagine that as they walked in their doors, they collectively flipped on the lights and other appliances. This, in turn, translated into an unusually above-average period of energy usage relative to a typical Sunday’s late night, when most people would have been winding down and going to bed.

Better together: TV-Pooling is good for the planet, the pocketbook, and civic life

We were struck by how the Super Bowl – which has for the last 3 years been the most watched television event in US history – appears to reduce home energy usage.

Mass TV watching traditionally conjures up images of enormous, electricity-gobbling 42-inch plasma screens.  But as it turns out, all these TVs are a force, at least over the course of a few hours, for decreased electricity consumption.

And we were especially intrigued by the energy-efficiency implications of a key factor behind the decrease: TV-Pooling.

Communal TV viewing may at first seem like a trivial concept, but its effect on a country’s residential energy consumption could be significant. Super Bowl XLVI demonstrated that when around one-third of Americans collectively watch a single 3.5-hour sporting event, the corresponding reduction in the nation’s daily energy bill can be upwards of $3.1 million. That’s a lot of guacamole. Replicate this phenomenon a couple times each month, and you’re potentially talking about some serious energy and cash savings.

In addition, TV-Pooling may do more than conserve energy: it may help strengthen our social bonds. In his influential book Bowling Alone, Harvard Professor Robert Putnam chronicles how Americans have become increasingly disconnected from their friends and neighbors in recent decades, replacing communal engagements with individualized entertainment. In Putnam’s analysis, television is among the culprits – and perhaps appropriately so. But, there is something different about how we watch major broadcasts like the Super Bowl.

Events like the Super Bowl are not just popular, they are social. And, as such, they may help, just a little, to unite neighbors, friends, and family – and help save the planet.

Special thanks to Ashley Sudney, Efrat Levush, Steven Blumenfeld, Nathan Srinivas, Emily Bailey, Elena Washington, Yoni Ben-Meshulam, David Moore, Katie DeWitt, Andrew Sharp, Nate Kaufman, Jordan Jakubovitz, Carly Baker, and Arkadi Gerney.

Follow @OpowerOutlier on Twitter

Methodology:

The aggregated data used for this analysis stems from two large regional samples (nwest = 91,355 households; neast = 53,574 households). Time-of-use consumption data in both cases are defined at an AMI granularity of 15 minute intervals.

Baseline electricity usage levels (used to evaluate the deviation in home electricity usage on Super Bowl Sunday, relative to a typical day) correspond to energy usage levels observed on other Sundays in January 2012 and February 2012 that exhibited similar regional weather to February 5, 2012.  Similarity in weather is based on comparable daily mean temperature and negligible-to-zero precipitation.  Each regional analysis is demarcated by a geographic area with maximum radius of 25 miles.  To reinforce the analytical comparability of daily weather conditions and the underlying electricity usage requirements associated with them, the analysis is explicitly restricted to homes that utilize gas heating systems. Meteorological data is sourced from Weather Underground.

Each 15-minute game-time usage interval exhibits a standard deviation of no more than 0.40 kWh. Accordingly, the computed interval differences from baseline usage are statistically significant at the 99% level, within an confidence interval of  +/- 0.005 kWh (equivalent to the amount of electricity consumed by a single 20-watt efficient light bulb during the interval considered).

Note that, in the western region case, the above-average usage levels observed during the pregame period are more than offset by the strongly below-average usage levels that register when the game starts. This suggests that Super Bowl Sunday does not simply reduce usage during a certain segment of day; it also, as a result of the exceptional game-time contraction in consumption, is an overall net reducer of daily residential energy use. This outcome holds true across both regional analyses.

Calculations:

TV power demand in terms of number of power plants: General Electric estimates that all TVs beaming the Super Bowl in the US collectively use 11,309,607 kWh over a 5 hour period, implying an instantaneous power demand of 2,262 MW.  Net capacity of an average coal-fired power plant in the US is 228 MW. Aggregate demand of 2,262 MW over the course of the event thus corresponds to approximately 10 average coal-fired power plants. This is a conservative assumption, as it does not reflect transmission and distribution losses, which are generally considered to be around 7%.

Game-time usage reduction > 3x the energy consumed by TVs watching it: Based on GE’s 5-hour TV energy consumption estimate of 11,309,607 kWh. Proportionality to our game-time-specific analysis suggests that all TVs watching the big game for 3.5 hours = (3.5/5) * 11,309,607 kWh = 7,916,725 kWh.  The larger of our two regional time-of-use evaluations suggest that average residential electricity usage falls to 5.0% below typical levels during the game. A given US household consumes an average of 11,496 kWh per year (i.e. 8,760 hours), and so for a 3.5 hour period is proportionally assumed to consume 11,496 kWh * (3.5/8,760) = 4.59 kWh.  Average per-household usage reduction during Super Bowl = 4.59 kWh * 5% game-time reduction = 0.23 kWh. Across 114.76 million households in the US, the specific game-time reduction nationwide sums to 26,355,811 kWh. This is approximately 3.3 times as much electricity as the 7,916,725 kWh consumed by TVs during the game. This is a conservative estimate, insofar as the Super Bowl takes place on a Sunday afternoon/evening, which is a relatively high-usage time of the week for the residential sector.

Game-time monetary savings almost equal to cost of a 30-second commercial: Specific game-time reduction sums to 26,355,811 kWh (see above). At a national average residential cost of electricity of $0.1187/kWh, the game-time energy cost savings is $3.13 million. 30-second spots during Super Bowl XLVI fetched $3.5 million.

Data Privacy: All data analyzed here are anonymous and treated in strict adherence to Opower’s Data Principles.

Author’s note: The analysis and commentary presented above solely reflect the views of the author(s) and do not reflect the views of Opower’s utility partners.

The amount of big data being analyzed across the world today…turns out to be quite small. At least compared to its potential.

That’s according to a report published last month by the International Data Corporation (IDC), which assessed the state of the “digital universe” – a measure of all the digital data created, replicated and consumed in a single year.

IDC reports that between now and 2020, the digital universe will double every two years, growing from 2.8 zettabytes to 40 zettabytes (i.e. 40 trillion gigabytes) of data. Emerging markets’ share of the digital universe will grow to about two-thirds by that time (much of it from China), compared to one-third today.

The report also estimates that 23% of the digital universe has the potential for generating valuable insights. But that estimate is hypothetical: it assumes a scenario wherein all data is properly organized and classified. In fact, IDC considers only 3% of the digital universe to be adequately characterized, or “tagged.” And only a fraction of that tagged data is ultimately analyzed — in the end representing just 0.5% of the world’s supply of digital data.

In particular, the recent advent of smart meters—which record energy usage every hour, and in some cases every 15 minutes—has brought about an unprecedented proliferation of energy data.  One in three households in the US now have a smart meter.  While a traditional monthly meter generates only 12 data points per year, a modern meter measuring electricity consumption at 15-minute intervals generates more than 35,000 data points over the same time period.

Managing this massive boom in energy information is a lofty challenge, but it has enormous potential for good: real-time data offers significant benefits to utility companies (e.g. pinpointing outages and monitoring power quality) as well as customers (e.g. better understanding home energy usage patterns can empower customers to improve their energy efficiency and lower their bills).

As the IDC report suggests, the growing opportunity to find valuable insights in data — energy-related or otherwise — is intimately connected with our ability to manage large data streams, and rigorously apply privacy safeguards in data collection and storage. To learn more about energy data’s growing role in the digital universe and to get one perspective on how an energy company can incorporate best practices in data management and privacy, check out Opower’s Data Principles.

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