“Big Data” is receiving a lot of attention. And for good reason. Nearly every process, transaction or interaction can now be digitized.  Computing power and sophisticated algorithms are detecting patterns in those aggregated data that were not possible even a few years ago.

Billions of data points from millions of people have high value. Companies like Facebook and Google have converted social media likes, internet searches, and cell phone tracking into marketing gold. The Internet of Things, Artificial Intelligence, and robotics are transforming the way services are provided and how products are manufactured and used.

The energy and water industry has been taking note. Along with commodity suppliers and government, utilities have focused on interval meter and sub-metering as a way of getting on the big data bandwagon. Interval data is being promoted to energy consumers as the big data tool that will deliver the success experienced in other sectors.

Interval data can play a tactical role for a business that wants to optimize production and energy efficiency in their operations. However, a pre-occupation with the big data success story can actually hamper effective action on energy when it distracts or prevents a business from grasping the strategic importance of small data.

Small data make energy use controllable.

Before energy consumers can appreciate the value of small data, they must confront a basic assumption. Most energy users assume that energy is not controllable; using energy is simply the cost of doing business. If energy can’t be controlled, it can’t be managed. If energy can’t be managed, there’s little point trying to use it more efficiently or cost effectively.

This thought process will always impede action on controlling energy input costs. Corporate leaders who hold these limiting beliefs will make no significant effort to change their organization’s internal processes or business plans. The initial challenge then, is to show senior managers how energy is controllable. They must come to see and understand the benefits that flow from managing energy. The information must be compelling enough, they are motivated to act. This is the strategic role that small data can play.

At this point, it is essential to note that the commonly held attitudes towards energy use also extend to the use of water. It is common for managers to view the water delivered to their buildings as an essential and necessary service. Little thought is given as to how water could be used more sparingly and productively. Attitudes are changing however, as concerns mount about our ability to enjoy safe, clean water into the future. It must be emphasized, that throughout this article, the references to energy serve also as a proxy for water.

Small data can be readily identified.

Most monthly utility bills are rich in data points, pertinent to specific sites. Energy consumers generally do not know or understand the value of these small data sets.  Nearly everyone thinks the utility bill is only useful as an invoice for payment to the energy provider; once the bill has been paid, its role has ended. Rarely is the other billing information seen as relevant for the entire organization. It is hard to reconcile the lowly utility bill as having power to unlock corporate profitability, efficiency and cost avoidance. That is the first task of small energy data analytics – to change the way senior managers perceive their utility bills.

There are at least four components to using small data in achieving positive energy management outcomes:

1. Analyze utility bill data for strategic purposes

Up to 20 separate factors contribute to utility costs. All of them can be analyzed and controlled. All of them can be understood by reference to monthly utility bills. For example:   What makes up the largest part of monthly utility costs? What are flat rate fees? Is the utility rate structure the right one, based on the site’s operating hours? How much of the monthly energy cost is impacted by peak demand charges?  What is the site’s power factor and is there a penalty for it? How much do utility costs vary and why do they vary? How have production, equipment or shift changes impacted monthly consumption?

Each factor on the bills can be tracked from month to month to identify variations.  How has energy and water consumption changed in the last three years? What patterns emerge? Are there unexpected charges that don’t fit the pattern? How do these insights suggest ways for costs to be controlled?

The answers to these queries can be surprising. A municipal energy manager once reported that it was only when they started looking closely at their utility bills did they discover their city had for many years, been erroneously paying for the lights at the airport runway!

Typically, most organizations that diligently review utility data realize 5 to 10% in savings and cost avoidance. Small energy data analysis can drive actions with paybacks of under a year. CEOs that were initially reluctant to embrace energy management do so, once they see the compelling business case for action.

2. Analyze interval energy data for tactical purposes

Monthly utility billing paints the corporate energy picture. That is the strategic view. The next use of small data is tactical.  Hourly use data comes from utility main service meters and is referred to as interval data. It is the record of how much energy is being used on an hourly or more frequent basis. Interval data is often tied to production and non-production operational conditions in a building or a plant. Utilities and service providers are increasingly making these data available to their larger customers via their website or by email.  From our experience, the more frequent the access to the data the better.

As the interval data is collected, reported and analyzed, what patterns emerge? For example, how much energy is being used during non-production times compared to energy used during production times? How does that compare to what is expected? Interval data is used to identify site specific anomalies. The data then guide the search to track down the causes.

Interval data is granular, a valuable attribute. Interval data can provide clues for reducing baseload or for flattening energy peak use. An organization can avoid huge cost premiums from utilities if they reduce energy consumption during periods when energy grids are strained. Many jurisdictions offer demand management programs to reward companies that reduce their energy consumption, especially at critical times. Access to hourly data is essential if a company wishes to successfully participate in these programs.

Interval data has other uses. An hourly energy profile can help determine the rate options or energy programs for which a company might qualify. This is helpful whether a facility is in a regulated or a deregulated market. In deregulated markets, hourly load profiles can identify and inform hedging options to best manage future procurement costs and mitigate risks. Interval data is useful for energy audit purposes.

Interval data may be small, but they can be mighty.

3. Make use of organizational knowledge and insights

Once an organization decides to query their utility bills, no one person can provide all the answers. A broad, diverse corporate team is assembled for input. Accounting and finance, executives, maintenance, and operations managers – all are needed. A strong internal energy team mandate is to obtain and review data for evidence based action. To be set up for success, the team has to receive timely energy information that is relevant to each person’s role in the company. The team also has to learn how to interpret that information. Organizations should consider drawing on external resources such as utility account representatives, energy suppliers or energy consultants who can supplement the knowledge and insight of the internal players.

The senior executive team sets out to discover what drives their energy and water costs. They gain insights into the company’s usage patterns from the monthly utility billing data.  Site specific teams on the other hand, primarily use interval data to map their site’s energy load by operating hours and by operating equipment. The load profile is created. Corporate targets and accountabilities are set. Priority projects are identified.

Teams start with easy changes like reducing idle run time on production equipment. They later move on to trickier projects.  A team learns not to address every energy issue in the first year. They learn to trust the data to guide them in making smart choices. This process truly is focused on small data. But the small data show the pathway to low cost, high payback initiatives.

4. Create a Small Data Analytics Plan

The corporate energy team discovers and interprets patterns in utility billing data sets. They then communicate their discoveries in ways that motivate commitment and action throughout their organization. Depending on the company size and the number of sites it operates, a variety of tools can support these tasks.

A well designed Excel spreadsheet might be sufficient for doing an energy analysis of a single site operation.  Energy management across multiple sites is more complex. A business with two or more sites may consider outsourcing this analytics service to one of many software houses in the marketplace. A diversity of choices is available. However, not all software packages are equivalent.

Among the tools on the market are those that:

• Pay utility bills with limited management reporting;
• Capture utility data inserted into the client’s energy management software;
• Capture utility data, provide measurement and verification of the data and generate management reports;
• Provide sector specific modules or programs, such as tenant billing for multi-unit building owners.

Many of these suppliers suggest their software provides analytics and reporting.  Analytics is only useful if it meets the needs of the organization. The energy software chosen must be able to meet the diverse requirements of senior management, accounting, operations, sustainability, engineering, maintenance, and purchasing.  A cross-functional approach to thinking and acting on energy is a relatively new concept. Quite frankly, most energy consumers have never really thought of managing energy in this way. Hiring an independent expert to assist may be well worth the investment.

Choosing the appropriate utility billing software is important, but that’s only the beginning. It will take time to install the software and get it running. Effort is required to capture historic as well as current billing information. Incorporating a training component will ensure departments and individuals can fully understand and apply what has been harvested from the energy data.

Curiosity Is the Strongest Analytics Tool

Data should never be taken at face value. Energy improvement teams should always be asking questions: Did consumption change over a multi-monthly period? Did two consecutive months of production use the same volumes of energy? Did consumption reflect what was expected? Was last winter really cold enough to have caused such an increase in gas use? Were variations attributable to production levels; number of shifts; external temperatures; other factors? These and many other questions spring from a desire to understand, to make meaning and continually improve.

Teams are required to take a deep dive into the factors that drive energy costs. A good starting place to find incremental opportunities for improvement is to always compare and contrast groups of small data. Team members must be truly curious. Curiosity is ultimately what drives them to understand whether energy performance is becoming more productive, despite multiple changing variables.

Small Data. Big Results.

A satisfactory energy management journey starts with senior management involvement and attention. Companies come to recognize that energy and water are controllable input costs. Identifying small energy data sets is fairly straight forward, yet strategically crucial. The data are found on the utility bills that are delivered monthly. The challenge is capturing, aggregating, analyzing and reporting the data. When energy information is fully shared and understood within an organization, small data will bring big results.

Big data has captured everyone’s attention. As a consequence, it is assumed the path to success requires companies to apply complicated, automated and expensive algorithms to large data sets. In regards to energy and water use, the potential for most companies lays waiting to be discovered in the opposite direction.