Data, data everywhere.
The old saying — “you can’t see the forest for the trees” — resonates with energy and sustainability managers everywhere. The volume of data available to measure and assess performance seems limitless, and at times obscures the view of an organization’s long-term goals. Just the tip of the information iceberg includes:
- Invoice data, meter data
- Production data, occupancy
- Weather data, temperatures
- Flow rates
- Baselines, targets
Each stream has value, but using the data to effect significant change requires context. To that end, commercial and industrial leaders have developed a variety of best practices to extract the pearls from the sea of data. Examples include:
- Power usage effectiveness in data centers
- Energy intensity in buildings (energy/square feet) and industry (energy/unit produced)
- Chiller plant and boiler efficiency ratings for HVAC
These key performance indicators (KPIs) are based on standardized calculations using raw data. Some of them are fairly simple, while others combine many data streams, which are filtered through complex formulas into one useful output. The data sources themselves often come from disparate systems in many different formats. As a result, companies need a tool to help collect and synthesize the information, such as an onsite or enterprise management system. Powerful software is not enough, however. While it can pay incredible dividends, having to develop commands for every action is inefficient, and can lead to diminished value and missed opportunities.
Ensure the software understands the business.
It’s vital that the systems used for data collection, reporting and analysis are capable of implementing customized calculations. That’s the quickest path to actionable information without a lot of extra effort. Software that understands unique data types streamlines the process of pulling critical details from a mountain of data by identifying and removing options that don’t add value. Such intelligence would allow the system to show the square footage of a facility, for example, even when drilling into a chart of 15-minute data values.
The same system would, however, prevent a casual user from being misled by seeing a monthly production number extrapolated down into identical 15-minute production values for the entire month. Reading reports without the correct context leads to misleading information, which then leads to incorrect action.
Businesses understand their unique KPIs, but don’t want to wade through volumes of technical documentation to figure out how to implement them in their software platform. And that’s not necessary with intelligent software. It understands that a monthly “energy intensity of widget production” number is total energy use divided by the amount of widgets produced.
Conversely, when the price of energy fluctuates, the actual cost of consumption must be calculated at each interval and then summed up for the month. The order of operations for these two examples is subtly different, but a capable software system adapts and functions accordingly. Without that intelligence, the outputs of the system could be extremely confusing.
Prevent unreliable metrics, invest in intelligent software.
Deploying enterprise-wide energy and sustainability management software is a big decision; make sure to find a system that’s been designed to recognize and evaluate the data that matters. When software understands the data, it helps create meaningful KPIs and other calculations that let managers report on metrics critical to their business, without accidentally creating “false precision” or otherwise unreliable information.