Environmental Sustainability Program Manager
Analytics made easy
SAS Environmental Sustainability Program finds money-saving opportunities, improves corporate stewardship with SAS® Visual Analytics
A year ago, the manager of SAS’ Environmental Sustainability Program would have had little recourse if he thought a compensation check for warranty work on the solar farms came in a little short of expectations.
With SAS Visual Analytics, we’ve cut the time it takes to design reports by 75 to 85%.
The effort to dig through the data to prove the warranty miscalculations would have been too time-intensive. But when the estimate for lost energy was calculated last year, Manager Jerry Williams had just begun using visual analytics. He loaded the performance data into the tool, immediately saw a discrepancy, created a report and emailed it to the company. As a result, SAS increased the refund by 50 percent.
The Environmental Sustainability Program keeps SAS compliant with a growing number of environmental laws and regulations, such as the European Union’s Emissions Trading System, provides data to customers about SAS’ environmental practices, and looks for cost savings on SAS’ North Carolina campus and its facilities around the globe. It’s a data-intensive job, and before using visual analytics Williams and Sustainability Analyst Zane Lichtneger say they were mired in unwieldy spreadsheets and going back and forth with IT to create reports.
“With SAS Visual Analytics, we’ve cut the time it takes to design reports by 75 to 85 percent,” says Williams. He now has time to look at data in different ways, respond more quickly to requests from customers, find opportunities to increase operational efficiency and keep SAS green.
Williams uses SAS Visual Analytics to analyze operational data in key areas such as: energy and emissions, waste and recycling, water, green building, and procurement practices. Performance is determined by evaluating data against internal and external baselines, weather impacts, building square footage and space utilization, number of employees, and a host of other metrics. Williams particularly likes being able to see data anomalies that suggest easily fixable problems. He offered three examples.
Trouble with a time stamp. Looking at the data on the solar farms, Lichtneger discovered that one solar array appeared to be coming online a few hours later than the one right next to it. When performance data was summarized by day and housed on a spreadsheet, it wasn’t the kind of thing he would have noticed. In visual analytics, that minute-by-minute anomaly popped out. Turns out a time stamp was three hours off because the data was aggregated and reported using a west coast time stamp. That matters because when the arrays were evaluated head to head (they use independent monitoring systems), the incorrect time stamp would make one array look like an underperformer.
Predicting maintenance needs. The solar arrays rely on inverters to convert direct current (DC) electricity to alternating current (AC) and route electricity to the utility grid. Slight degradations in an inverter’s performance decreases the amount of electricity converted for public use. Because solar energy production is dependent on sunlight and variable throughout the day, it’s difficult to pinpoint the cause of power fluctuations. With visual analytics, detailed data can be visualized, allowing Williams and Lichtneger to catch anomalies suggestive of a malfunctioning inverter. “Now we can proactively go in and reset the inverter to better optimize energy production,” Williams says.
Improving data quality. Williams and Lichtneger aren’t just finding anomalies that point to problems – they also can quickly see if there is a problem with the data itself, and take action to improve the integrity of the data source. “It’s reassuring to know your data set is accurate and comprehensive. This helps us ask detailed questions and better understand the cost of action or inaction,” Lichtneger says.
With data exploration capabilities at their fingertips, both men have delved into additional projects. The visual analytics solution allows them to feed data on energy consumption, recycling and other environmental measures into executive dashboards and publically available dashboards for all SAS employees. “Increased transparency raises environmental awareness and improves employee collaboration,” Williams says. “This will help SAS save money while minimizing the anthropogenic impacts from our operations.”
Environmentally conscious customers want to do business with like-minded organizations. They often ask suppliers for environmental data and performance metrics. SAS Visual Analytics performance reports support SAS’ Annual Corporate Social Responsibility Report and numerous customer questionnaires and surveys to include the data-intensive Carbon Disclosure Project (CDP) Supplier Survey.
In addition, plans are underway to explore utility rates and usage on a building-to-building basis and to create a real-time energy dashboard that would show energy usage by building floor as it’s occurring. “We would like to create a bit of a competition for turning the lights out and recycling,” Williams says. With dynamic data becoming more readily available, opportunities continue to surface for increasing operational savings and demonstrating environmental leadership.
- Engage with data more easily.
- Eliminate need to use IT resources.
- Find errors and anomalies that cost time and money.
- Discovered warranty underpayment.
- Able to predict needed maintenance before solar panel efficiency degrades.
- Easy to use, and decreased time it takes to design reports by 75 to 85%.