1. Understanding Your Impact

1.2 Planning & Collecting the Data

Currently viewing:  Refocus Home / Guidebook / Program Management Cycle / 1. Understanding Your Impact / 1.2 Planning & Collecting the Data

The next step involved in understanding the organization’s impact involves planning and collecting data. A data collection plan will serve as a road map for efficiently and consistently collecting data on the sources of impact that were included within scope. 

There are two competing factors that are key to building an effective data collection plan:

  1. Data Quality: refers to the usability and accuracy of the data collected. If the data collected is incomplete or inaccurate, or is without the context required to understand what it suggests, the program will suffer. It is also important to recognize how the timing of data collection affects the output of the results; for instance, collecting data on the number of hours lighting is turned on in an office would likely be different during times of year when there is more or less daylight available.
  1. Resource Intensity: refers to the amount of time, effort and money involved in collecting the data. Collecting higher quality data is likely to be more resource intense.

A balance between these two factors will need to be struck. The challenge is that it would be possible to invest unlimited resources into collecting ever-more-valuable data, but at some point, investing more produces diminishing returns and isn’t worth it.

Developing a Data Collection Plan

Collecting data is most effectively performed with the aid of a well-defined plan that can be repeated. As part of developing a solid plan, it is important to define exactly what steps need to be taken, at what time, and by whom. The more clearly we define the steps involved in collecting the data within scope, the more likely they are effectively executed each year, especially as employees come and go or change roles.  

The specific steps involved may or may not be straight forward. Here are three techniques that are commonly employed when the required data is not currently available:

  1. Converting Available Data: sometimes, the data is available but needs to be converted into a different metric or unit. In this case, any conversion necessary should be clearly defined for reference. For example, a carbon accounting system may require litres of gasoline consumed to calculate a carbon equivalent emission value, although records only capture amount of gas purchased in dollars. By obtaining the average gasoline price during the measurement period, the following conversion can be applied: 

Litres of Gas = Dollars Spent / Average Price Per Litre

  1. Adjusting Existing Processes: in certain cases, data is available, but not currently recorded. By adjusting a process, it may be possible to obtain the data with little difficulty. Consider a situation where the amount spent on paper usage is transcribed from invoices into an accounting system but the carbon accounting system requires the number of sheets of paper. The transcription process can be easily modified to include capturing the number of sheets purchased from the same invoice.

To learn about data boundaries in chapter 3 and 4 of the the GHGP click here  icon_library_16x16

  1. Creating New Data: in some cases, the data required is not available or reliable, and must be newly generated. There may be any number of ways to capture new information; being creative will be important toward balancing data quality with resource intensity. Here are a few ideas for collecting new data that may be useful:
  • Surveys / Interviews: directly asking questions of individuals can generate valuable data.  This option is best when a question(s) can be added to an existing survey or interview.
  • New Systems or Technologies: there are an incredible number of resources that can be used to automatically capture new information or to help manage and manipulate what is available to produce the data required.
  • Representative Sampling: in some cases, capturing a data sample and extrapolating it is a reliable practice that can be used in place of capturing a complete body of data.
  • Partnerships: an external supplier or stakeholder (or some other kind of partner) may be able to provide data already captured but not currently shared, sometimes even at no expense. That may demand putting a formal agreement in place to protect both parties.

Your data collection plan should include a clear process or step for collecting data on each source of impact included in scope so that it can be followed, repeated and adjusted over time. That will keep data collection efficient, consistent and manageable.

To learn about collecting GHG data specific to the service sector click here  icon_library_16x16

Collecting the Data

With a clear plan in place, the data collection itself can be relatively straightforward. Here are a few simple tips to remember as you get started:

  • Follow the Plan: it’s been well-designed, so try to implement it as it was intended.
  • Be Flexible: adjustments will likely be necessary; plans are never perfect.
  • Document Changes: any necessary deviations or workarounds should be recorded and used to adjust the plan for future years.
  • Be Sensitive: when engaging employees, remember they may not fully appreciate what’s going on and could feel threatened; be sure to use change management. In particular, you can help avoid issues by ensuring leadership is vocal in supporting the process and encouraging employees to comply.
  • Capture Relevant Data: the data points being captured may not, on their own, paint a clear or complete picture. Sometimes the situation or circumstances surrounding the data can tell us as much as the data itself and should be sought out. 

    The need for more than the planned data is especially apparent when looking at sources of impact that are part of complex and interacting systems.  A building, for example, is made up of a variety of elaborate and interdependent systems that are difficult to address without a deeper understanding of the relationship between each. In a case like that, systems thinking capacity can be quite invaluable to the data collection process.

The plan should eventually be improved and revised to address any inadequacies or opportunities for improvement identified while collecting data. It is likely there will be much more to change following the plan’s first use versus in subsequent years.

*Note: Inputting data related to the environment into the carbon accounting system selected in the Program Setup module will generate the organization’s carbon footprint.

Currently viewing:  Refocus Home / Guidebook / Program Management Cycle / 1. Understanding Your Impact / 1.2 Planning & Collecting the Data