Using the Environmental Management System Framework to Approach Data Management
Published on 30 Apr 2019
A key component of a comprehensive Environmental Management System is an ongoing data collection and monitoring. Data is used to inform progress towards goals, identify trends, perform analysis and conduct sustainability planning and budgeting.
As useful as data can be, it can be equally cumbersome to
collect and process. To properly manage data and ensure its highest level of
quality, a process should be developed. The ISO 14001 framework for
Environmental Management Systems follows a Plan-Do-Check-Act cycle and can also
serve as a guide to data management, as outlined below.
The types of data that can be collected within this
framework are either qualitative or quantitative. Qualitative is also referred
to as dimensions or categories and can include metrics such as initiatives,
certifications, or geospatial coordinates. Quantitative data includes
indicators, such as energy consumption or greenhouse gas emissions. As a
general rule, if the data can be summed, it is quantitative data, otherwise it
is qualitative.
PLAN.
Before beginning
data collection, a comprehensive strategy should be designed to ensure a
thorough approach. The bulk of the work is done during this phase. Begin by taking
an inventory of all qualitative and quantitative metrics that will be collected
and tracked. For each of these metrics, consider the following:
Source of the metric. Determine the system and
owner of the data.
Frequency of updates. The more regularly the
data is refreshed, the greater the ability to update. This may also require
increased storage capacity.
Consistency amongst metrics. Although individual
assets may use different systems, data should be converted to the same unit and
scale to allow aggregation for a complete portfolio dataset.
Amount of manipulation required. If data
requires calculations or conversions, this can create a potential opportunity
for errors.
Data quality. Consider the integrity of the
rawest form of data available to determine quality.
The last point is the most important. Ultimately, the
quality of reporting and calculations is dependent on the quality of the raw
material. If your input data is inaccurate, obsolete or incorrect, so will be
your output.
DO.
Several approaches to data collection exist and the best
option will depend on the data management system’s architecture and
capabilities. One option is to manually extract data by exporting each source
as many times as necessary, then store in Excel files. This requires a
continuous time input to extract, store and version every metric.
An alternative is to design a data management system that
will collect data automatically, by creating and scheduling data flows between source
and storage. This type of system is typically administered by a third-party.
CHECK.
Monitor the project advancement at regular intervals to verify
data quality. Data quality can be checked against previous time intervals or
industry norms. If anomalies are identified, revisit the process of data
collection and raw source of data to ensure that any issue is systematically
corrected to avoid further data quality problems.
An additional layer of data quality check or assurance can
be conducted by a third-party. This is referred to as data verification or data
assurance. A third-party who is not involved in the data management process
should conduct data verification.
ACT.
At this point, data is collected, cleaned and ready for
exploration. Utilize a reporting system to analyze and interpret the data.
Reports should be designed for actionable decisions and meaningful insights and
should be delivered to internal and external stakeholders who can use the data
to drive change, improve performance, and gain insight. Reports should offer
broader context and be provided at regular intervals to accurately measure
impact.
CodeGreen Solutions, Inc.was founded in 2006 in Manhattan and has provided sustainable building solutions to a total of over 250 million square feet nationwide. CodeGreen is an employee owned and privately held company. CodeGreen establishes long-term relationships with building owners, managers, operators, and occupants to develop innovative and creative solutions to achieve sustainability goals, reduce energy costs and overall environmental footprint. CodeGreen assists building owners to maximize project profitability through the sourcing of available federal and state financial incentives for sustainable and energy efficient initiatives.
This article is written by Raphael Lalou, Director, Data Practice; and Karen Mahrous, Associate Director of Corporate Sustainability, CodeGreen Solutions
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