Data as Kryptonite
words “data quality” can strike fear in the heart of even the most astute sustainability
manager. Lurking below the surface of most data conversations is a murky mix of
questions about data reliability and coverage issues, accounting and normalization
methods, exceptional circumstances, and the nuances of different asset types.
may be kryptonite to many, but it’s essential for all. Good data—data that is accurate,
comparable and easy to understand—allows sustainability managers to evaluate
performance and provides incisive insights to help asset managers make informed
team supports asset owners and managers—collectively responsible for assets
well in excess of $200 billion—tackle their data woes head on and report with
confidence. While the road to data bliss is never easy, we guarantee that these
seven steps will make the journey smoother.
Step 1: Develop a data management plan.
data blunder we see most often is not having a data management plan. A data
management plan sets out the rules and procedures to quantify performance and
address change in the portfolio over time. Without a plan, performance analyses
quickly become inconsistent.
the plan outlines the scope of the analysis and how to handle a variety of
assumptions and circumstances, like exceptional uses and data coverage gaps. For
example, the plan will stipulate which emission factors to use each year, how
to decide which properties are included or excluded in the analysis (e.g.
acquisitions, dispositions and development activities) and what procedures to
use when estimating data. A plan is therefore an indispensable resource when
you seek verification.
Step 2: Invest in a data collection
and management system.
is no one-size-fits-all tool. Whether you use a commercially available product
or an in-house solution will depend on your portfolio’s size and heterogeneity,
as well as your analytical needs.
off-the-shelf solutions can be customized to improve usability and handle more
advanced automation and complex analytics, this requires a significant time investment
up front. Be aware that implementation can easily take double the predicted
time, and make sure the contract includes clear parameters for data completeness
and quality control.
in place, get to know your system – understand what it can and can’t do. Involve
all users in the learning process, and provide training, especially to those
who will be responsible for checking and following up on data issues.
solution you choose, know that your requirements will evolve over time. Plan to
retool the system every three years to tailor it to your developing needs.
we wholeheartedly recommend using a data management system, remember that it is
not a panacea for all your data woes. There is no substitute for strong,
informed sustainability management.
Step 3: Validate your data with some simple
our experience, two simple checks reveal the majority of data issues:
intensities and year-over-year changes.
example, if your property displays an energy intensity of 0.2 or 200 ekWh/sf, the
data probably contains errors. In most cases, either energy use data is wrong,
or the area associated with the data is misaligned. Similarly, if you compare
two years of data and the year-over-year consumption patterns have changed by +10%,
we recommend investigating further.
Step 4: Follow-up on issues and
Make sure you have a process and enough time to follow up on data issues,
anomalies and outliers. Depending on the issue, property
management teams or data management system providers can offer the answers
you’re looking for.
move on until you have a satisfactory explanation and have addressed the issue
at the source. Untangling data knots requires effort, but take heart —over
time, you will definitely face fewer surprises.
Step 5: Analyze your data to gain insight.
gain deeper insights into your data, you need to analyze it. Use graphs to visually
plot trends, distribution patterns, averages and variances and glean quick
Look out for regional differences and seasonal
fluctuations. Is the data acting as you expected? If not, why? Digging deeper will
help uncover remaining issues and isolate the story that the data tells.
Step 6: Be transparent.
describe what the data represents and your calculation methodologies. This
should include the scope and boundary of what data is included or excluded, the
proportion of actual versus estimated data, and if the data is normalized.
stating what figures represent, there is no peer comparability.
Step 7: Verify!
you are comfortable with the quality of your data, assess whether or not you
are ready to have it verified. While investors see verification as a signal of
quality, there is benefit too for sustainability managers. Subjecting your data
and processes to the rigours of professional verification is an excellent way
to validate your methods and focus your improvement efforts.
Rome wasn’t built in a day
the quality of your data won’t happen overnight. Achieving investment-grade data
can easily take two to three years of hard work. The key is to confidently move
through the process knowing that you are following best practice.
strive for perfection; instead aim for continuous improvement. Develop an
action plan to address performance, data gaps, and data collection issues.
Also, don’t be afraid to estimate, at least at first. You will reach a point of
diminishing returns if you insist on absolute accuracy.
remember that good data takes a village. Data bliss often hinges on your
partners so make sure you thank everyone for their collaboration.
road may be long, but the reward will be worth it.
Quinn & Partners supports leading
institutional investors, real estate and infrastructure companies with ESG
integration and GRESB assessment services. In 2018, the value of all Real
Estate and Infrastructure Assessments that the team submitted on behalf of its
clients was CAD 210 billion, which is equivalent to 10% of all North American
responses. Please reach out to Francisca Quinn, Managing Partner, Quinn &
Partners, at +1 416 300 8068 for more information.www.quinnandpartners.com