sustainability measures: five essentials for effective data collection
TRANSCRIPT
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8/2/2019 Sustainability Measures: Five Essentials for Effective Data Collection
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Global CorporateConsultancy
2012 Global Corporate Consultancy l AnteaGroup USA, Inc.
April 2012
SUSTAINABILITY MEASURES
Five Essentials for Effective Data Collection
Anna Blitz, Global Corporate Consultancy, AnteaGroup
Collection Protocols
Routine Audits
Data Definitions
Built in Validation
Managing Change
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Globalization, unprecedented financial failures and
unsettling predictions about the future have many
contemplating what changes are necessary for societys
core institutions to grow, prosper and flourish over thelong term. Some would argue that part of the solution is
to rethink and redefine value, integrating both
environmental and social dimensions to the traditional
financial calculus that defines success.
Today, many companies seem to agree with this notion
and are in fact diligently working to characterize theirsocial and environmental performance. While there
may be differences of opinion as to precisely which
sustainability measures are most material or best at
delineating value, there is little debate on the essentials
of good data collection, especially among those
disclosing and using these new metrics and indicators in
ways similar to that of traditional financial measures.
This white paper shares the fundamental aspects of
what we have learned in managing sustainability data
for numerous corporations around the globe. While it is
impossible to include all our experience in this brief, we
have condensed those learnings considered most
important into the Five Essentials for effective
sustainability data collection. Our hope is that these
thoughts will serve as practical aids in the design andimplementation of systems to better characterize and
quantify the value social and environmental progress
measures can bring.
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THE FIVE ESSENTIAL ELEMENTS
At first glance, collecting sustainability data may appear to be a relatively
straightforward process. Consider something as simple as gathering and compiling
the total volume of water consumed at any given location. Oftentimes, localpersonnel quickly review a facilitys monthly water utility bill, determine the volume
purchased, and report a value to those interested in compiling this data. Sounds
simple - but what if it then became apparent that water from a nearby river had
been supplying the plants cooling and irrigation processes for years? Suddenly
results (thought to be correct) are deemed inaccurate by 25-30%, month after
month. Multiply similar omissions or errors across a wide variety of social and
environmental parameters, then again by tens or even hundreds of facilities or
products, and it becomes readily apparent why clear data collection practices are
essential. This is especially true when companies plan to use such metrics and/or
indicators in manners similar to traditional financial measures (e.g. to demonstrate
progress, or in support of significant corporate performance decisions).
To minimize the potential for such problems, leading organizations adopt complete
systems and processes designed to ensure data collection is as comprehensive and
as reliable as possible to support their key decisions. Our experience indicates that
the most advanced approaches have at least Five Essential elements or practices in
common. These include:
1. Clear Data Definitions & Boundaries specifying the scope, purpose,
exceptions and limits associated with the data being collected.
2. Defined Data Collection Protocols serving as a definitive description of how
information is to be gathered and compiled, explaining methods, frequency,
automation and documenting other important aspects in ways that simplify
and assist those responsible for collecting this data.
3. Built-In Validation integrating discrete data quality checks into collection
processes, verifying completeness, accuracy and plausibility at logical points
along the way.
4. Capabilities To Anticipate & Effectively Manage Change improving when
innovations reveal better data collection methods or adapting when changes at
a company warrant modification to current practices/processes.5. Routine Auditing & Assurance designed to assure transparency and validate
the data collection methods as well as the quantitative results of such efforts.
More details on each of these Essentials , along with illustrative examples, are
provided in the following sections.
Leading organizations adopt complete systems and
processes designed to ensuredata collection is ascomprehensive and reliable as
possible to support their key
decisions.
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1. CLEAR DATA DEFINITIONS & BOUNDARIES
Those companies most effective in collecting sustainability data have established
clear and concise definitions and boundaries for the information they want to
gather. While seemingly obvious, our experience has shown that manyorganizations neglect or even ignore this important aspect of data collection until
only after costly errors or omissions occur.
To avoid such pitfalls, ensure that, for each parameter to be tracked:
The scope of the collection effort is clearly defined;
The purpose of data collection is articulated to all those who will collect orutilize the information; and
Any exceptions are clearly noted, minimizing initial effort and subsequent
rework of the data.
Scope:
Defining the scope establishes the data collection boundary the information that
should be included as part of the collection process for this specific measure, as
well as those locations that are required to collect it (e.g. setting operational scope
boundaries to include all owned manufacturing operations and distribution centers
versus all operation system sites, including leased locations). When including
information beyond the four walls of your company, defining the scope to include
the specific components of the value chain is also necessary (e.g. emissionsassociated with packaging and ingredients from first tier suppliers).
Purpose:
Defining the purpose denotes why data should be collected and reported on a
particular measure and how the information will be utilized by the company. This
component of the definition details context and company expectations, thereby
encouraging those supplying information to ensure every submittal is correct,
accurate and complete. A purpose that is tied to performance or benchmarking
against particular goals or targets will incent consistent reporting more than a
purpose that is perceived to have no value to either those supplying the
information or staff using the outputs once compiled.
Exceptions:
Describing what is not included in the scope (i.e. exceptions) is just as important as
identifying what is included. By identifying and describing exceptions at the onset,
companies can minimize over or under estimations, as well as other errors that
could result in significant rework (and additional cost) down the line. Exceptions
are typically related to the boundary conditions of data to be reported, by including
Illustrative Example:Clear data definitions and boundaries have a scope,
purpose, and noted exceptions
SCOPE 3 E MISSIONS- EMISSIONFROM INGREDIENTS
SCOPE:Emissions associated with ingredients
used in our products shall becollected from first-tier suppliers.
Ingredient suppliers included in thisrequest are: grains, sugars, fruits,
and oils. First tier suppliers are thosethat provide materials directly to our
company for inclusion in our products.Emissions associated with the product
our company purchases shall becompiled by our supplier (pro-ratedbased on revenue) and delivered to
the Director of Sustainability forinclusion in our emissions database.
PURPOSE:This information is collected as part of
an ongoing effort to capture the fullimpact of our operations from farmto consumer. Ingredient emissions for
grains, sugars, fruits, and oilsrepresent the largest emissions
sources from our first tier suppliers.This information will be used to drive
performance improvements andemissions reductions throughout the
companys value chain.
EXCEPTIONS:Ingredients other than grains, sugars,
fruits, and oils, along with second- andthird-tier suppliers (our suppliers
suppliers) will not be considered at thistime. Additional ingredients and
suppliers will be considered if deemedmaterial to reporting efforts.
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or excluding specific operations (e.g. manufacturing, offices, or the like), or by
addressing unique situations that arise during data collection that may be out of an
organizations control (e.g. utilities included as a lease agreement).
By taking this first step and setting clear and concise definitions from the outside,
companies will have a well-defined foundation for their data collection process.
Evaluating the scope, purpose and exceptions during the continuous improvement
of the organizations sustainability program will result in more accurate results and
ensure a proactive, flexible approach that anticipates and accounts for variables,
rather than reacting to data issues down the road that could result in costly rework.
Leading companies, however, dont just stop there our experience shows that, to
ensure effective results, the best company systems carry this proactive mindset
throughout the data collection process, paying just as much attention to setting
forth expectations of how to collect the data and where to report it.
2. DEFINED DATA COLLECTION PROTOCOLS
Developing data collection protocols is often where most companies begin their
information collection processes. Successful companies have established clear and
straightforward data collection protocols that function both as a how-to guide and
as a basis to audit or assure the quality of information provided (as further
discussed below).
The protocol should include the following:
The methodology for gathering and reporting measures, as well as calculatingany metrics or indicators (as applicable), including conversions and, when
necessary, specifying appropriate compilation/consolidation methods for
multiple data sources (e.g. multiple metering devices, multiple facilities,
collections of products, etc.);
Frequency of reporting;
Where and for how long data should be retained; and
If necessary, any documentation required to support the information reported.
Methodology:
The methodology builds on data definitions previously specified during the first
essential collection step, describing how to gather the data to report any measures
requested, as well as detailing the calculations performed as part of the collection
and reporting process. In some cases, calculation may be required at the facility
prior to supplying information (in a database, spreadsheet, etc.) to those requesting
the data. For example, a facility may need to convert the collected data into a
Successful companies haveestablished clear and straightforward datacollection protocols that
function both as a how-toguide and as a basis to assurethe quality of information
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specific unit of measure prior to data entry into a web-based system.
Instructions for how to enter the information should also be included as part of the
methodology for a particular measure. Once the data is gathered, the methodology
should describe how to deliver and possibly retain measurements, calculations or
any other required supporting information related to collection, as well as the
documentation required for noting any limitations to data collection when data
cannot be gathered per the previously established definitions.
Frequency:
To assure your company has the flexibility needed to adapt to results from data
collected, the frequency of data collection must be defined for each measure:
Some measures may be best monitored daily and then compiled forreporting on a monthly or quarterly basis (e.g. production, energy
consumption, water usage, safety incidents, etc.). Some measures are better suited for monthly monitoring when invoices
are received (e.g. waste, employees, training, compliance, customer
satisfaction).
Yet, other measures may be more suited for infrequent or ad-hocreporting (e.g. refrigerant replacement, community investment initiatives,
employee satisfaction).
By collecting and analyzing data more frequently, a company can utilize the data to
manage sustainability efforts more effectively, accelerating impact to goals and
targets by identifying and implementing best practices from locations with positivetrends, and adapting or correcting practices at under-performing locations.
Collection System:
The data collection system utilized can be as simple as an excel spreadsheet
requiring manual input to a complex web-based tool with automated data entry
and reporting functions.
Utilizing a spreadsheet program may be sufficient for discrete tracking, and it is very
useful to provide a back-up during auditing; however, this system naturally
introduces the potential for human error.
A web-based tool may ease data entry and reporting requirements, reduce the
potential for human error and provide a platform to solicit information per the
definitions as well as preliminary validation during data entry to catch the easy to
correct errors immediately.
A partially automated collection system is the next step in data collection processes,
and, in many locales, will connect an organizations on-site utility metering directly
to the collection system - further reducing the level of effort required and
mitigating the potential for errors.
Illustrative Example:Data Collection Protocols ensure flexibility and clear parameters
for quality information
TOTAL WATER USAGE Total water usage data should be
collected by all facilities monthly andsubmitted to the online database
Gather the meter readings for allsources of water present at yourfacility pursuant to the previously
defined scope.Municipal Utility: water
provided by a local utility.Well/Borehole: An onsite water
source pumping water from a localaquifer. This water will may betreated prior to use on site.
Surface waters: include rivers,streams, lakes and other surfacewater sources. This water may betreated prior to use on site.
Collected rainwater: Watercollected as part of a storm watercollection system. This water mayonly be used for irrigation.
All entries shall be in either gallonsor kiloliters. If the meter at yourproperty reads data in a different
unit of measure, please refer to theunit of measure guide to convertyour data to one of the accepted
units prior to entry .
Monthly invoices from the waterutility should be attached to your
submission
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Documentation:
The documentation component of the data collection protocol is to ensure back-up
of the information is maintained, both during initial data entry and when/if
subsequent changes are made. Many data collection systems are developed withthe functionality to attach utility bills and manifests to document the values
reported.
In the course of our work, we have found that clients who have made the time
investment to establish a clearly defined data collection system, detailing the how-
to for data gathering and submission, tend to have fewer errors in their dataset
because they ensured that their system has the flexibility and efficiency needed to
change course mid-stream as necessary and maximize time investments. Successful
companies have a clear, concise methodology that takes a proactive approach
toward calculating the value of their sustainability efforts. To further reduce errors
in the data set, and to minimize time wasted on reviewing and correcting datahowever, forward-thinking companies invest in a built-in verification process
during data entry - the third Essential for Effective Data Collection.
3. BUILT-IN VALIDATION
We have found that a built-in validation process, activated when data is first
entered into a spreadsheet or an online system, improves efficiency by triggering
any necessary corrections at the time of initial data entry and by the individual who
is supplying (and best understands) the information. Data confidence is built on a
thorough review of the data, and leading companies have established automaticprotocols in their collection systems to evaluate completeness, accuracy and
plausibility of the reported data. All three processes are interconnected, but their
individual purposes are unique.
Completeness:
Completeness is one of the first validation checks routinely noted in highly effective
data collection methods/systems. This is a simple, but critical check to verify that
all required measures have been collected/supplied for the specified reporting
period. Missing data will bias results, and if omissions are significant enough,results may need to be restated at a future date. Restating results often triggers a
variety of additional concerns, especially if such information has been included in
external disclosures or reports. To prevent omissions, superior data
collection/management systems often automatically identify and alert personnel of
potentially missing information, allowing evaluation and correction early on and
thereby pre-empting any question as to the datas integrity.
Data confidence is built on athorough review of the data
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Plausibility:
Plausibility checks are also common in highly effective data collection processes.
This, too, is typically a simple process for most measures that evaluates whether
the data supplied seems plausible or realistic. Plausibility checks are typicallyestablished as thresholds where values beyond certain limits are considered
suspect, requiring review prior to proceeding in data collection or analysis
processes.
Accuracy:
Accuracy checks are also common in effective collection systems. These checks are
typically conducted by assessing the degree of difference between data submitted
and that which was previously accepted. Specifically, the most common types of
accuracy checks are:
Current period compared to prior period (e.g. March compared toFebruary), and
Current period compared to same period prior year (e.g. March 2012compared to March 2011).
A threshold established for reasonable changes is usually established (e.g. a change
in productions of +/- 15%), which requires additional review of measures that fail.
This check generally identifies issues related to units of measure or significant
changes in production and usage, and/or human error during data entry.
A data collection system is an effective tool for making management decisions, but
only if the data within is correct. A built-in data validation process encompassing areview for completeness, plausibility and accuracy is an important component for
effective data collection, and triggers corrections immediately upon data entry
not allowing perceived inaccuracies to skew the dataset and devalue the companys
reported social and environmental progress.
4. CAPABILITY TO ANTICIPATE & EFFECTIVELY MANAGE CHANGE
One challenge that every organization must face is change. Designing a data
collection approach that anticipates and effectively manages change can be tricky,
but investing in a flexible system is essential for a companys reporting efforts toremain viable over the long term.
Two very common changes that impact data collection processes are:
The need to establish new goals and data collection measures that supportthese targets; and
Changes to the operational boundary of an organization.
One challenge that every organization must face ischange. Designing a datacollection approach that anticipates and effectively manages change can be tricky,but investing in a flexiblesystem is essential for acompany's reporting efforts toremain viable over the longterm.
Illustrative Example:Data Review Checklist
EMISSIONSR ATIO
Complete
(all periods have an associated emissions ratio)
Plausible
(emissions ratio is between 5.0 and 10.0 grams CO 2/kilogram product)
Accurate
(emissions ratio is +/- 15% from thesame period last year)
Requires Review Please review to correct or comment.
OK to Save Please save.
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Changes to goals and targets require flexibility within any data collection system,
enabling the addition of new measures or refining how certain existing metrics or
indicators are managed. For example, when beginning data collection for the first
time, a company may have designed its collection system to collect a total
wastewater discharge volume. In the years subsequent, the company has adopteda gray water reuse system for landscaping, and would like to track how much
wastewater is diverted. The data collection system must be able to accept two
kinds of wastewater discharge values: reuse and discharge to a treatment system.
Adaptable systems, such as in this example, will allow for the creation of the two
new wastewater discharge values as well as permit a total wastewater value to
become a calculated measure.
Changes to an organizations operational boundaries are also common.
Consequently, highly effective data collection systems must be able to adapt to
changes such as acquisitions of new operations, divestitures, product changes and a
host of others factors which are relevant and important in maintaining an accurateand complete baseline, from which a company can assess and report on progress.
Since change is constant and adapting to change is a perpetual challenge, our fourth
Essential for Effective Data Collection builds a reasonable level of foresight into
company data collection processes, allowing sufficient adaptability and flexibility to
reduce resource constraints that inevitably occur when redesigning collection
systems.
5. ROUTINE AUDITING & ASSURANCE
Simply proclaiming data collection programs are complete, accurate and
representative is sometimes not enough for certain stakeholders or disclosure
advocates. These forces are asking companies to do more including
implementation of routine and independent audits to assure sustainability data is
transparent and genuinely reflective of a programs scope and progress.
In general, the most effective data auditing and assurance programs have both
internal and external components. These include defined data collection auditing
protocols, which are used to verify that the collection process is followed as
required. Regular auditing allows for early identification of uncertainty and
improvement opportunities in definitions, discrepancies in processes and training
opportunities and allows organizations to have confidence in and defensible
processes for data collection, verification and review.
Assurance is more commonly viewed as an audit by a third party, namely an
assurance provider, and based on an accepted industry protocol (e.g. ISAE 3000 and
AA1000AS) or reporting framework (e.g. GRI). There are many different protocols
and levels of assurance, but for leading companies disclosing their data publicly,
either in sustainability/integrated reporting or through other disclosure venues
The element of auditing and assurance is the vital pieceadding confidence and ensuringintegrity to any data collection
program
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(e.g. the Carbon Disclosure Project), third party assurance is often considered
mandatory by most companies to clearly demonstrate the data disclosed can be
considered accurate and unbiased.
While the first four Essentials will address effective data collection for a company,
the element of auditing and assurance is the vital piece adding confidence and
ensuring integrity to any program. Regular reviews, internally and by a third party,
will assure data collection processes have been implemented as designed,
delivering data that is accurate, unbiased and reliable to use in decision-making as
well as in assessing and reporting on the organizations sustainability progress.
CONCLUSIONS: EFFECTIVE DATA & THE VALUE OF PROGRESS
To flourish and achieve meaningful results amidst societys ever changing demands,
leading organizations adopt complete systems and processes designed to assuredata collection is as comprehensive and as reliable as possible through clearly
defined data boundaries, data collection protocols, built-in validation systems,
capability to anticipate and manage change, and utilization of routine auditing and
assurance. Companies are also most successful when they craft systems that build
in flexibility and promote a proactive approach to data gleaned during the
collection process. Managing data measures for sustainability is rife with
challenges, but implementing these 5 Essentials will ease the process, allowing
companies to accelerate toward their social and environmental goals at an efficient,
cost-effective pace.
Establishing proper data collection processes is only half the battle of course; in ourexperience, when companies initiate the process of establishing a data collection
protocol with definitions of required reporting measures, many find the usefulness
of that data lacking since it is historical, warehoused data - rather than the most up-
to-date information (which is best for management decision-making). When
companies are ready to take their data collection programs to the next step
through the implementation of these 5 Essentials , we have the expertise and
practical experience necessary to help clients use their data to manage and
enhance their programs, rather than merely collecting it to disclose.
By implementing these essentials for an effective data collection system, companies
will see the value of their social and environmental initiatives not only throughresulting better business, but also a better world.
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ABOUT THE AUTHOR
Anna Blitz ([email protected] ), a Consultant with the Global
Corporate Consultancy of Antea Group, is dedicated to partnering with
clients as they develop environmental metrics processes and integrate key
performance indicators into their overall sustainability strategy. With more
than 12 years of experience in sustainability and environmental consulting,
Anna specializes in corporate sustainability programs, including development
of environmental metrics and key performance indicators as well as preparation of
sustainability reports and public disclosure documents. Anna has a proven track record of
exceeding client expectations and is known for her efficiency, accuracy and attention to detail.
Companies like Mandarin Oriental Hotel Group and The Coca-Cola Company, along with the
Beverage Industry Environmental Roundtable (BIER) - a consortium of 16 leading beverage
companies - rely on Annas expertise. Her current work ranges from managing environmental
performance metrics databases for risks and opportunities related to environmental,
occupational safety & health, and fleet metrics, to developing internal and external companysustainability reports describing trends and progress to goals, as well as industry benchmarking
report initiatives.
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ABOUT THE GLOBAL CORPORATE CONSULTANCY
For sustainability initiatives that deliver results from strategy through implementation, leading
organizations around the world trust the expertise of AnteaGroups Global Corporate
Consultancy (GCC), a management consulting firm that specializes in sustainability. Believing
the best programs integrate sustainability into core business practices, the GCC team helpsclients identify and act on business-relevant social and environmental opportunities.
Our sustainability consultants offer a comprehensive approach that creates value at the
intersection of business, environment and society. By sharing knowledge and expertise in
sustainability initiatives, we help companies set priorities and accelerate their sustainability
efforts through relevant and practical solutions. The GCCs demonstrated methods and tools
move the sustainability business case from the anecdotal to the tangible.
We listen. We take time to understand our clients business and culture. We answer by
delivering fit-for-purpose solutions unique to each organization. Through our innovative
solutions, clients benefit from reduced business risk, new opportunities and long-term
competitive advantage.
The GCC is a business of AnteaGroup, an international engineering and environmental
consulting firm specializing in full-service solutions in the fields of environment, infrastructure,urban planning and water. By combining strategic thinking and multidisciplinary perspectives
with technical expertise and pragmatic action, we do more than effectively solve clientchallenges; we deliver sustainable results for a better future. By understanding today, we are
improving tomorrow.
With access to more than 3,000 employees in over 100 offices and experience on six
continents, we serve clients ranging from global energy companies and manufacturers tonational governments and local municipalities. Our partnership in the Inogen Environmental
Alliance provides us the diversity, strength and enhanced global capacity of 4,800 consultants
in 165 offices around the world.
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Global CorporateConsultancy
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www.anteagroup.com/gcc
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