Sustainability Measures: Five Essentials for Effective Data Collection

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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Sustainability Measures: Five Essentials for Effective Data Collection

1 www.anteagroup.com/gcc

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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