- What is ESG data management, excatly?
- What is ESG data governance?
- Data user vs. data owner – which one are you?
- The seven dimensions of data quality
- What is the purpose of ESG data?
- What does data quality mean for your organization?
- How to leverage good ESG data governance and strategy
- Responsibility and accountability of ESG data
- Supporting ESG data management through digitization
- How ESG data management softwares, like Novisto, can help
The purpose of corporate data is to allow both management and stakeholders to make decisions and take actions that maximize benefits to the company and to stakeholders. It is a foundational building block of managing and reporting on business-critical issues. Poor data leads to poor decisions and even poorer results – as the popular computer science saying goes: garbage in, garbage out (“GIGO”).
While the digitization of sustainability data collection and reportingーincluding ESG data managementーis still in its early days, the rise in requests for investment-grade, externally audited ESG information requires companies to adopt effective ESG data management practices.
Thankfully, these are already well documented.
What is ESG data management, exactly?
Data management is the practice of collecting, keeping, and using data securely, efficiently, and cost-effectively. Technology giant Oracle defines the goal of data management as helping “people, organizations, and connected things optimize the use of data within the bounds of policy and regulation so that they can make decisions and take actions that maximize the benefit to the organization.” Good ESG data management helps ensure that users trust and have confidence in the data to be fit for its intended purpose, without the need for manual alignment or data reconciliation.
So, what is ESG data governance?
ESG data governance is more holistic than ESG data management because it is an important business area that requires policy and oversight. According to the Data Governance Institute, “Data Governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.” ESG data governance ensures that data is well-managed, used, and disposed of. ESG data management sits under data governance and provides the “how”. Good data governance is truly the foundation of quality data.
In the world of sustainability, good ESG data management and governance empower companies to better understand their sustainability performance, manage issues efficiently, and inform stakeholders appropriately.
The Basics of ESG Data Management: Data user vs. data owner – which one are you?
A data user is any person (stakeholder), application, or process that receives and uses data. They determine the characteristics they require of this data and their expectation of quality. They need to know that the data is fit for their intended use.
A data owner is the person with overall accountability for the meaning, content, quality and distribution of a given set of data. This includes how the data is defined, manufactured, identified, maintained, delivered and consumed.
The seven dimensions of data quality
In the context of corporate sustainability data, the many data users and owners make quality more difficult to define and control. However, good ESG data is like any good business dataーthe same rules apply.
According to the Enterprise Data Management Council, data quality can be evaluated against seven key dimensions:
- Accuracy measures the precision of data. It can be measured against either original documents or authoritative sources and validated against defined business rules.
- Completeness measures the existence of required data attributes in the population of data records.
- Conformity measures how well the data aligns to internal, external, or industry-wide standards.
- Consistency provides assurance that data values, formats and definitions in one data population agree with those in another population.
- Coverage refers to the breadth, depth, and availability of data that exists, and whether it is missing from a data provider.
- Timeliness measures how well content represents current market/business conditions as well as whether the data is functionally available when needed.
- Uniqueness refers to the singularity of records and or attributes. The objective is a “single source of truth” of data.
What is the purpose of ESG data?
All of these dimensions are designed to ensure that data is fit for its intended purposeーin other words, good enough to do the job it was designed to do.
Whether or not your business data is ESG-related, its purpose is multifold:
- Measure performance on issues that matter to the business.
- Analyze performanceーover time, against targets, and against peersーto inform the management of these issues.
- Allow stakeholders to make the best-informed decisions (e.g. whether or not to buy a company’s products or to become an employee).
- Enable investors and other capital providers to decide on the most efficient flow of capital towards their intended or desired use, at the appropriate price.
Today, the relatively poor quality and reliability of ESG data remains a significant challenge to using it effectively. However, the digitization of data collection, when paired with good ESG data management practices, will enable data quality and make its seamless for any intended purpose.
What does data quality mean for your organization?
Not all data requires the same level of quality. We can think of the level of data quality in terms of the number of dimensions for which a given data point must be of “good” quality – a data point may be of “good” quality in one dimension but not another.
For example, if you put a phone number in the field for a postal code, the data will be at 100% quality for the completeness dimension (meaning the field is filled) but at 0% for the accuracy dimension.
In other words, data quality is context-specific (think: what is the data’s intended purpose?). This context determines which dimensions are relevant to use in measuring and determining data quality. For ESG data, the quality needed differs based on the type of dataーfor example, data for disclosure to investors may not require the same level of granularity (or quality) as data used internally in an exploratory context.
Data quality should also be measured and managed throughout its entire lifecycle, because data flows and interactions with other data may alter the level of quality. For example, if numbers in a data set are truncated to integers following a transfer to a data warehouse (rather than to two decimal places, as they were originally), their quality may be impaired. You need to implement controls to monitor ESG data quality throughout its useful life, and assign people to remediate quality issues as they arise.
How to leverage good ESG data governance and strategy
Like anything else governance-related, data governance should be embedded in an organization’s governance structure and aligned with the current way of overseeing the rest of the company. The best way to leverage good data governance is by making sure it contributes to the strategy and the objectives of your organization. Ensuring proper data quality and definition serves to improve the reliability and credibility of reporting; it also serves to improve management functions.
As companies strive to become data-centric, data governance can become a key component of larger initiatives such as digital transformation or change management. It’s part of a continuous improvement process similar to that of embarking on a sustainability journeyー one that requires shifting the cultural mindset and laying a strong foundation to handle and leverage information.
And it’s no easy task; a survey of hundreds of decision-makers conducted by Wharton Business School found that only 24% of senior decision-makers passed the test for data literacy. Making your company more data literate requires training employees, developing capabilities, and creating a culture where decisions are made based on data and not purely on gut feelings.
Responsibility and accountability of ESG data
An important part of data governance is the definition, documentation, and communication of roles and responsibilities, as well as the accountability over data. Since data has become ubiquitous, everyone in an organization works to capture, access, manage, or transform data at some point. Therefore, everyone has some degree of responsibility over the data life cycle and quality.
This is particularly true with sustainability-related issues, which are inherently multidimensional. This means that ESG data lives in many different business functions within a companyーand nearly everyone may impact ESG data in some way.
If everyone is responsible for data quality, who is actually accountable?
Legislation around the world, including privacy regulations, increasingly requires someone to officially take on a role with direct accountability for data in an organization. For example, GDPR requires a company to have a Chief Privacy Officer, while the province of Québec mandates a corporate Chef de l’information (Chief Information Officer).
Corporate boards are explicitly adding data governance to committee charters—or even creating whole data governance committees—and the topic is regularly added to the directors’ agenda.
Some companies have additional data governance specialists on staff to support the person acting as a Chief Data or Chief Privacy Officer. Since reporting on ESG and sustainability is a relatively new practice, these positions are especially critical for overseeing ESG data quality alongside other data collection and reporting at an organization.
Supporting ESG data management through digitization
The digitization of data collection into a single, centralized system of records and the automation of data processing and reporting will go a long way to improving the quality of ESG data throughout the data lifecycle. Digitization will help make data easier to use for its intended purpose and create a virtuous circle of “quality in, quality out” (QIQO).
ESG Data Management life cycle: Collection and capture
Today, corporate data is still often collected manually— especially non-financial, sustainability-related data. Manual processing of data is cumbersome and generally difficult to control or monitor. Further, for large volumes of data, success is often measured by the performance of data entry clerks (evaluated based on the quantity or volume they can achieve) rather than on the quality of the data.
Automating data ingestion through digital tools not only speeds up the process, it also allows for validation controls to ensure consistent and high quality ESG data along the various dimensions mentioned above.
ESG Data Management life cycle: Access and storage
Proper access and storage of data protects confidentiality and prevents unwanted modifications to data, which both contributes to data integrity and security and ensures the availability of data. Access controls include logs that keep track of “who accessed what” and “who changed what”. Integrity controls protect data against unwanted alterations or keep track of previous versions.
Availability is the capacity to make the data available in a timely fashion to those with appropriate access levels. Compared to physical repositories, digital repositories make retrieving ESG data easier, faster and more secure.
ESG Data Management life cycle: Sharing
Sharing controls and transfer agreements serve to define data ownership and the means by which data is transferred or passed to protect its confidentiality and integrity (i.e. that no data is lost or modified along the way). This includes any metadata (which is simply data that describes other data, such as source or fiscal period).
When ESG data is digitized and consolidated in an organized system of records, it becomes possible to quickly give a user access to what they need rather than send multiple copies out to different people.
ESG Data Management life cycle: Retention vs. Disposal
The bigger the dataset, the more difficult it is to store. Whether or not to keep records (and if so, for how long) needs to be clearly defined. While some data assets should probably be kept forever, others should not. This is one (albeit rare) case where technology could make things “worse”, as the decreasing costs of data storage and increasing capacity of data analytics result in companies hanging onto their data indefinitely.
This is to be avoided, as it increases the noise ratio, gives rise to duplicates and reduces the certainty of a single source of data truth. In some instances, it may even be prohibited by law. For example, companies cannot retain personal data beyond a certain amount of time, else risk considerable fines for doing so.
How ESG data management softwares, like Novisto, can help
Many tools exist to try and help companies effectively collect, manage, and use ESG data.
Novisto helps companies create a “single source of truth” for ESG dataー making it quick and easy to collect, share, and use. Our platform ensures that corporate ESG data, workflows, and reporting are trusted (read: auditable, like financial data), efficient (automated and streamlined), and insightful (contextualized, with clear guidance for decision-making).
Closing the loop on ESG data management, quality, and governance
Ultimately, the benefits of digitizing the data life cycle processes for your organizationーgreater data quality, certainty, efficiency and integrityーare critical to a successful data governance strategy. With quality, centralized ESG data, and good ESG data management practices, your company will be able to better understand, share, and act on its sustainability performance.
Some of the contents of this blog are based on a previous Novisto article co-authored by Catherine Nadeau, Senior Manager of Information and Data Governance at KPMG in Canada.