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Data Governance – September 2023

The Case for Environmental Data Governance

Environmental problems consist of complex systems and, by necessity, require diverse methods for to solve them. These problems call for decision-support tools and models that are built around information management practices that follow agreed upon principals and methods on how best to use the tools selected. This complexity of environmental problems manifests across the requirement to monitor different environmental matrices and chemical constituents, but also to adapt those data-collection efforts to the context of existing environmental legal structures, regional politics and policies, and industry economics. The data that are collected by or for a business entity requires governance of those collected data.

Data governance encompasses all the activities undertaken by a company to ensure the security, accuracy, availability, and usability of data throughout its lifecycle. It involves establishing internal standards that govern data collection, storage, processing, and disposal. Data governance also entails compliance with external standards set by industry associations, government agencies, and other stakeholders. These standards, coupled with defined roles and metrics, ensure the effective and efficient use of information.

Benefits of Data Governance

  • Decision Making: Facilitates better and more-timely decision making.
  • Cost Reduction: Eliminates decisions based on outdated information, resulting in efficient day-to-day operations and reduced resource waste.
  • Compliance: Enhances regulatory compliance.
  • Trust: Earns greater trust from customers and suppliers.
  • Risk Management: Enables better risk management.
  • Data Access: Allows more personnel access to valuable data.
  • Data quality: Ensures data are suitable for use, addressing dimensions like accuracy, completeness, consistency, timeliness, validity, and uniqueness. Data governance also includes processes such as profiling and monitoring, cleansing and remediation, and data lineage and traceability.
  • Single Source of Truth: All decision makers work from a unified view and terminology.
  • Metrics: Track and reports progress and success of the data governance program, strengthening support from business leadership.

Data Management is not Data Governance

It is essential to differentiate between data management and data governance. While data management encompasses all aspects of managing data as an enterprise asset, including collection, storage, usage, and oversight, data governance focuses on policies and procedures governing data use and storage. Data management is the technical implementation of those procedures. Data governance is overarching, extending beyond individual projects, while data management plans should align with and follow overarching data governance policies.

However, challenges persist in implementing effective data governance:

  • Company-wide Acceptance: Clear leadership and cross-functional collaboration are required due to data spanning across multiple departments.
  • Poor data management: Insufficient data governance leads to unsecured, siloed data and undisciplined processes.
  • Standardization: Balancing governance standards with flexibility is crucial.
  • Aligning stakeholders: Transparency is vital in persuading stakeholders to invest in data governance.
  • Assignment of responsibilities: Defining who can access specific data segments and when can be challenging, but ensures reduced risk.
  • Compliance/Enforcement: Establishing a program that ensures compliance with data governance policies and standards, with requirements and policies defined at an organizational level and enforced by responsible entities.

Making the Case

If you see a need to make a case for stronger data management and data governance practices, Environmental Standards can help. We assist by leveraging our history of implementing best practices, processes, and systems, helping you and your organization achieve enhanced decision making, ensuring regulatory compliance, and building trust in the data, establishing with best practices in achieving a viable data governance solution for your environmental data. Data governance is not the primary job of the majority of your organization. Environmental Standards will help minimize the impact to individual contributors and Project Teams at your organization and will make the steps and best practices easy to follow.

Matt Miller Envstd

Matthew T. Miller

Project Data Manager