What Does Data Quality Mean in Sustainable Business Practices

As the world shifts towards a more sustainable future, businesses of all sizes are increasingly placing emphasis on developing and implementing sustainable practices. Central to these initiatives is the role of data and the use of effective measurement tools. As the classic saying goes “You can’t manage what you can’t measure.” Quality data forms the bedrock of sustainable practices, influencing decision-making, driving meaningful action, and underpinning robust and credible reporting.

Data quality is a multi-dimensional concept that consists of several interrelated aspects, each contributing to the overall robustness, reliability, and utility of the data. Within the realm of sustainable practices, five key characteristics play a critical role in defining data quality: accuracy, coverage, completeness, access, and governance.

Data Accuracy: Truth in Sustainability Metrics

In the context of sustainability data, accuracy pertains to the degree to which reported metrics correspond to their real-world values. Recent regulatory shifts and escalating stakeholder expectations have placed data accuracy firmly in the spotlight.

For instance, a recent Deloitte survey revealed that executives regard ensuring data quality as the top data-related challenge they’re facing. With the Securities and Exchange Commission (SEC) expected to finalize its climate disclosure rule in the near future, many enterprises are finding their existing data infrastructure ill-equipped to meet the forthcoming demands, particularly concerning data accuracy. 

Another Deloitte survey found that merely 3% of the 150 consumer companies surveyed asserted that their sustainability data was as accurate and verifiable as their financial data. This clearly indicates that there is significant scope for improving data accuracy in sustainability reporting, even as companies grapple with the logistical and technical challenges of doing so.

Automation can be a valuable tool for optimizing data access, where companies can minimize human error, enhance scalability, and conduct systematic audits that identify anomalies. This ensures that the data remains as accurate as possible, bolstering the credibility of sustainability reporting.

However, the pursuit of data accuracy isn’t without its trade-offs. Striving for 100% accuracy can incur significant costs and lead to delays, potentially diverting resources away from other impactful sustainability initiatives. A myopic focus on data accuracy can lead to a phenomenon we refer to as “greenwishing,” where a company’s obsession with accuracy in reporting overshadows the need for concrete action.

Data Coverage: Expanding the Horizon of Sustainability Data

Data coverage represents the extent of a portfolio captured by the data collected. As sustainability reporting frameworks like the Global Real Estate Sustainability Benchmark (GRESB) emphasize, coverage is a vital aspect of sustainability data. 

Data coverage, as the GRESB framework illustrates, has a significant impact on your sustainability score. In the GRESB scoring, data coverage contributes substantially more than data performance. Simply put, enhancing data coverage can provide immediate improvements in sustainability ratings and, by extension, stakeholder perceptions of a company’s sustainability efforts.

While the pursuit of data accuracy is essential, there is a point of diminishing returns. Incremental improvements in accuracy often require exponentially increasing investments of time, resources, and effort. By contrast, improving data coverage can yield substantial returns in terms of sustainability scores and operational insights.

Data Completeness: A Comprehensive Picture

Often confused with data coverage, data completeness is another crucial aspect of data quality. It refers to the temporal coverage of data and the breadth of information captured. In practical terms, this means having a complete suite of utility consumption, demand, and cost data that covers all relevant time periods. High-quality, real-time environmental data is paramount in this shift. Tracking facility-level energy consumption and greenhouse gas emissions over time provides granular insights, enabling firms to set realistic, science-based targets and reducing the risk of greenwashing. 

Access to complete data provides several operational advantages. It helps identify peak usage periods, target efficiency measures, audit sales taxes, and even inform procurement decisions. The richness of complete data significantly enhances its utility, allowing sustainability data to drive operational improvements and strategic decisions.

Data Access: Making Data Available to Decision-Makers

Data access involves putting data into the hands of the teams who can use it to drive action. While the granular data may seem irrelevant to your sustainability reporting analyst, your procurement, engineering, or property management team can use this information to drive real action. This is the crux of achieving progress in your sustainability goals – breaking down data silos and enabling data-driven decision-making across your organization.

Data Governance: Securing and Controlling Your Sustainability Data

Data governance wraps up all the data concepts into one, adding layers of security and control. As data privacy and security concerns rise, more questions arise from client IT teams regarding security. Data governance protocols should include procedures to ensure data integrity, privacy, and compliance with applicable regulations. Providing visibility into the data collection process and securing independent third-party assurance can further enhance credibility and trustworthiness. Reporting trends also indicate that external assurance may become required in certain regulatory mandates so it is a good idea to think ahead with verification of data today. 

In conclusion, data quality is not just about accuracy but is a comprehensive concept encompassing accuracy, coverage, completeness, access, and governance. Focusing on these elements will provide a sound foundation for effective sustainability management and reporting, enabling businesses to achieve their sustainability goals effectively and credibly. By ensuring data quality, businesses can turn their sustainability commitments into tangible actions and results.