How to Manage Sustainability Data

VERIFY, VALIDATE AND LIFT QUALITY CONTROLS STEP 03: GOVERNANCE

Having strong data validation and governance controls is essential to good management. After all, even automated data can be incorrect, while other information can arrive in varying forms and languages from various sites, suppliers, and platforms. How can you – let alone an auditor – be reassured the data is accurate? It takes a high level of due diligence and care. Many challenges are addressed by sound data governance, and there are good reasons why robust checks and balances are important. For example, it’s not uncommon for gaps in data management, human error, or the use of spreadsheets over more reliable platforms to call reporting into question. When it happens, it takes time and effort from senior management to rectify. While not exhaustive, some of the measures below may help prevent that from occurring. When it comes to trend analysis, good governance will also ensure the veracity of insights.

• Errors or gaps in data feeds from meters and other sources • Details lost when information is translated from different languages • Estimated bills replaced by actual bills, leading to missing values • Incorrect data transferred between platforms or miscalculated estimates • Human error or lack of expertise in entering, validating, or reviewing data

• Regularly refresh organizational boundaries • Establish quality assurance processes, including specific actions if data is missing or late • Use processes to sample and test the integrity of data coming in • Assign a team to review the data and cross check against master lists • Regularly conduct peer reviews of the data • Verify data inputs through integrations with chosen platforms

COMMON

DATA QUALITY ISSUES

ACTIONS TO ENSURE HIGHER DATA QUALITY

RETURN