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Why Data Quality Matters

At a restaurant, we expect our order to be right and the food to be well-prepared. When we have a package delivered, we expect it will not be broken. And, in the case of ESG data, we expect companies to accurately report how sustainable they are. Quality really does matter, especially when it’s behind major business decisions.

So how do you make sure you have quality data in the first place? And how do you prevent a returned plate, bad review, or broken trust?

What is data quality?

Let’s limit our parameters a bit. When we’re talking about “data” here, we mean ESG (environmental, social, governance) – commonly referred to as “sustainability” data – in the built environment. This is all information related to the ESG performance of a building, including quantitative aspects, like utility data, and qualitative data, like projects and certifications.

So, how accurate and trustworthy is this data? Think of data quality like sound quality; if there are too many errors in your ESG data, it appears “noisy”, just like the sound quality of bad speakers. The goal is to have “crisp” data without distractions caused by bad data. Having a systematic way of assessing ESG data accuracy will get you on a path to high-quality data.

Why does ESG data quality matter?

You’re making decisions with this data; with bad data, you make bad decisions that could potentially cost your company millions.

100% of corporations trust reported ESG data, which is very different than the 29% of investors who trust that same data. This difference between trust centers around the integrity and quality of the data. Investors are increasingly interested in ESG data as part of financial and business health metrics, so the need to have accurate, transparent data continues to grow.

What can you do to ensure you have high-quality data?

You need to establish reputable verification processes. In conjunction with Shorenstein and Urjanet, we outline 5 ways to ensure your data never gets sent back.

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If you’re already a Measurabl user, we’ve got you covered. Our anomaly detection and data quality processes have been built to report accurate and complete data.

Measurabl’s full suite of automated data validation tools assesses the entire portfolio to find intensity outliers and year-over-year variances. You’ll also receive data quality alerts on electricity, fuel, district, and water data anomalies. Learn how to utilize data quality alerts.

Are you a Pro or Premium user? Even better! For GRESB and CDP reports, you’ll receive additional data checks using our data science anomaly detection. Our Customer Success team analyzes monthly data going back to 2015 for electricity, fuel, district, water, emissions, and waste compared to average peer usage for:

  • Intensity anomalies for unusual consumption per square foot/meter
  • Cost-per-Unit anomalies that identify unusual spend-to-usage ratios
  • Month-over-Month anomalies that identify very large variances
  • Data gaps or overlaps
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