Written by: Travis Edwards
“Would you like to sync with Facebook?” “Do you want to share your location?” “You are over your data usage for the month.” None of these phrases come as any surprise to us anymore. With the rapid immersion of technology into society, we have all become familiar with the everyday applications and impacts data has in our lives. Today, many companies obtain, integrate, and interact with some form of data to successfully perform their business functions, including Waypoint with its Utility Connect program benchmarking and energy efficiency opportunity analyses. Because data plays such an imperative role, ensuring its quality has become a vital activity in the business world.
To put matters into perspective, the Data Warehousing Institute estimates that American businesses lose 600 billion dollars annually due to the cost of poor data quality. Additional estimates find 15-20% of data in a typical organization erroneous or otherwise unusable. Staggering figures like these have given new meaning to the often grouped data quality management buzzwords, Quality Assurance (QA) and Quality Control (QC).
While both processes involve improving the quality of data, the purpose of each is quite different. The QA process entails filtering data for any inconsistencies and other anomalies in the data, as well as performing data cleansing activities (e.g. removing outliers, missing data interpolation). The QC process entails controlling the usage of data with known quality measurements for an application or a process. The QC process is usually performed after a QA process is complete so that factors such as data inconsistency, incompleteness, accuracy, and precision can be evaluated in the process.
One of the key procedures in Waypoint’s Utility Connect program involves benchmarking and trending multi-tenant commercial building utility data. While the “QA/QC” process is often associated with the negative connotation of being tedious or repetitive, Waypoint associates the QA/QC process with much more positive terms such as robust, crucial and impactful. Conducting a robust QA/QC process provides Waypoint with a substantial benefit in its ability to accurately trend energy usage, identify the best buildings for energy efficiency opportunities, and ultimately guarantee customer satisfaction.
Waypoint’s background and expertise in the utility and commercial real estate industries has led to a robust internal QA/QC process. When conducting the QA process with utility data sets, Waypoint automatically searches for any missing data, duplicate data, or erroneous data, while also leveraging its background and expertise in the market to identify any anomalies or abnormalities within the datasets. For a given dataset, here are a few examples of such anomalies or abnormalities:
Cost of energy is significantly higher or lower than average rates for a given year
Electric and gas consumption values result in an unusual energy usage intensity (EUI) for a given building’s square footage
Electric and gas usage swings unfeasible amounts month to month or year to year
Conducting a vigorous QA process allows Waypoint to confidently administer a QC process on the dataset. If a given dataset has too many missing data points or data anomalies, Waypoint’s QC process may determine that the dataset does not meet the quality threshold to accurately profile a building. This may result in looping back with the customer to either verify the values and accuracy of the given datasets or to request a new, more accurate dataset altogether.
Overall, although sometimes overlooked or undermined, the QA/QC process is an incredibly valuable aspect of any data analysis. It mitigates the errors and mistakes that can be detrimental in the development of trust and confidence from Waypoint’s utility clients and commercial real estate partners. Waypoint continues to implement and improve upon its QA/QC process to further establish its credibility and impact on the utility and commercial real estate energy efficiency markets.