The consequences of relying on inaccurate data can be far-reaching, impacting everything from operational efficiency to financial viability.
Of all existing measurement points from which we have collected energy data, more than ´60% have provided data with errors and deficiencies (plus/minus 20% depending on how you define errors). Regardless of the error margin you set as a criterion, the proportion is so high that it renders all energy data unreliable.
We have been in contact with others, including researchers in SINTEF's SmartBuildingHub project, who are familier with the challenge associated with poor data quality.
Even though those of us who work with data management on a daily basis are aware of this, there are many others who trust their data - and who use this as a basis for operational automation, cost allocation, reporting and more.
What cause the errors?
The root cause of this issue often lies in human errors during the installation process. Submeters have often been installed without too much attention to detail; sensors are misconfigured, and connections are flawed. As a result, inaccurate data infiltrates your systems, leading to skewed insights.
The meters themselves can also be a source of misinformation. The quality of submeters varies and most of them are not maintained as they should. Hardware malfunctions create discrepancies that may go unnoticed for long periods.
Read more about the causes here
Can you trust your data?
How can you ensure the accuracy of your data? It starts with rigorous verification of installations and continuous monitoring of data streams. By scrutinizing installations and monitoring data flows, you can identify and address discrepancies before they affect your operations.
To all commercial building owners and software companies: Ignorance is not bliss when it comes to data quality. Take control of your data, question its integrity, and embrace measures to ensure its accuracy. Only then can you unlock the full potential of your data.
