With so much attention on security and big-data capacity, the importance of data quality can be easily forgotten. Devices within smart buildings—especially interval-metered equipment and resources—can generate massive data streams. The quality, persistence, and auditability of that data is critical to its effective use within analytics and business intelligence applications.
Accuracy and raw data
You need to know that your data is accurate. CANDI is designed and constantly tested for end-to-end accuracy and quality of raw data from each device endpoint. CANDI gathers and time-stamps raw data from building devices at the edge to support accurate analysis upstream. Our QA department regularly audits raw data streams from end-to-end as part of our reliability testing process, to ensure complete accuracy.
CANDI’s cloud service partners often prefer raw data to be “normalized” before delivery to them. For example, CANDI can translate data to different formats (such as Microsoft Azure IoT Hub), or average raw data across 5 or 15 minute intervals for more useful displays and reports.
CANDI leaves raw data intact, and also offers normalized data (plus metadata for context) to meet our partners’ requirements. This supports service partners’ needs without overwhelming their analysis engines with irrelevant volumes of raw data.
By focusing first on preserving end-to-end integrity of raw data, we can ensure the highest level of data quality in all scenarios.