Best practices for IoT data validation

The importance of data validation cannot be understated. In the McKinsey report “The Internet-of-Things catching up to an accelerating opportunity”, Nov 2021, it is stated: ”We estimate the total IoT value captured by the end of 2020 to be $1.6 trillion.” This value creation is contingent upon the validity and quality of the IoT data that underpins the data driven decisions.

In this white paper, we will present best practice IoT data validation including processes and tools. The body of knowledge presented here is collected and/or developed by FORCE Technology in collaboration with the Alexandra Institute, under the Nordic IoT Centre. It aims at giving guidance and inspiration to those who want to improve the data quality of IoT systems or discover how they can determine the quality of their IoT data.

Data validation refers to the process of ensuring the proper level of accuracy and quality of data making the data fit-for-purpose. The described data validation process is primarily focused on validation of data from IoT systems but will naturally be applicable to other types of data as well. This is because IoT systems rarely deal with only “IoT data”, which by most is seen as time-series measurements. Data validation is an iterative process that aims at ensuring a suitable level of data quality for a system or an application. Defining the data quality requirements and the prioritisation of activities in the iterative data validation process, are key to substantially improve the validity and quality of the data with minimum effort.