Good data is essential for providing quality information and answering critical questions that lead to insights and better decision-making. Before starting any analysis, mapping, or visualization, we always assess the data to ensure it is fit for purpose. This includes verifying that all necessary attributes are complete, evaluating the impact of any missing values on reporting, and determining whether data cleansing processes can be applied to improve data quality.
For some projects, new data needs to be created. In these cases, we begin by designing a dataset to hold the information, carefully planning what attributes need to be captured to support future analysis and reporting. This includes defining constraints for each attribute, such as picklists of values to ensure consistent and normalized data entry. We also determine the best geometry (when spatial data is required) to represent the information, such as using points for monitoring sites or lines for streams. Careful attention to data design ensures that everything that follows is smoother and more efficient.
Catchment Management PlansLiving Water (Fonterra & Department of Conservation) & Aqualinc
Initially, existing waterway data lacked information on informal flow paths and potential waterways. To tackle this issue, we developed our own process to create a layer to represent these important features. Our approach began with a Digital Elevation Model (DEM) of the area. From this model, we identified and digitized potential flow paths and waterways. To ensure the accuracy of our findings, we conducted field visits for verification. The result is a reliable, ground-verified layer that can be utilized for Catchment Management Plan (CMP) reporting and various other applications.
Comparing Data from Multiple SourcesMinistry for the Environment & Mountains to Sea Conservation Trust
A requirement of this project is that it needs to ensure the incoming data from multiple sources is fit for purpose and can be compared where necessary. To achieve this, we have designed data quality checks to be applied across any incoming data, to identify any risks and mitigate issues. Our team leverages our expertise with FME to create workflows that interrogate each dataset line by line, checking for factors such as completeness, normalization of attributes, and spatial projection before integrating these datasets into mapping and reporting workflows. As a result, we have a high level of confidence in the quality of the data, allowing us to determine whether it meets our needs and to include quality statements where necessary.
