Tutorial: Data Validation and QA with FME

Liz Sanderson
Liz Sanderson
  • Updated

FME Version

  • FME 2021.1


While moving, integrating, and transforming data, it's also important to ensure that the data quality is of a suitable standard.

FME has a number of tools and transformers that can be used for data validation. Some - such as the GeometryValidator and AttributeValidator transformers - are specifically designed for data validation. Others - as this tutorial will show - are not specific to data validation, but can be used for that purpose.

When assessing data quality there are three techniques that can apply:

  • Identifying Problems: Identifying features that do not conform to the required standard
  • Counting Problems: Assessing overall data quality by quantifying the number of substandard features
  • Fixing Problems: Improving data quality by fixing the issues found in substandard features

Each of the examples in this tutorial will include information on how to identify, quantify, and fix the problem that is being discussed.


Geometric Inconsistencies

These are geometries that are bad in themselves, rather than being part of a substandard network or coverage. This includes:



These are features that have some form of duplication. This includes:


Small Features

Often very small features are indicative of poor geometry and/or topology. This includes:


Area Topology

These are issues related to a continuous polygon coverage. This includes:


Linear Topology

These are issues relating to a linear network. This includes:


Spatial Concepts

These are geographic features containing some form of logical issue, for example a road represented by a polygon or a bicycle path that runs through a lake. This includes:

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