  Background: Data errors are a common challenge in machine learning (ML)
projects and generally cause significant performance degradation in ML-enabled
software systems. To ensure early detection of erroneous data and avoid
training ML models using bad data, research and industrial practice suggest
incorporating a data validation process and tool in ML system development
process.
  Aim: The study investigates the adoption of a data validation process and
tool in industrial ML projects. The data validation process demands significant
engineering resources for tool development and maintenance. Thus, it is
important to identify the best practices for their adoption especially by
development teams that are in the early phases of deploying ML-enabled software
systems.
  Method: Action research was conducted at a large-software intensive
organization in telecommunications, specifically within the analytics R\&D
organization for an ML use case of classifying faults from returned hardware
telecommunication devices.
  Results: Based on the evaluation results and learning from our action
research, we identified three best practices, three benefits, and two barriers
to adopting the data validation process and tool in ML projects. We also
propose a data validation framework (DVF) for systematizing the adoption of a
data validation process.
  Conclusions: The results show that adopting a data validation process and
tool in ML projects is an effective approach of testing ML-enabled software
systems. It requires having an overview of the level of data (feature, dataset,
cross-dataset, data stream) at which certain data quality tests can be applied.
