  There has been a massive explosion of data generated by customers and
retained by companies in the last decade. However, there is a significant
mismatch between the increasing volume of data and the lack of automation
methods and tools. The lack of best practices in data science programming may
lead to software quality degradation, release schedule slippage, and budget
overruns. To mitigate these concerns, we would like to bring software
engineering best practices into data science. Specifically, we focus on
automated data validation in the data preparation phase of the software
development life cycle.
  This paper studies a real-world industrial case and applies software
engineering best practices to develop an automated test harness called RESTORE.
We release RESTORE as an open-source R package. Our experience report, done on
the geodemographic data, shows that RESTORE enables efficient and effective
detection of errors injected during the data preparation phase. RESTORE also
significantly reduced the cost of testing. We hope that the community benefits
from the open-source project and the practical advice based on our experience.
