(raw) bytes to information analyses at tactical level and, finally, strategic interpretation of the analytical results that provides knowledge and wisdom to management
groups. It follows, as a result, that the water companies have acknowledged that
good data provides a basis for good decision-making.
3 As such, the policies to
control data quality serve a fundamental function to the transformation from data to
wisdom [21] for water utilities, along with the broader frameworks that extend data
applications for decision-making provided by the concept of hydroinformatics [14].
As in other product, process and service cycles in organizations, ensuring data of
good quality requires an encompassing framework of continuous quality improvement, which can be defined as a framework for Data Quality Control (DQC). To
design such a framework, classic quality improvement methodologies can be
employed, such as the Plan-Do-Check-Act (PDCA) approach (Fig. 2) [22, 24, 25],
which can be used to describe the continuous improvement of measurement systems
Fig. 2 The PDCA approach for data quality improvement, data to information at water utilities.
Adapted from [22, 23]
3 Minutes, Hydroinformatics platform 12 October 2017
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