  The rise of advanced data technologies in electric power distribution systems
enables operators to optimize operations but raises concerns about data
security and consumer privacy. Resulting data protection mechanisms that alter
or obfuscate datasets may invalidate the efficacy of data-driven
decision-support tools and impact the value of these datasets to the
decision-maker. This paper derives tools for distribution system operators to
enrich data-driven operative decisions with information on data quality and,
simultaneously, assess data usefulness in the context of this decision. To this
end, we derive an AC optimal power flow model for radial distribution systems
with data-informed stochastic parameters that internalize a data quality
metric. We derive a tractable reformulation and discuss the marginal
sensitivity of the optimal solution as a proxy for data value. Our model can
capture clustered data provision, e.g., from resource aggregators, and
internalize individual data quality information from each data provider. We use
the IEEE 33-bus test system, examining scenarios with varying photovoltaic
penetration, to demonstrate the application of our approach and discuss the
relationship between data quality and its value.
