Data Observability - Data Quality Testing Solutions

  • One of the major contributors of the defects in the Data Systems or Products is bad data itself.
  • As most of the data products depend on upstream systems, the accuracy of data getting delivered from them becomes the reason for their failures.
  • Most of these failures are handled by the coders by making assumptions about the possible scenarios. Stills bugs sail through the systems.
  • The above situations become more critical for data science use cases because incoming bad data could form a skew for the algorithms and may lead to drastically bad outcomes.
  • After releasing the data code into production, in addition to the successful runs and performance, it is important to observe for data inputs and data outputs.
  • Observing data inputs through profile metrics, makes data operations more proactive in anticipating any failures due to incoming data anomalies.
  • Observing data outputs makes sure that the data delivered to reports and downstream systems does not contain any inadvertent issues.
  • Before business notifies you about the quality of deliveries it is better to measure and correct proactively.